DEV Community: yureki_lab

How I Made 140 Background Jobs Idempotent With Claude Code: 5 Lessons

yureki_lab — Thu, 15 Oct 2026 14:31:31 +0000

TL;DR

A retried background job charged 37 customers twice, and that one incident sent me on a two-week audit of all 140 jobs in our Python/Celery stack. I used Claude Code to classify every job's side effects, add idempotency keys, and build a "run it twice" test harness. The AI was excellent at the boring 80% and surprisingly bad at one specific thing, which I'll get to.

The Problem

Here's the incident in one paragraph.

A worker picked up a charge_subscription_renewal job, called the payment provider, got a successful charge back... and then the pod got OOM-killed before it acknowledged the message. The broker did exactly what it's supposed to do with an unacknowledged message: it redelivered it. A second worker ran the job from scratch and charged the card again. 37 customers, one bad Tuesday morning, a lot of refund emails.

The root cause wasn't the OOM. It was that we had been quietly assuming exactly-once delivery in a system that only ever promised at-least-once. Every queue I've worked with (Celery on RabbitMQ, SQS, Sidekiq, Cloud Tasks) has the same fine print: your job may run more than once. Retries, visibility timeouts, worker crashes, deploys that kill in-flight tasks. Any of them can do it.

So the real question became: which of our 140 jobs are safe to run twice, and which ones are time bombs?

Nobody on the team knew. The jobs had been written over six years by maybe 20 different people. Some were obviously harmless (rebuild a search index), some were obviously dangerous (charge a card), and about 90 were somewhere in the murky middle.

Stack, for context:

Python 3.12, Django 5.1
Celery 5.4 on RabbitMQ, acks_late=True on most tasks
PostgreSQL 16
Claude Code (CLI) as the coding agent, October 2026 build

How I Solved It

I broke the work into four phases. The key decision was to make the AI produce a structured inventory first, before touching any code.

Phase 1: Inventory every side effect

I asked Claude Code to walk every task and fill out a fixed schema. Not a summary, a schema. Free-form summaries are where hallucinations hide; a rigid table forces the model to commit to something you can check.

# one entry per task, generated by the agent, reviewed by me
- task: billing.tasks.charge_subscription_renewal
  side_effects:
    - kind: external_api
      target: payment_provider.charge
      reversible: false
    - kind: db_write
      target: Invoice
      operation: insert
  natural_idempotency: none
  retry_policy: autoretry_for=(Exception,), max_retries=5
  risk: critical
  evidence: "billing/tasks.py L88-L141"

The evidence field turned out to be the most important line. Every claim had to point at a file and a line range, so I could verify it in seconds instead of re-reading the whole task.

The prompt was roughly:

For each Celery task in the repo, produce one YAML entry with this exact schema.
Follow calls at least 3 levels deep into helpers.
If you are not sure whether a call has a side effect, mark it `unknown`,
never guess `none`. Cite file + line range for every side effect.

That "mark it unknown, never guess none" line did a lot of work. It flipped the model's default from optimistic to paranoid, which is exactly what you want in a safety audit.

The result after one long session:

Risk level	Count	Example
Critical (money / external, irreversible)	9	charge card, send payout
High (user-visible duplicates)	23	send email, push notification
Medium (internal duplicates, fixable)	38	insert audit row, increment counter
Safe (naturally idempotent)	61	rebuild cache, recompute stats
Unknown	9	deep helper chains

I manually verified all 9 critical, all 9 unknown, and a random sample of 20 from the rest. The agent's classification held up in 17 of the 20 sampled. The 3 misses were all "medium" jobs that were actually "high", and I'll come back to why.

Phase 2: One idempotency primitive, not 140 bespoke fixes

The tempting move is to let the agent patch each task individually. Don't. You end up with 140 slightly different home-grown dedup schemes.

Instead I wrote a single primitive by hand (this is the part I didn't want an AI to improvise) and had the agent apply it everywhere.

# Idempotency guard: claim a key in Postgres before doing side effects.
from django.db import IntegrityError, transaction

class AlreadyProcessed(Exception):
    pass

def claim_once(key: str, task_name: str) -> None:
    try:
        with transaction.atomic():
            IdempotencyRecord.objects.create(key=key, task_name=task_name)
    except IntegrityError:
        raise AlreadyProcessed(key)

class IdempotencyRecord(models.Model):
    key = models.CharField(max_length=200, unique=True)
    task_name = models.CharField(max_length=200)
    created_at = models.DateTimeField(auto_now_add=True)

The unique constraint does the real work. Two workers racing on the same key can't both win, because Postgres won't let them.

For the payment job specifically, a local guard isn't enough, because the crash can happen after the charge but before our DB commit. So we also pass a deterministic idempotency key to the payment provider itself:

def charge_subscription_renewal(subscription_id: int, period: str):
    key = f"renewal:{subscription_id}:{period}"
    provider.charge(
        amount=sub.price,
        customer=sub.customer_id,
        idempotency_key=key,  # provider dedups server-side
    )

💡 The key must be derived from the business event, not generated at runtime. uuid4() inside the task is useless, since every retry gets a fresh UUID. renewal:{subscription_id}:{period} is the same no matter how many times the job runs.

Phase 3: Let the agent roll it out, in batches

With the primitive in place, the agent's job was mechanical: for each non-safe task, pick the business-event key, wrap the side effects, and handle AlreadyProcessed as a success (not a failure, or the retry logic will keep hammering it).

I ran it in batches of about 10 tasks, one PR per batch, risk-ordered: critical first, then high, then medium.

flowchart LR
    A[Task received] --> B{claim_once key}
    B -- new key --> C[Run side effects]
    B -- IntegrityError --> D[Log + ack as success]
    C --> E[Commit + ack]
    C -- crash --> F[Broker redelivers]
    F --> A

Phase 4: The "run it twice" harness

This is the part I'd steal if I were you. I had Claude Code generate a pytest fixture that runs every task twice in a row with identical arguments and asserts that the observable state after run 2 equals the state after run 1.

import pytest

@pytest.mark.parametrize("task, args", TASK_CASES)
def test_task_is_idempotent(task, args, snapshot_state):
    task.apply(args=args).get()
    after_first = snapshot_state()

    task.apply(args=args).get()
    after_second = snapshot_state()

    assert after_second == after_first, f"{task.name} is not idempotent"

snapshot_state() captures row counts of the relevant tables plus every call recorded by our mocked email, push, and payment clients. If run 2 sends one more email or inserts one more row, the test fails.

On the first run of this harness, 14 tasks failed that the inventory had marked as fixed or safe. That's the number that made me glad I built it.

Lessons Learned

1. Structured output beats "please audit this"

Asking an agent to "check these jobs for idempotency issues" gets you a confident essay. Asking it to fill a schema with evidence line ranges gets you something you can actually verify. The schema is your review interface. ✅

2. The AI is great at following calls, bad at implicit side effects

Here's the specific weakness I promised. All 3 misclassifications in my sample, and most of the 14 harness failures, came from side effects that weren't visible as a function call:

Django post_save signals that sent a webhook
A model's save() override that bumped a counter
A database trigger (!) that inserted an audit row

The agent followed explicit calls three levels deep without breaking a sweat. But "this .save() also fires a signal registered in a different app" is spooky action at a distance, and it missed it repeatedly. ⚠️ If your codebase leans on signals or triggers, tell the agent explicitly to enumerate them first.

3. Write the safety primitive yourself

I let the agent write roughly 90% of the diff, but claim_once and the model behind it were mine. The core of a correctness guarantee should be small, boring, and authored by someone who will be paged when it breaks. Let the AI do the rollout, not the invention.

4. "Already processed" is a success, not an error

Our first batch treated AlreadyProcessed as an exception, so Celery retried it five times and then dumped it in the dead-letter queue. That generated a flood of false alerts. Catch it, log it at INFO, and ack the message.

5. Tests that run things twice are cheap insurance

The harness took the agent about 20 minutes to scaffold and me about two hours to tune the state snapshot. It now runs in CI on every PR that touches a task. New jobs get caught before they ship, which is the only way this audit doesn't rot in six months. 🚀

What's Next

Cleanup policy for IdempotencyRecord. It grows forever right now. I'm adding a TTL sweep keyed to each task's maximum retry window.
Signal inventory. Since implicit side effects were the agent's blind spot, I'm having it build a static map of every signal and trigger, and feeding that map into future audits as context.
Chaos test in staging. Randomly kill workers mid-task for a day and confirm nothing double-fires. Trust, but verify.

Wrap-up

If you run background jobs, assume they will run twice, because eventually they will. An AI coding agent can make the audit dramatically faster, but only if you give it a rigid schema, write the core guard yourself, and back everything with a test that literally runs each job twice.

👉 If this was useful, follow me here on Dev.to for more build logs on shipping real systems with Claude Code. And I'm curious: what's the worst duplicate-job bug you've seen? Drop it in the comments. I'll read every one.

How I Split a 3,200-Line God Class Into 14 Modules With Claude Code

yureki_lab — Wed, 14 Oct 2026 14:31:22 +0000

TL;DR

I had a 3,200-line Python class called OrderService that everyone was afraid to touch. Over five weeks, I used Claude Code to break it into 14 focused modules without a single production incident. The trick wasn't the AI writing the code — it was characterization tests first, a seam map I drew myself, and one extraction per pull request.

The Problem

Every codebase that lives long enough grows one. Ours was OrderService: a single class that handled pricing, discounts, tax, inventory reservation, payment capture, refunds, email notifications, audit logging, and — for reasons nobody remembered — CSV exports for the finance team.

The numbers, when I finally measured them:

3,212 lines in one file
87 methods, 31 of them private helpers called from 5+ places
19 instance attributes, many mutated by multiple methods
Cyclomatic complexity of 41 on checkout() alone
Touched by 60% of all pull requests over the previous six months

That last number was the real cost. Every feature landed in this file, so every PR had merge conflicts, and every review needed someone who "knew the order code." That someone was usually me.

I'd tried splitting it by hand twice before. Both times I got about two days in, discovered some method mutated self._cart_snapshot in a way three other methods silently depended on, and rolled everything back.

This time I wanted to see if an AI coding agent could do the tedious parts while I kept control of the risky parts.

Setup: Claude Code (v2.x CLI), Python 3.12, pytest 8, a Django 5.1 app, roughly 92% line coverage on the service — which sounds great until you realize coverage tells you which lines ran, not which behaviors were asserted.

How I Solved It

The whole thing came down to four phases.

flowchart LR
    A[Characterization tests] --> B[Seam map]
    B --> C[Extract one module per PR]
    C --> D[Delete the facade]
    C -->|tests red| A

Phase 1: Pin the current behavior (even the weird parts)

Before moving a single line, I asked Claude Code to write characterization tests — tests that record what the code currently does, not what it should do.

The prompt that worked:

For each public method on OrderService, write pytest tests that capture
its CURRENT behavior, including edge cases that look like bugs.
Do not fix anything. If behavior looks wrong, add a comment
"# CHARACTERIZED: looks wrong, preserved on purpose" and assert it anyway.
Use real fixtures from tests/fixtures/, not mocks, wherever possible.

That "don't fix anything" line was crucial. Without it, the agent kept "helpfully" asserting the correct behavior, which then failed against real code, which it then tried to fix. Exactly the spiral I wanted to avoid.

It produced 214 new tests in an afternoon. I reviewed them for about two hours and found 11 cases tagged CHARACTERIZED — real bugs, including a discount that stacked twice when a coupon was applied after a gift card. I logged those as tickets and left the behavior frozen. Refactoring and bug-fixing in the same diff is how you lose a weekend.

Phase 2: Draw the seam map myself

This is where I made the most important decision of the project: the agent does not pick the module boundaries.

I did ask Claude Code to analyze the class. This prompt gave me a genuinely useful report:

List every instance attribute on OrderService. For each one, show which
methods read it and which methods write it. Output a table.
Then group methods into clusters that share writes.

The output was a read/write matrix that showed me the real coupling. A trimmed version:

Attribute	Written by	Read by
`_cart_snapshot`	`checkout`, `apply_coupon`, `recalc`	14 methods
`_tax_cache`	`compute_tax`	`checkout`, `refund`, `export_csv`
`_payment_intent`	`capture`, `refund`	`checkout`, `notify`

Then I drew the boundaries on paper: pricing, tax, inventory, payments, refunds, notifications, audit, exports, and a few smaller ones — 14 in total. Some of the agent's suggested clusters were perfectly sensible on paper but would have split things our team thinks of as one concept, or merged things that change for different reasons.

An AI can tell you what's coupled. It can't tell you what your team will understand six months from now.

Phase 3: One extraction per PR, facade stays alive

Every extraction followed the same recipe, which I wrote into a short instruction file the agent read at the start of each session:

Extraction rules:
1. Extract exactly ONE module per session. Name given by me.
2. OrderService keeps every public method. It delegates to the new module.
3. No behavior changes. No renames of public methods. No "while I'm here".
4. Run the full characterization suite before and after. Paste both summaries.
5. If any test changes, stop and explain why instead of editing the test.

Rule 2 is the facade pattern, and it's what made the whole thing safe. Callers never noticed anything was happening. After extracting tax logic, OrderService looked like this:

class OrderService:
    def __init__(self, order, *, tax_calculator=None):
        self.order = order
        self._tax = tax_calculator or TaxCalculator(order.region)

    def compute_tax(self, lines):
        # Delegates to extracted module; kept for existing callers.
        return self._tax.compute(lines)

And the new module was small, boring, and testable on its own:

@dataclass(frozen=True)
class TaxCalculator:
    region: str

    def compute(self, lines: list[LineItem]) -> Decimal:
        rate = RATES.get(self.region, DEFAULT_RATE)
        taxable = sum(l.amount for l in lines if not l.tax_exempt)
        return (taxable * rate).quantize(Decimal("0.01"))

Notice frozen=True and no mutable cache. The old _tax_cache turned out to save about 0.4ms per checkout, and removing it was one of the few deliberate changes I allowed — in its own PR, with a benchmark attached.

Each extraction PR averaged about 180 lines changed, which meant reviews took 15 minutes instead of an hour. My teammates could actually review them, which mattered as much as the code itself.

The war story: the hidden shared state

Extraction #6 was refunds. The agent moved the methods, the delegation looked clean, and all 214 characterization tests passed.

Then staging broke. Partial refunds on orders with a coupon were refunding the pre-discount amount.

What happened: the old refund() read self._cart_snapshot, which was set by apply_coupon() earlier in the same request — because the view called both on the same OrderService instance. The new RefundProcessor got its own fresh snapshot, built from the database, which didn't have the coupon applied yet because that transaction hadn't committed.

None of my characterization tests called apply_coupon() and refund() on the same instance. Each test built a fresh service. The tests pinned methods, not sequences.

The fix took ten minutes once I understood it — pass the snapshot explicitly instead of letting the refund module rebuild it. The lesson took longer to sink in. I went back and asked Claude Code to do something I should have done in Phase 1:

Search the codebase for every call site that invokes two or more
OrderService methods on the same instance. List the method sequences.
Then write one characterization test per unique sequence.

That found 23 distinct sequences across views, Celery tasks, and management commands. Three of them exercised shared state in ways that would have broken in later extractions. Those tests caught two more problems before they left my laptop.

Phase 4: Delete the facade

Once all 14 modules existed, OrderService was about 260 lines of pure delegation. I had the agent migrate call sites to use the new modules directly — this was the most mechanical part and where AI speed really paid off. It updated 142 call sites across 38 files in an afternoon, in batches of roughly 20 per commit so I could review each batch.

Then I deleted OrderService. Felt great. 🚀

Final tally after five weeks:

14 modules, the largest at 410 lines, the median at 190
checkout() complexity: 41 → 9
Characterization tests: 214 → 237 (plus the sequence tests)
11 frozen bugs logged and later fixed in separate PRs
Merge conflicts in order code: dropped from "most weeks" to roughly one a month
Production incidents caused by the refactor: zero

Lessons Learned

1. Characterization tests are the real deliverable. 💡 The refactor was almost a side effect. Once behavior was pinned, extracting modules became low-stakes. If you only take one thing from this post, it's this: have the agent freeze current behavior before it changes anything, and explicitly tell it not to fix what it finds.

2. Test sequences, not just methods. God classes hide coupling in call order on a shared instance. Per-method tests miss it entirely. Grepping for multi-method call sites should be step one, not something you learn after staging breaks.

3. Let the agent analyze coupling; you draw the boundaries. The read/write matrix was one of the most useful outputs of the whole project. The suggested clusters were not. Module boundaries are a team-communication decision as much as a technical one, and that's still on you.

4. Small PRs are a feature for humans, not just for safety. ⚠️ An AI can happily produce a 3,000-line diff. Nobody can review that. Capping the agent to one extraction per session kept every PR reviewable and kept my teammates involved, which is how knowledge of the new structure actually spread.

5. Write the rules down once, not in every prompt. The five-rule instruction file did more to keep the agent on track than any clever phrasing. "No 'while I'm here' changes" alone probably saved me a dozen rejected PRs.

What's Next

There are two more classes in the same codebase over 1,500 lines — a reporting service and a user-permissions module. I'm going to reuse the same playbook, but this time start with the sequence-scan prompt before anything else.

I'm also experimenting with having the agent generate the read/write matrix in CI on every PR that touches a large file. If a new method starts writing to an attribute that three other clusters read, that's a smell worth flagging early — before you end up with another 3,200-line monster.

Wrap-up

God classes don't need a heroic rewrite. They need pinned behavior, boundaries a human chose, and a lot of small, boring, reviewable steps — which is exactly the kind of work an AI coding agent is great at, as long as you keep the steering wheel.

If you've got your own monster class, I'd love to hear how you're approaching it — drop it in the comments. 👇

Follow me here on Dev.to for more build logs on working with Claude Code and AI coding agents on real, messy codebases. And if you haven't tried Claude Code on a refactor yet, start with the characterization-test prompt above on your scariest file — it's the safest first step you can take.

How I Fixed 19 Timezone Bugs in My Time-Tracking App With Claude Code

yureki_lab — Tue, 13 Oct 2026 14:31:30 +0000

TL;DR

My time-tracking app had been quietly lying to users in every timezone that wasn't mine. I used Claude Code (v2.x) to build a test harness that replays the app across 40 timezones and every DST boundary, found 19 real bugs, and fixed them in about a week. The secret wasn't the fixes — it was making the agent prove each bug with a failing test before it was allowed to touch a single line of date code.

The Problem

I build a small time-management app as a side project. You start a timer, you stop it, and at the end of the week you get a report of where your hours went. Simple, right?

Then the bug reports started showing up, and they all had the same flavor:

"My Monday report shows 25 hours."
"A session I logged at 11:30 PM shows up on the wrong day."
"My weekly streak reset even though I worked every day."
"After the clocks changed, every timer I started was off by an hour."

Every single one of these worked perfectly on my machine. Of course they did — I live in a single timezone, I develop in that timezone, and my CI runs in UTC. The app had effectively been tested in exactly two timezones, neither of which had daylight saving time in the configuration I was using.

The interesting constraint: date/time code is the worst possible place to let an AI agent "just fix things." Every fix looks obviously correct in isolation. new Date() → dayjs.utc(), done, ship it. But time bugs are almost never local. Changing how one function interprets a timestamp silently changes the meaning of every timestamp that flows through it. I've watched agents (and, honestly, myself) "fix" a timezone bug by moving it somewhere else.

So the goal wasn't "have Claude fix the timezone bugs." The goal was: build a system where a timezone bug can't be fixed until it's been proven, and can't come back once it's fixed.

How I Solved It

Step 1: Make the clock a dependency, not a global

The first thing I asked Claude Code to do wasn't fixing anything. It was an audit: find every place the code reads the current time or the local timezone directly.

The prompt was roughly:

Search the codebase for every direct read of wall-clock time or
local timezone: Date.now(), new Date() with no args, Intl APIs that
default to the runtime zone, getTimezoneOffset(), and any date
library call that implicitly uses local time. List each with file,
line, and what it's used for. Do not change anything yet.

It came back with 61 call sites. That number alone explained a lot. Sixty-one places where "now" and "here" were silently assumed.

Then I had it introduce a tiny clock abstraction and route everything through it:

// clock.ts
export interface Clock {
  now(): Date;          // an absolute instant
  zone(): string;       // IANA zone of the *user*, not the server
}

export const systemClock = (userZone: string): Clock => ({
  now: () => new Date(),
  zone: () => userZone,
});

export const fixedClock = (iso: string, zone: string): Clock => ({
  now: () => new Date(iso),
  zone: () => zone,
});

This refactor was boring, mechanical, and exactly the kind of thing an agent is great at. It touched 34 files and didn't change behavior. I verified "didn't change behavior" by running the existing test suite before and after — all green, same count.

Step 2: Build a harness that lives in hell

Once the clock was injectable, I could build the thing that actually found the bugs: a test harness that runs the same scenario across a matrix of timezones and instants that are known to be nasty.

// tz-matrix.ts
export const ZONES = [
  "UTC",
  "America/Los_Angeles",
  "America/St_Johns",       // UTC-3:30, half-hour offset
  "Europe/London",
  "Asia/Kolkata",           // UTC+5:30
  "Asia/Kathmandu",         // UTC+5:45 (yes, really)
  "Australia/Lord_Howe",    // 30-minute DST shift
  "Pacific/Kiritimati",     // UTC+14
  "Pacific/Pago_Pago",      // UTC-11
  // ...31 more
];

export const NASTY_INSTANTS = [
  "spring-forward minus 1 minute",
  "spring-forward plus 1 minute",
  "fall-back, first 1:30 AM",
  "fall-back, second 1:30 AM",
  "local midnight minus 1 second",
  "local midnight",
  "Sunday 23:59 (week boundary)",
  "Dec 31 23:59 (year boundary)",
  "Feb 29 (leap day)",
];

The instants are described in local terms on purpose. A helper resolves each one to an absolute UTC instant for each zone using the IANA tz database, so "spring-forward minus 1 minute" means something different in London than in Los Angeles — and something that doesn't exist at all in Kolkata, which is itself a useful test case.

Then every core behavior got a property-style test:

describe.each(ZONES)("weekly report in %s", (zone) => {
  it.each(NASTY_INSTANTS)("total hours are sane at %s", (instant) => {
    const clock = clockAt(instant, zone);
    const sessions = generateSessions(clock, { days: 7 });
    const report = buildWeeklyReport(sessions, clock);

    for (const day of report.days) {
      expect(day.hours).toBeLessThanOrEqual(25); // 25 = fall-back day
      expect(day.hours).toBeGreaterThanOrEqual(0);
    }
    expect(sumHours(report)).toBeCloseTo(sumHours(sessions), 5);
  });
});

Notice the invariants are about meaning, not specific values. "No day has more than 25 hours." "The report's total equals the sum of the sessions." "A session that starts and ends on the same local day appears on exactly one day." I wrote these invariants myself. That part I did not delegate.

40 zones × 9 instants × 12 behaviors = 4,320 test cases. They run in about 14 seconds.

The first run: 312 failures.

Step 3: Cluster before you fix

312 failures is not 312 bugs. My first instinct was to tell Claude "fix the failing tests," and I'm very glad I didn't.

Instead:

Here are 312 failing test cases. Group them by root cause, not by
test name. For each group, write one sentence describing the actual
bug, the smallest failing case, and which function you believe is
responsible. Don't propose fixes yet.

That collapsed the 312 failures into 19 distinct root causes. A few highlights:

Day bucketing used server time. Sessions were grouped by toISOString().slice(0, 10) — the UTC date. Anyone east or west of UTC by enough hours got late-night sessions on the wrong day. This one bug was responsible for ~90 failures by itself.
Duration computed from local wall-clock fields. (end.getHours() - start.getHours()) instead of subtracting instants. On fall-back night, a 2-hour session became 1 hour. On spring-forward, it became 3.
Week start was hardcoded to Sunday in UTC. Users in Asia/Kolkata had their Monday morning counted as last week.
Streak logic compared dates as strings in different zones. One side was the user's local date, the other side was UTC. Two days apart in Pacific/Kiritimati looked like the same day.
Kathmandu broke the "round to 15 minutes" helper, because it assumed offsets are multiples of 30 minutes. It's a +5:45 zone. My rounding produced timestamps that didn't exist.

Step 4: Prove it, then fix it, one root cause at a time

Here's the workflow rule that made the whole thing safe. For each root cause, Claude had to:

Write one focused regression test that reproduces only that bug, outside the giant matrix.
Show me that test failing, with the actual output.
Propose the fix as a diff, touching only the function identified in the clustering step.
Run the focused test (must pass) and the full matrix (failure count must go down, never up).

flowchart LR
    A[Cluster failures] --> B[Pick one root cause]
    B --> C[Write focused failing test]
    C --> D{Fails for the right reason?}
    D -- No --> C
    D -- Yes --> E[Minimal fix]
    E --> F[Run focused test + full matrix]
    F --> G{Matrix failures decreased<br/>and none new?}
    G -- No --> H[Revert and rethink]
    H --> E
    G -- Yes --> I[Commit]
    I --> B

Step 4's "never up" rule caught the agent three times. The most memorable one: its first fix for the day-bucketing bug converted everything to the user's zone at read time. Correct! Except the streak logic was already doing its own conversion, so streaks were now converted twice. Matrix failures went from 312 down to 220 — and 14 brand-new failures showed up in streak tests that had been passing. Without the full matrix, that would have shipped looking like a clean win.

I also enforced a single rule in the project spec file (CLAUDE.md) that every session reads:

## Time handling rules
- Store and transmit instants as UTC ISO-8601 with a Z suffix. Always.
- Convert to the user's zone only at the presentation edge
  (reports, UI labels, day bucketing).
- Durations are instant subtraction. Never subtract wall-clock fields.
- Never call Date.now() or new Date() directly. Use the injected Clock.
- Any change to date logic must run the full timezone matrix.

That last rule matters more than it looks. Agents follow rules that are written down and checkable far better than rules that are implied by "the codebase style."

The results

Over six working days:

19 root causes fixed, each with its own regression test
Matrix went from 312 failures → 0
61 direct clock reads → 0 (enforced by a lint rule Claude also wrote)
Bug reports about "wrong day" / "wrong hours" since then: zero, after about a month in production

Total time I personally spent: maybe 10 hours, almost all of it writing invariants and reviewing diffs.

Lessons Learned

1. 💡 Write the invariants yourself. Delegate everything else.

The single highest-leverage thing I did was write a dozen sentences like "no day has more than 25 hours." The agent wrote thousands of lines of test harness and fixes around those sentences, but the sentences were the spec. If I'd let the agent invent the invariants, it would have encoded the current (buggy) behavior as correct. Agents are great at satisfying a definition of "right." They're bad at choosing one.

2. ⚠️ "Fix the failing tests" is a trap with time code

With 312 failures, an agent told to make tests pass will find the fastest path to green — and with date logic, the fastest path is often adjusting an expected value or adding a special case for one zone. Clustering by root cause first turned an unbounded mess into 19 small, reviewable problems.

3. ✅ A test matrix is a ratchet, not just a test

The rule "failure count must go down and no new failures may appear" did more than any code review. It caught three fixes that were locally correct but globally wrong. Any time you let an agent touch something with wide blast radius, give it a ratchet it can't cheat.

4. 🚀 Make the dangerous thing impossible, not discouraged

"Please don't use new Date()" in a prompt works most of the time. A lint rule that fails CI works every time. Once a rule mattered, I turned it into a check — and asked the agent to write the check. It's weirdly good at writing guardrails against its own mistakes.

5. 🌏 Test the zones that make you uncomfortable

My bugs didn't show up in Los Angeles or London. They showed up in Kathmandu (+5:45), Lord Howe (30-minute DST), and Kiritimati (+14). If your test zones are all whole-hour offsets that you've heard of, you're not testing time handling — you're testing your own geography.

What's Next

A few things I'm working on now:

Recurring events. "Every weekday at 9 AM" across a DST change is its own special nightmare, and the matrix doesn't cover recurrence rules yet.
Running the matrix on every PR from my autonomous coding setup. I run a fully autonomous implementation system that picks up tasks from a queue; date-related changes now automatically trigger the full timezone matrix before anything is merged.
Travelling users. Right now the user's zone is a profile setting. People who fly from Tokyo to New York mid-week deserve a report that makes sense, and I haven't figured out what "makes sense" even means there yet.

Wrap-up

Timezone bugs are the perfect example of where AI coding agents shine and where they're most dangerous: tons of mechanical work, and every fix looks right in isolation. The trick is to keep the definition of "correct" in human hands and let the agent grind through everything else — with a ratchet it can't cheat.

If you've got your own timezone horror story (I know you do), drop it in the comments — I'm collecting zones to add to the matrix. 👇

Follow me here on Dev.to for more build logs on running AI coding agents in real projects, and if you haven't tried Claude Code on a gnarly refactor yet, a date/time audit is a great first job to give it.

How I Hunted Down 23 N+1 Queries in a Rails App With Claude Code

yureki_lab — Mon, 12 Oct 2026 14:31:23 +0000

TL;DR

Our Rails app's p95 response time had crept from 280 ms to 1.4 s over a year, and nobody could say why. I paired with Claude Code for four days, found 23 N+1 queries, fixed 19 of them, and got p95 back under 400 ms. The real win wasn't the fixes, though: it was making Claude prove every N+1 with a failing test before touching the code, because the first "fix everything with includes" pass made two endpoints slower.

The Problem

Here's the setup: a seven-year-old Rails monolith (Rails 7.1, Ruby 3.3, PostgreSQL 16), about 140 models, a B2B dashboard that customers live in all day. Nothing was on fire. It was just... slow. The kind of slow where support tickets say "the app feels sluggish" and nobody can reproduce anything specific.

When I pulled APM data, the pattern was obvious in hindsight:

p95 on the dashboard endpoint: 1.4 s (was ~280 ms a year earlier)
Average SQL queries per request on the top 10 endpoints: 187
Worst single request I could find: 2,341 queries to render one page

Nobody had written a "bad" query. Every one of those queries was a perfectly reasonable SELECT ... WHERE id = $1. The problem was that they ran inside loops, inside partials, inside serializers, inside helper methods that called other helper methods. Classic N+1, spread across hundreds of files.

I'd tried fixing this by hand twice before. Both times I got through three or four endpoints, got pulled onto feature work, and the gains evaporated as new code reintroduced the same patterns. This time I wanted two things:

Find all of them, not just the ones I happened to notice.
Make sure they stay fixed.

That second goal turned out to be the one that mattered.

How I Solved It

Step 1: Measure before touching anything

My first instinct was to open Claude Code and say "find N+1 queries in this app." I did exactly that, and it came back with a list of 60+ "suspicious" associations based on reading the code. Most were false positives: associations that looked like they'd loop, but were only ever called on a single record.

Static reading isn't enough for N+1s. You need runtime evidence. So I backed up and asked Claude to help me build a tiny query counter that logs per-request query counts with the call site that triggered them:

# config/initializers/query_counter.rb
module QueryCounter
  THRESHOLD = 50

  def self.track
    counts = Hash.new(0)
    sub = ActiveSupport::Notifications.subscribe("sql.active_record") do |*, payload|
      next if payload[:name] == "SCHEMA" || payload[:cached]
      site = caller_locations.find { |l| l.path.include?("/app/") }
      counts["#{site&.path}:#{site&.lineno}"] += 1
    end
    yield
    counts
  ensure
    ActiveSupport::Notifications.unsubscribe(sub)
  end
end

Then a around_action in development and staging that logs anything over the threshold, sorted by hottest call site. Running our existing request specs plus a 20-minute click-through of the dashboard produced a ranked list of every line in app/ that fired the same query more than ~10 times in one request.

That list had 23 entries. Not 60. Twenty-three real, reproducible N+1s, each with an exact file and line number.

💡 This was the single highest-leverage thing I did all week. Claude is great at fixing a problem it can see. It's mediocre at guessing which problems are real.

Step 2: The mistake — "just add includes everywhere"

With the list in hand, I got greedy. I handed Claude all 23 call sites and said "fix these with eager loading."

It did. Fast. Every fix looked textbook:

# before
@projects = current_account.projects.active

# after
@projects = current_account.projects.active
  .includes(:owner, :tags, tasks: [:assignee, :comments])

Tests passed. I deployed to staging. Two endpoints got slower. The project list went from 900 ms to 1.6 s.

What happened: tasks: [:assignee, :comments] eager-loaded every comment on every task on every project, for a page that only displayed a comment count. For our biggest customer that meant pulling ~80,000 comment rows into Ruby memory to call .size on them. We'd swapped 400 tiny queries for one enormous one plus a mountain of object allocation.

Claude wasn't wrong, exactly. It solved the problem I described ("eager-load these"), not the problem I had ("make this page fast"). That's on me.

Step 3: Failing test first, every single time

I reverted and switched to a strict loop. For each of the 23 call sites:

Write a test that asserts a query budget for that endpoint, and confirm it fails.
Only then, let Claude propose a fix.
Re-run the test plus a quick timing check against a realistic data fixture.

The query-budget helper is tiny:

# spec/support/query_budget.rb
RSpec::Matchers.define :make_at_most do |expected|
  supports_block_expectations

  match do |block|
    @actual = 0
    counter = ->(*, payload) { @actual += 1 unless payload[:cached] || payload[:name] == "SCHEMA" }
    ActiveSupport::Notifications.subscribed(counter, "sql.active_record", &block)
    @actual <= expected
  end

  failure_message { "expected at most #{expected} queries, got #{@actual}" }
end

And a typical spec:

it "renders the project list in a bounded number of queries" do
  create_list(:project, 25, :with_tasks, account: account)
  expect { get projects_path }.to make_at_most(12).queries
end

The key detail: the test seeds 25 records, not 1. With one record, an N+1 is invisible. With 25, it explodes the budget immediately.

Once the failing test existed, the conversation with Claude changed completely. Instead of "add includes," I'd say: "This spec fails with 214 queries. Make it pass. The view only shows the comment count, not the comments." And it would reach for the right tool:

# counter cache instead of eager-loading 80k rows
class Comment < ApplicationRecord
  belongs_to :task, counter_cache: true
end

Across the 23 call sites, the fixes broke down like this:

Fix	Count
Targeted `includes` / `preload`	9
Counter cache column	4
Single aggregate query (`group(...).count`)	4
Moved logic out of a partial loop	2
Left alone (not worth it)	4

That last row matters. Four of the N+1s were on admin pages used by three people a week, with max N around 10. Fixing them meant a migration and a backfill. Not worth it. Claude actually flagged two of those itself when I asked "is this fix worth the complexity?", which I appreciated.

Step 4: Lock it in CI

The query-budget specs are now regular specs. They run on every PR. If someone adds a loop that lazy-loads an association on the dashboard, the build goes red with a message like:

Failure/Error: expect { get dashboard_path }.to make_at_most(15).queries
  expected at most 15 queries, got 63

That's a far better feedback loop than "p95 crept up 40 ms this quarter, anyone know why?"

Here's the overall flow I ended up with:

flowchart LR
    A[Runtime query log] --> B[Ranked call sites]
    B --> C[Write failing budget spec]
    C --> D[Claude proposes fix]
    D --> E{Spec passes + timing OK?}
    E -- No --> D
    E -- Yes --> F[Commit spec + fix]
    F --> G[CI guards forever]

The results

After four days (roughly 26 hours of actual work):

Dashboard p95: 1.4 s → 380 ms ✅
Average queries per request (top 10 endpoints): 187 → 14 ✅
Worst request: 2,341 queries → 31 ✅
New query-budget specs: 19, all running in CI
Regressions caught by those specs in the first three weeks: 3 🚀

That last number is my favorite. Three PRs would have quietly reintroduced N+1s. Instead, they failed the build, and the authors fixed them before review.

Lessons Learned

1. Runtime data beats static analysis for performance bugs

Asking an AI agent to "find N+1 queries" by reading code gives you a list of suspects, not culprits. Give it a log of actual query counts per call site and it stops guessing. Measure first, then delegate.

2. Describe the outcome, not the technique

"Add eager loading here" gets you eager loading, even when it's the wrong call. "This page shows a count, make it under 12 queries" gets you a counter cache. The agent is only as good as the goal you hand it. If you name the technique, you've already made the decision for it.

3. A failing test is the best prompt you can write

A red spec with a concrete number ("got 214, expected ≤12") is unambiguous in a way no English sentence is. It also gives the agent a way to check its own work, so you're not the verification step. Every time I skipped this, I regretted it.

4. Seed realistic N in tests

One record hides every N+1. Twenty-five exposes them. If your factories default to creating a single child record, your performance tests are lying to you.

5. "Not worth fixing" is a valid outcome

I almost let Claude fix all 23 just because it could. But each fix has a cost: migrations, backfills, more code to maintain. Asking "is this worth it?" explicitly, and letting the agent argue both sides, saved me two pointless migrations.

What's Next

A few things I'm working on now:

Budget specs for background jobs. The same N+1 patterns show up in nightly report jobs, where they cost CPU instead of latency. Nobody notices until a job runs past its window.
Auto-generating budget specs. I'm experimenting with having Claude draft a query-budget spec for any new controller action in a PR, so the guardrail exists from day one instead of after the slowdown.
Memory budgets. Query count was the obvious metric. Allocated objects per request is the less obvious one, and the counter-cache episode taught me it matters just as much.

Wrap-up

If your Rails app has gotten mysteriously slow, I'd bet money it's N+1s, and I'd bet more that fixing them without tests means they'll be back in six months. Measure first, write the failing spec, then let your AI agent do the tedious part.

Tested with Claude Code v2.x, Rails 7.1, Ruby 3.3, PostgreSQL 16 (October 2026).

👉 Follow me on Dev.to for more build logs on shipping real code with AI coding agents. And if you've got a war story about an "optimization" that made things slower, drop it in the comments. I want to hear it.

How I Tracked Down a Race Condition in Async Python With Claude Code

yureki_lab — Sun, 11 Oct 2026 14:31:24 +0000

TL;DR

A background worker in one of my Python services was sending duplicate webhooks about once every 3,000 jobs. It never happened locally, never in tests, and only sometimes in production. I used Claude Code to turn a "can't reproduce" ticket into a deterministic, 100%-failing test in one afternoon, and the actual fix ended up being 11 lines. Here's the exact process, plus the lessons I'd steal if I were you.

The Problem

The setup was boring, which is exactly why the bug was so annoying:

Python 3.12, asyncio, an async Postgres driver
A worker pulls "send webhook" jobs off a queue and runs them with a concurrency limit of 20
Each job checks whether the webhook was already delivered, and if not, sends it and records the delivery

Customers started reporting that they occasionally got the same order.paid event twice. Not often. Our logs said roughly 0.03% of jobs produced a duplicate. Downstream, a few customers' systems treated each webhook as a new payment notification, so "rare" still meant real support tickets.

The kicker:

❌ Never reproduced on my laptop
❌ Never showed up in the test suite (~900 tests, all green)
❌ Adding debug logging made it less frequent (classic heisenbug)

I'd already burned most of a day staring at the code. Everything looked correct. That's the thing about race conditions: each line is fine, it's the gaps between them that kill you.

How I Solved It

Step 1: Give the agent the symptoms, not my theory

My first instinct was to tell Claude Code "I think it's the retry logic." I stopped myself. I'd been wrong about the retry logic for four hours already, and if I fed the agent my theory, it would happily go confirm it.

Instead, I gave it raw evidence:

Symptom: duplicate webhook deliveries, ~0.03% of jobs, production only.
Evidence: attached 40 log lines for two duplicate pairs (job IDs + timestamps).
Constraints: worker concurrency = 20. Jobs can be re-enqueued on timeout.
Task: list every code path where the same delivery could be sent twice.
Do NOT fix anything yet. Rank the paths by likelihood using the timestamps.

That last line matters. "Do not fix anything yet" keeps the agent in investigation mode instead of jumping straight to a plausible-looking patch.

It came back with four candidate paths. Path #2 was the one I'd been ignoring: two different jobs for the same delivery (one original, one re-enqueued after a timeout) running concurrently in the same worker process. The timestamps in my logs showed both copies starting within ~40 ms of each other. I had been assuming re-enqueued jobs only ran after the original died. They didn't.

Step 2: Find the check-then-act gap

Here's a simplified version of the handler (names changed, logic intact):

async def handle_delivery(job: Job) -> None:
    delivery = await repo.get_delivery(job.delivery_id)

    if delivery.status == "sent":
        return  # already done, skip

    await http.post(delivery.url, json=delivery.payload)  # ~100-800 ms

    await repo.mark_sent(delivery.id)

Read it top to bottom and it seems fine. But every await is a point where the event loop can switch to another task. So with two jobs for the same delivery:

sequenceDiagram
    participant A as Job A (original)
    participant B as Job B (re-enqueued)
    participant DB as Postgres
    participant C as Customer endpoint
    A->>DB: get_delivery → status = pending
    B->>DB: get_delivery → status = pending
    A->>C: POST webhook
    B->>C: POST webhook (duplicate!)
    A->>DB: mark_sent
    B->>DB: mark_sent

Classic check-then-act race. The check (status == "sent") and the act (http.post) are separated by awaits, and nothing makes them atomic.

Claude Code spotted this within minutes once it had the "two jobs, same delivery" framing. Honestly, I could have spotted it too. The value wasn't the insight; it was being forced to stop assuming.

Step 3: Make the race deterministic

A theory isn't a fix. I've shipped "fixes" for race conditions before that just shifted the timing and made the bug rarer. I wanted a test that failed every single time before the fix and passed every time after.

The trick is to control the interleaving instead of hoping for it. I asked Claude Code to write a test that pauses both tasks at the exact gap using asyncio.Event:

import asyncio
import pytest

@pytest.mark.asyncio
async def test_concurrent_jobs_send_only_once(fake_repo, fake_http):
    both_checked = asyncio.Event()
    checks = 0

    original_get = fake_repo.get_delivery

    async def gated_get(delivery_id):
        nonlocal checks
        result = await original_get(delivery_id)
        checks += 1
        if checks == 2:
            both_checked.set()
        await both_checked.wait()  # hold both tasks right after the check
        return result

    fake_repo.get_delivery = gated_get

    job = Job(delivery_id="d-1")
    await asyncio.gather(handle_delivery(job), handle_delivery(job))

    assert fake_http.post_count == 1

Before the fix: AssertionError: assert 2 == 1, 50 out of 50 runs. 🎯

This was the moment the ticket went from "intermittent, can't reproduce" to "known bug with a red test." That shift is worth more than any amount of log staring.

A note on version drift: this used pytest-asyncio 0.24 in strict mode. Older versions handle event loops differently, so if you copy this pattern, check which loop your fixtures run on.

Step 4: Fix it at two layers

Claude Code's first proposal was a single asyncio.Lock per delivery ID. That fixes the test, but it only protects one process. We run three worker replicas, so two copies of the same job on different machines would still race.

I pushed back, and we landed on two layers:

Layer 1: claim the delivery atomically in the database.

async def handle_delivery(job: Job) -> None:
    claimed = await repo.claim_delivery(job.delivery_id, worker_id=WORKER_ID)
    if not claimed:
        return  # someone else owns it

    delivery = await repo.get_delivery(job.delivery_id)
    await http.post(
        delivery.url,
        json=delivery.payload,
        headers={"Idempotency-Key": delivery.id},
    )
    await repo.mark_sent(delivery.id)

-- claim_delivery: only one caller can win
UPDATE deliveries
SET status = 'sending', claimed_by = $2, claimed_at = now()
WHERE id = $1
  AND (status = 'pending'
       OR (status = 'sending' AND claimed_at < now() - interval '5 minutes'))
RETURNING id;

The UPDATE ... WHERE status = 'pending' is atomic in Postgres. Two concurrent callers can't both match the row, so exactly one gets a result back. The 5-minute clause lets a crashed worker's claim expire so deliveries don't get stuck forever.

Layer 2: idempotency key for the receiver. Even with a perfect claim, there's still the case where the POST succeeds but we crash before mark_sent. Then the claim expires and we send again. You can't fully eliminate that on the sender side (it's the old "exactly-once delivery" problem), so the Idempotency-Key header lets well-behaved receivers drop duplicates. We also documented it for customers.

After deploying: zero duplicates in 21 days, down from roughly 6 per day. The deterministic test now lives in CI as a regression guard.

Lessons Learned

1. Don't hand your AI agent your hypothesis

This was the biggest one. When I'm stuck, my theory is usually the reason I'm stuck. Coding agents are very good at building a convincing case for whatever you point them at. Give them symptoms, evidence, and constraints, and ask for a ranked list of possibilities. Then say "don't fix yet."

2. A race you can't reproduce on demand isn't understood yet

If your test passes "most of the time" before the fix, you haven't proven anything. Gate the interleaving with asyncio.Event, barriers, or injected sleeps until the failure is 100% deterministic. AI agents are great at writing this kind of scaffolding, which is tedious for humans and exactly the kind of thing you'd skip at 6 p.m.

3. Every `await` is a door

In async Python, it's tempting to think "single-threaded means no races." Wrong. Any await between a check and the act it guards is an opening for another task. I now ask the agent, during review, to list every await between a read and a dependent write. It's a cheap, mechanical check that catches real bugs.

4. In-process locks are a trap for distributed workers

The agent's first fix (asyncio.Lock) was correct for the code it could see and wrong for the system it couldn't. It didn't know we ran three replicas. That's not the agent's fault, it's a context gap. Lesson: tell the agent about your deployment topology when the bug smells like concurrency. One sentence ("we run N replicas behind a shared queue") changes the answer.

5. Push correctness down to the database

Application-level checks are suggestions. A conditional UPDATE ... RETURNING or a unique constraint is a guarantee. When I need "exactly one winner," I now reach for the database first and treat app-level locks as a performance optimization, not a correctness mechanism.

What's Next

A few things I'm working on now that this bug is dead:

A concurrency review checklist that the agent runs on any PR touching job handlers: list awaits between check and act, flag in-process locks, ask about replica count.
A small helper for gated interleaving tests, so writing the "pause both tasks right here" scaffolding takes 3 lines instead of 20.
Auditing the other 14 job handlers in the same service for the same pattern. The first pass already flagged two more check-then-act gaps (lower impact, but still real).

If the audit turns up anything interesting, I'll write a follow-up with the numbers.

Wrap-up

Race conditions feel like bad luck, but they're usually just a missing guarantee. The combination that worked for me: give the agent raw evidence instead of my theory, force a deterministic repro, then fix it where the guarantee actually lives.

If this was useful:

👉 Follow me on Dev.to for more build logs on working with AI coding agents in real codebases
💬 Drop a comment with the nastiest race condition you've hunted down. I'm collecting war stories
🚀 If you haven't tried Claude Code for debugging yet, start with a bug you've already given up on. That's where it surprised me most

How I Ran a Zero-Downtime Postgres Migration on a 180M-Row Table With Claude Code

yureki_lab — Sat, 10 Oct 2026 14:31:57 +0000

TL;DR

Our events table was about to hit the 2,147,483,647 ceiling on its int4 primary key, and it had 180 million rows and zero tolerance for downtime. I used Claude Code to plan and write an expand/contract migration to bigint that ran over 9 days with no locked writes. The agent was a fantastic co-pilot for the boring 80%, and confidently wrong in two places that would have taken production down.

The Problem

Here's the alert that started it:

events_id_seq: 1,931,204,118 / 2,147,483,647 (89.9%)
projected exhaustion: ~41 days

The events table is the busiest table in the app. It takes roughly 1,400 inserts per second at peak, it's referenced by three other tables via foreign keys, and our API reads from it on almost every request. It was created years ago with serial, which means int4. Once the sequence hits the max, every insert fails. Not "slows down". Fails.

The textbook fix is one line:

ALTER TABLE events ALTER COLUMN id TYPE bigint;

And that one line is a trap. It takes an ACCESS EXCLUSIVE lock and rewrites the whole table plus every index on it. On 180M rows (~210 GB with indexes), our staging rehearsal of that command ran for 3 hours and 12 minutes. Three hours of no reads and no writes on the core table is not a migration, it's an outage with extra steps.

The constraints I set for myself:

No statement may hold a heavy lock for more than ~2 seconds
Replication lag on the read replicas must stay under 5 seconds
Every step must be reversible until the final swap
It has to be done well before the 41-day deadline, with buffer for surprises

Setup for context: PostgreSQL 16.4 on a managed service, a Node.js 22.x API, and Claude Code (v2.x at the time) running in my terminal with read access to the schema and a staging database.

How I Solved It

Step 0: Make the agent understand the blast radius first

Before writing any SQL, I asked Claude Code to map everything that touched events.id. Not just the table, but the whole graph:

> Find every foreign key, index, view, trigger, and application query
> that depends on events.id. Output a table. Do not propose a fix yet.

That "do not propose a fix yet" line matters. Without it, the agent jumps straight to a plan and the inventory gets sloppy. With it, I got a clean list: 3 FK columns in other tables (also int4, also need migrating), 4 indexes, 1 materialized view, and 11 ORM queries that cast the id in application code. Two of those casts I didn't know existed.

Step 1: Expand — add the new column and keep it in sync

The plan we landed on is the classic expand/contract pattern:

flowchart LR
  A[Add id_new bigint] --> B[Trigger syncs new writes]
  B --> C[Batched backfill of old rows]
  C --> D[Build unique index CONCURRENTLY]
  D --> E[Validate NOT NULL via CHECK]
  E --> F[Swap in one short transaction]
  F --> G[Contract: drop old column]

Adding a nullable column with no default is metadata-only in modern Postgres, so it's instant:

SET lock_timeout = '2s';
ALTER TABLE events ADD COLUMN id_new bigint;

Then a trigger so that every new or updated row gets id_new populated automatically:

CREATE OR REPLACE FUNCTION events_sync_id_new() RETURNS trigger AS $$
BEGIN
  NEW.id_new := NEW.id;
  RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER events_sync_id_new
BEFORE INSERT OR UPDATE ON events
FOR EACH ROW EXECUTE FUNCTION events_sync_id_new();

The SET lock_timeout = '2s' line became a rule for every DDL statement in this project. If the ALTER can't get its lock within 2 seconds (because a long-running query is holding something), it gives up instead of queueing. That matters because a queued ACCESS EXCLUSIVE request blocks every query behind it, even plain reads. A failed DDL you can retry. A lock queue pile-up you can't undo.

Step 2: Backfill in small, throttled batches

This is where Claude Code saved me the most typing. I asked for a backfill script with explicit requirements: batches by primary key range, a pause between batches, and a check on replica lag that slows down automatically.

Here's the load-bearing part of what we ended up with (Node.js, pg client):

const BATCH = 20_000;
const MAX_LAG_SECONDS = 5;

async function replicaLagSeconds(replica) {
  const { rows } = await replica.query(
    `SELECT COALESCE(EXTRACT(EPOCH FROM now() - pg_last_xact_replay_timestamp()), 0) AS lag`
  );
  return Number(rows[0].lag);
}

async function backfill(primary, replica, startId, endId) {
  for (let lo = startId; lo <= endId; lo += BATCH) {
    const hi = lo + BATCH - 1;
    await primary.query(
      `UPDATE events SET id_new = id
       WHERE id BETWEEN $1 AND $2 AND id_new IS NULL`,
      [lo, hi]
    );

    let lag = await replicaLagSeconds(replica);
    while (lag > MAX_LAG_SECONDS) {
      await sleep(2_000);            // back off until replicas catch up
      lag = await replicaLagSeconds(replica);
    }
    await saveCheckpoint(hi);         // resumable after a crash or deploy
  }
}

A few details that made a real difference:

Range-based batches, not LIMIT/OFFSET. Offsets get slower as you go. Ranges on the primary key stay constant-time.
AND id_new IS NULL makes each batch idempotent. Re-running a range is harmless.
A checkpoint after every batch. The script got killed twice (once by a deploy, once by me), and both times it resumed from the last saved id.

The backfill ran for about 5.5 days at an average of ~380 rows/second during peak hours and much faster at night, when the lag check rarely kicked in. Peak replica lag during the whole run: 4.1 seconds.

Step 3: Build the index without blocking writes

You can't make id_new a primary key without a unique index, and a normal CREATE INDEX blocks writes for the whole build. So:

CREATE UNIQUE INDEX CONCURRENTLY events_id_new_idx ON events (id_new);

That took 47 minutes, with writes flowing normally the whole time.

Step 4: Prove NOT NULL without a full-table lock

A primary key requires NOT NULL. ALTER COLUMN ... SET NOT NULL normally scans the entire table under a heavy lock. The trick (Postgres 12+) is to add a CHECK constraint as NOT VALID, validate it separately with a weaker lock, and then let SET NOT NULL reuse that proof:

SET lock_timeout = '2s';
ALTER TABLE events
  ADD CONSTRAINT events_id_new_not_null CHECK (id_new IS NOT NULL) NOT VALID;

-- Takes SHARE UPDATE EXCLUSIVE: reads and writes keep working
ALTER TABLE events VALIDATE CONSTRAINT events_id_new_not_null;

Step 5: The swap — one short, rehearsed transaction

Everything above was preparation for about 1.4 seconds of actual lock time:

BEGIN;
SET LOCAL lock_timeout = '2s';

ALTER TABLE events ALTER COLUMN id_new SET NOT NULL;  -- instant, uses the CHECK
ALTER TABLE events DROP CONSTRAINT events_pkey;
ALTER TABLE events
  ADD CONSTRAINT events_pkey PRIMARY KEY USING INDEX events_id_new_idx;

ALTER SEQUENCE events_id_seq AS bigint;
ALTER TABLE events ALTER COLUMN id_new SET DEFAULT nextval('events_id_seq');
ALTER SEQUENCE events_id_seq OWNED BY events.id_new;
ALTER TABLE events ALTER COLUMN id DROP DEFAULT;

ALTER TABLE events RENAME COLUMN id TO id_old;
ALTER TABLE events RENAME COLUMN id_new TO id;

DROP TRIGGER events_sync_id_new ON events;
COMMIT;

We rehearsed this on a staging snapshot 6 times. In production it ran in 1.4 seconds. The first attempt actually hit the lock_timeout because of a long analytics query, rolled back cleanly, and the second attempt 30 seconds later went through. That's exactly the behavior you want.

The 3 foreign-key tables went through the same dance afterwards (smaller tables, so faster), and a week later we dropped id_old once we were confident nothing read it.

Where the Agent Was Confidently Wrong

I want to be honest here, because this is the part most "I did X with AI" posts skip.

❌ Mistake 1: `CONCURRENTLY` inside a migration transaction

The first migration file Claude Code generated wrapped CREATE UNIQUE INDEX CONCURRENTLY in our migration tool's default transaction. Postgres rejects that outright (CREATE INDEX CONCURRENTLY cannot run inside a transaction block), so it would have failed loudly, not silently. Annoying, but safe. The fix was to mark that migration as non-transactional.

⚠️ Mistake 2: The "simpler" swap that would have caused an outage

This one scared me. Midway through, I asked the agent to "simplify the swap step". Its suggestion dropped the NOT VALID / VALIDATE dance and went straight to SET NOT NULL inside the swap transaction, with the comment "the column is fully backfilled, so this is fast."

The column was fully backfilled. But SET NOT NULL without a validated CHECK still scans all 180M rows to prove it, while holding ACCESS EXCLUSIVE. On staging, that "simplified" swap held the lock for 4 minutes and 38 seconds. In production, that's every API request timing out at once.

The agent's reasoning was correct about the data and wrong about the locking behavior. That's the pattern I now watch for: AI agents reason well about what the data looks like and badly about what the database does to get there.

Lessons Learned

Make the agent inventory before it plans. "Find every dependency, don't propose a fix yet" produced a far better map than "how do I migrate this column?" The two hidden casts in app code would have been production bugs.
Encode your safety rules as constraints, not hopes. lock_timeout = '2s' on every DDL and a replica-lag guard in the backfill turned "be careful" into something the database enforces. The agent followed these rules perfectly once they were written down in the task.
Never accept "simplify" on a lock-sensitive step without a staging rehearsal. Ask the agent to state which lock each statement takes. When I started asking "what lock does this acquire, and for how long?", the answers got much more careful.
Rehearse the swap until it's boring. Six rehearsals felt excessive. Then the first production attempt hit a lock timeout, and nobody panicked because we'd already seen that exact rollback.
Let the agent write the boring code, but you own the timeline. Claude Code wrote ~90% of the scripts and SQL. I owned the order of operations, the go/no-go calls, and the rollback plan. That split felt right.

What's Next

We have 4 more int4 tables on the "within 2 years" list. My plan is to turn this whole flow into a reusable runbook with the agent: a checklist template, a parameterized backfill script, and a staging rehearsal step that measures lock duration automatically and fails the run if any statement exceeds 2 seconds. The goal is that the next migration is mostly a review job, not a 9-day project.

Wrap-up

If you're sitting on a serial column on a busy table, check your sequence today:

SELECT last_value FROM events_id_seq;

Then compare it to 2,147,483,647. Future you will be grateful.

👉 Follow me on Dev.to for more build logs on shipping real systems with AI coding agents, and drop a comment with your own migration horror story. I'm especially curious whether anyone has a cleaner way to handle the foreign-key tables. 🚀

How I Hunted Down a Node.js Memory Leak With Claude Code: 5 Lessons

yureki_lab — Fri, 09 Oct 2026 14:31:21 +0000

TL;DR

One of our Node.js 22 API services was slowly eating memory until Kubernetes OOM-killed it roughly every 30 hours. I paired with Claude Code (CLI, October 2026 build) to hunt it down. The win didn't come from the AI "knowing" the answer — it came from forcing it to work from heap snapshots instead of hunches. Two leaks, one afternoon, and five lessons I'd steal if I were you.

The Problem

The service was boring in the best way: an Express-style HTTP API, a Postgres pool, a Redis client, and a few hundred requests per second at peak. It had been stable for a year.

Then the memory graph started looking like a staircase:

Fresh pod: ~180 MB RSS
After 12 hours: ~620 MB
After ~30 hours: hits the 1 GiB limit, gets OOM-killed, restarts, repeat

Nobody noticed for weeks because the restarts were "self-healing." Then a restart landed during a batch import and we dropped a few hundred in-flight requests. Suddenly it mattered.

The annoying constraints:

It didn't reproduce locally. A quick autocannon run on my laptop showed flat memory.
The growth was slow. ~15 MB per hour is invisible in a 5-minute test.
The diff since "it was fine" was huge. Three months of dependency bumps and ~140 merged PRs.

My first instinct was to paste the memory graph into Claude Code and ask "why is this leaking?" I did exactly that. It gave me a confident list of seven plausible causes — closures, global arrays, unclosed DB connections, timers — all reasonable, none grounded in our code. That was the moment I changed approach.

How I Solved It

Step 1: Make the agent collect evidence, not opinions

Instead of asking for causes, I asked Claude Code to help me instrument the service. The rule I gave it was explicit:

Don't propose a fix until we have two heap snapshots that show which object types grew.

First, a tiny memory logger so we could see whether the growth was in the V8 heap or outside it (native buffers, etc.):

// memory-probe.js — loaded only when MEMORY_PROBE=1
const INTERVAL_MS = 60_000;

setInterval(() => {
  const m = process.memoryUsage();
  console.log(JSON.stringify({
    probe: "memory",
    rssMb: Math.round(m.rss / 1e6),
    heapUsedMb: Math.round(m.heapUsed / 1e6),
    externalMb: Math.round(m.external / 1e6),
    arrayBuffersMb: Math.round(m.arrayBuffers / 1e6),
  }));
}, INTERVAL_MS).unref();

After a few hours in staging, the answer was clear: heapUsed tracked RSS almost one-to-one. Buffers were flat. So this was a plain old JS object leak, which is the good kind — heap snapshots can find it.

Step 2: Heap snapshots on demand

Node has a built-in flag for this, and Claude Code suggested it before I remembered it existed:

node --heapsnapshot-signal=SIGUSR2 server.js

Then in staging:

# snapshot 1, right after warm-up
kill -USR2 <pid>
# ...wait ~3 hours under replayed traffic...
# snapshot 2
kill -USR2 <pid>

To get realistic traffic, I had Claude Code write a small replay script that took a sample of sanitized access logs and fired them at staging at roughly production shape. This turned out to be the key, because synthetic load on one endpoint never triggered the leak.

Step 3: Let the agent read the diff — but give it the right diff

Heap snapshots are large JSON files (ours were ~250 MB each), so pasting them into a chat is a non-starter. Instead I loaded both into Chrome DevTools, used the Comparison view, sorted by # Delta, and exported the top rows as plain text. That summary was what I handed to Claude Code:

Constructor            # New    # Deleted   # Delta    Size Delta
(string)               912,340  301,220     +611,120   +41.2 MB
Object                 455,002  120,889     +334,113   +28.7 MB
Map                    18,421   210         +18,211    +6.9 MB
(closure)              37,550   1,104       +36,446    +3.1 MB
TLSSocket              0        0           0          0

Now the conversation got useful. I asked it: "Given this delta, list the places in our codebase that create long-lived Maps and long-lived closures. For each, tell me whether entries are ever deleted."

It came back with a table of 11 candidates. Two stood out.

Leak #1: A "temporary" cache with no eviction

Someone (fine, it was me, eight months ago) had added an in-memory cache for a per-tenant pricing lookup:

// before
const priceCache = new Map();

export async function getPrice(tenantId, sku, currency) {
  const key = `${tenantId}:${sku}:${currency}`;
  if (priceCache.has(key)) return priceCache.get(key);
  const price = await loadPriceFromDb(tenantId, sku, currency);
  priceCache.set(key, price);
  return price;
}

With a handful of tenants this was fine. After a large customer onboarded with ~200,000 SKUs and four currencies, the key space exploded. Nothing was ever evicted. That explained the (string) and Object growth.

The fix was boring, which is how fixes should be:

// after
import { LRUCache } from "lru-cache";

const priceCache = new LRUCache({
  max: 50_000,           // hard ceiling on entries
  ttl: 10 * 60 * 1000,   // prices can change; 10 minutes is plenty
});

Leak #2: An event listener added per request

The (closure) and Map growth pointed somewhere else. Claude Code flagged this middleware, which subscribed to a shared emitter to support request cancellation:

// before
app.use((req, res, next) => {
  const onShutdown = () => req.socket.destroy();
  lifecycle.on("shutdown", onShutdown);
  next();
});

Every request added a listener to a process-wide emitter and never removed it. Each closure held a reference to req, which kept the entire request object graph alive. We had even been getting MaxListenersExceededWarning in the logs — someone had "fixed" that months ago by calling lifecycle.setMaxListeners(0). Ouch.

// after
app.use((req, res, next) => {
  const onShutdown = () => req.socket.destroy();
  lifecycle.on("shutdown", onShutdown);
  res.on("close", () => lifecycle.off("shutdown", onShutdown));
  next();
});

I also had Claude Code remove the setMaxListeners(0) call and add a test that asserts the listener count returns to baseline after 1,000 requests:

test("shutdown listeners do not accumulate", async () => {
  const before = lifecycle.listenerCount("shutdown");
  await Promise.all(
    Array.from({ length: 1000 }, () => request(app).get("/health"))
  );
  expect(lifecycle.listenerCount("shutdown")).toBe(before);
});

Step 4: Prove it, don't assume it

Here's the flow I ended up using, roughly:

flowchart LR
  A[Memory graph looks wrong] --> B[Probe: heap vs external?]
  B --> C[Two heap snapshots under replayed traffic]
  C --> D[Comparison view: top deltas]
  D --> E[Agent maps deltas to code]
  E --> F[Fix + regression test]
  F --> G[Re-run replay 6h, compare again]

After deploying both fixes to staging and replaying traffic for six hours, heapUsed plateaued at ~210 MB and stayed there. In production, pods have now run for over two weeks without a restart.

Total time: about 3.5 hours of focused work, most of which was waiting for snapshots.

Lessons Learned

1. "Why is this leaking?" is the wrong prompt 💡

When I asked for causes, I got a generic checklist. When I asked the agent to map specific evidence to specific code, I got two real bugs. AI coding agents are great at search and correlation across a codebase; they're mediocre at guessing runtime behavior they can't observe. Feed them observations.

2. Summarize big artifacts before handing them over

A 250 MB heap snapshot is useless in a context window. A 10-line delta table is gold. The skill isn't "give the agent everything" — it's compress the evidence to the part that discriminates between hypotheses. This applies to logs, traces, and profiles too.

3. Realistic load beats heavy load

My laptop benchmark hammered one endpoint and showed nothing. The leak only appeared with the real mix of tenants and SKUs. Having the agent write a log-replay script was the single highest-leverage thing it did all day.

4. Silenced warnings are leak detectors you turned off ⚠️

MaxListenersExceededWarning was literally telling us about leak #2 for months. If you've ever written setMaxListeners(0), go look at why. I now ask Claude Code to grep for suppressed warnings as part of any performance investigation.

5. Every fix ships with a test that would have caught it

The listener-count test takes 200 ms to run and would have caught leak #2 the day it was introduced. For the cache, we added a bounded-size assertion. It's tempting to stop when the graph goes flat. Don't — the graph will be someone else's problem in six months.

What's Next

A few things I'm working on now:

A nightly "soak" job that replays an hour of sanitized traffic against a staging pod and fails if heapUsed grows more than 10% after warm-up.
A lint rule that flags new Map() at module scope without an accompanying size bound or eviction strategy.
Turning this into a reusable debugging playbook for my agent, so the "evidence first, fixes second" rule is the default rather than something I have to remember to say.

Wrap-up

Memory leaks feel like black magic until you treat them as a data problem. Claude Code didn't magically know where the leak was — but once I gave it heap deltas instead of vibes, it found both culprits faster than I would have alone.

If you've got a leak war story (or a better heap-diff workflow), drop it in the comments 👇 I read every one.

Follow me here on Dev.to for more build-in-public posts on working with AI coding agents in real production codebases — next up is the soak-test setup. And if you haven't tried Claude Code for debugging yet, start with an evidence-first prompt on your next weird bug. 🚀

How I Broke 37 Circular Imports in a Python Codebase With Claude Code

yureki_lab — Thu, 08 Oct 2026 14:31:22 +0000

TL;DR

I had a ~180k-line Python service with 37 circular import chains, held together by function-level imports and prayer. I used Claude Code to map the import graph, rank the cycles, and break them one PR at a time — then added a CI contract so they can't come back. It took 8 working days. The biggest lesson: give the agent the graph, not the error message.

The Problem

If you've worked on a Python codebase that's older than three years, you've probably seen this:

ImportError: cannot import name 'InvoiceService' from partially initialized
module 'billing.services' (most likely due to a circular import)

Our service — a billing and subscriptions backend, roughly 180,000 lines of Python 3.12, Django-ish layering but hand-rolled — had been "fixing" these for years the same way: move the import inside the function.

def create_invoice(customer_id: int):
    # avoid circular import
    from billing.services import InvoiceService
    ...

I counted. There were 214 # avoid circular import comments in the repo. Every one of them was a tiny lie that said "these modules don't depend on each other at load time" when they absolutely did.

Why it actually mattered (not just aesthetics):

⚠️ Import order bugs in tests. Running a single test file would pass; running the whole suite in a different order would explode. We had 6 tests marked "skip on CI" for exactly this reason.
⚠️ Cold start time. Lazy imports were scattered across hot paths, so the first request to some endpoints paid a ~400 ms import tax.
⚠️ Nobody could reason about layers. "Can models import from services?" The honest answer was "sometimes, depending on which file you're in."

I'd tried to fix this by hand once before. I got through four cycles in two days, broke a webhook handler in staging, and quietly gave up. This time I wanted to see if an AI coding agent could do the tedious part while I made the design calls.

How I Solved It

Step 1: Build the graph first (don't let the agent guess)

My first attempt was the naive one: paste the ImportError into Claude Code and say "fix this circular import." It fixed it — by adding another function-level import. Technically correct, completely useless.

The agent wasn't wrong; it just didn't have the full picture. A single traceback shows you one edge of a cycle. So I generated the whole import graph and handed that over instead.

I used pydeps to dump module dependencies as JSON, then a tiny script to find strongly connected components:

import json
import networkx as nx

deps = json.load(open("deps.json"))
g = nx.DiGraph()
for mod, info in deps.items():
    for imported in info.get("imports", []):
        if imported in deps:  # only first-party modules
            g.add_edge(mod, imported)

cycles = [c for c in nx.strongly_connected_components(g) if len(c) > 1]
cycles.sort(key=len, reverse=True)

for i, c in enumerate(cycles, 1):
    print(f"{i:>2}. size={len(c):>2}  {sorted(c)}")

Output (trimmed):

 1. size=14  ['billing.models', 'billing.services', 'billing.tasks', ...]
 2. size= 6  ['accounts.permissions', 'accounts.models', 'audit.log', ...]
 3. size= 3  ['notifications.email', 'notifications.templates', 'billing.models']
...
37. size= 2  ['reports.csv', 'reports.formatters']

37 strongly connected components. One monster with 14 modules, and a long tail of 2–3 module loops.

Step 2: Rank by blast radius, not by size

My instinct was "start with the giant one." Wrong. I asked Claude Code to score each cycle by:

How many other modules import any member of the cycle (fan-in)
How many lazy imports live inside it
Whether any member is on a request hot path

Then I sorted ascending. Small, isolated cycles first. Each one I fixed shrank the big component a little, because several small cycles shared edges with the monster. By the time I reached cycle #1, it had dropped from 14 modules to 5.

💡 This was the single best decision of the project. Starting with the 14-module beast would've meant a 3,000-line PR nobody could review.

Step 3: One cycle per PR, with a fixed prompt

For each cycle, I gave Claude Code (v2.x at the time) the same structured prompt:

## Cycle #23
Members: notifications.email, notifications.templates, billing.models

Edges inside this cycle (from the graph, not a guess):
- notifications.email -> notifications.templates
- notifications.templates -> billing.models
- billing.models -> notifications.email   <-- suspicious

## Rules
- Do NOT add function-level imports to break the cycle.
- Prefer, in order: (1) move the shared thing to a lower layer,
  (2) depend on an interface/Protocol, (3) invert via a callback/event.
- Remove any existing "avoid circular import" lazy imports this unlocks.
- Touch only modules in this cycle plus at most one new module.
- Explain which edge you removed and why that edge was the wrong one.

That last rule — "explain which edge was wrong" — turned out to be the most valuable line. It forced the agent to make an architectural claim I could agree or disagree with, instead of just making the error go away.

For cycle #23, the answer was obvious once stated: models should never know about email. billing.models was importing an email helper to send a receipt inside a save() override. The fix was to emit a domain event and let the notifications layer subscribe:

# billing/events.py  (new, lowest layer — imports nothing from billing)
from dataclasses import dataclass
from typing import Callable

@dataclass(frozen=True)
class InvoicePaid:
    invoice_id: int
    customer_id: int

_subscribers: list[Callable[[InvoicePaid], None]] = []

def subscribe(fn: Callable[[InvoicePaid], None]) -> None:
    _subscribers.append(fn)

def publish(event: InvoicePaid) -> None:
    for fn in _subscribers:
        fn(event)

# notifications/email.py
from billing.events import InvoicePaid, subscribe

@subscribe
def send_receipt(event: InvoicePaid) -> None:
    ...

The model now calls publish(InvoicePaid(...)) and has zero idea email exists. Edge removed, cycle gone.

Step 4: The three fix patterns that covered everything

Across all 37 cycles, every fix fell into one of three buckets:

Pattern	Cycles	Example
Move shared code down a layer	19	Constants and enums living in `services` moved to a `types` module
Depend on a `Protocol` instead of a class	11	Permission checks typed against `HasOwner` instead of importing `Account`
Invert with events/callbacks	7	The receipt example above

graph TD
    A[api] --> S[services]
    S --> M[models]
    M --> T[types / events]
    S --> T
    N[notifications] --> T
    N -.subscribes.-> T

Once the layers looked like this, the remaining "big" cycle mostly dissolved on its own.

Step 5: Lock it in with a CI contract

Fixing cycles is pointless if they come back in three weeks. I added import-linter with layer contracts:

[importlinter]
root_package = app

[importlinter:contract:layers]
name = Layered architecture
type = layers
layers =
    app.api
    app.services
    app.models
    app.types

Plus a forbidden rule banning notifications from being imported by anything in models. This runs in about 4 seconds in CI and fails the build if anyone (human or agent) reintroduces an upward import.

I also added a tiny lint rule that flags new # avoid circular import comments. If you need one, the contract is telling you something.

The results

✅ 37 → 0 strongly connected components among first-party modules
✅ 214 → 9 function-level imports (the 9 remaining are legit optional-dependency imports)
✅ 6 → 0 tests skipped for import-order flakiness
✅ Cold start on the worst endpoint dropped from ~1.1 s to ~0.7 s
✅ 31 PRs, average ~140 lines changed each, every one reviewed in under 15 minutes
❌ One regression: a Celery task registration that silently relied on a side-effect import. Caught in staging, fixed in an hour.

Total: 8 working days, maybe 25 hours of my actual attention.

Lessons Learned

1. Give the agent the graph, not the traceback.
A traceback shows one edge. An agent fixing one edge will pick the cheapest hack — a lazy import. When I handed over the full strongly connected component with every edge listed, the fixes became architectural instead of cosmetic. Context shape matters more than prompt cleverness.

2. "Explain which edge was wrong" is the best review question I've found.
It turns a diff into a claim. I disagreed with the agent's choice on 5 of 37 cycles — and in every case, the disagreement was a genuinely useful design discussion, not a "the code is broken" fight. That's what I want from an AI pair.

3. Ban the lazy fix explicitly.
If the rules don't forbid function-level imports, the agent will use them, because they work. Agents optimize for "error gone." You have to define what "fixed" means.

4. Small cycles first. Always.
Big cycles are usually several small cycles sharing edges. Chip away at the edges and the monster shrinks. This also kept every PR reviewable, which matters more than speed when an agent is writing the code.

5. A fix without a guardrail is a loan, not a payment.
The import-linter contract took 20 minutes to set up and is the only reason I'm confident this work will still hold in six months.

What's Next

I'm planning to run the same graph-first approach on our frontend — a TypeScript app with madge reporting 22 cycles. My hunch is the pattern split will look different (way more "move shared types down" and fewer events), and I'm curious whether the "explain the wrong edge" rule holds up there too.

I'm also experimenting with having the agent propose layer contracts by reading the graph before any fixes, rather than me writing them by hand. Early results: it gets the layers right about 80% of the time and is weirdly opinionated about where utils belongs. (It's right. utils should not exist.)

Wrap-up

If you've got a pile of # avoid circular import comments, you don't have a few annoying bugs — you have an undocumented architecture. Map the graph, hand it to your agent with strict rules, go small-first, and lock it in with a contract.

👉 If this was useful, follow me here on Dev.to — I write build logs about shipping real refactors with Claude Code and autonomous coding agents, wins and failures included.

💬 And I'd love to hear: what's the worst circular import you've ever untangled? Drop it in the comments. 🚀

How I Upgraded a Django 3.2 App to Django 5.2 With Claude Code in 6 Days

yureki_lab — Wed, 07 Oct 2026 14:31:29 +0000

TL;DR

I upgraded a ~70,000-line Django 3.2 monolith to Django 5.2 LTS in six working days, with Claude Code doing most of the mechanical work. The trick wasn't a clever prompt. It was hopping one LTS at a time, turning deprecation warnings into hard errors, and giving the agent a single, boring loop to follow. Here's the exact setup, the three places the agent got it wrong, and five lessons I'd apply to any framework upgrade.

The Problem

Django 3.2 went end-of-life in April 2024. Our app was still on it in 2026. Not because nobody cared, but because every time someone opened the upgrade ticket, they'd look at the changelog for four major releases and quietly close the tab.

Here's what we were dealing with:

~70,000 lines of Python across 31 Django apps
Python 3.9 in production (Django 5.2 needs 3.10+)
46 third-party packages, several of which hadn't seen a release in years
~2,100 tests, about 78% line coverage, with a handful of known-flaky ones
One team member who remembered why USE_TZ was set the way it was (spoiler: they didn't)

The constraint that made it interesting: I had roughly one sprint, and I couldn't freeze feature work. Whatever I did had to land in small, reviewable PRs that the rest of the team could keep merging around.

I'd already been using Claude Code (2.x) for refactors, so the question was: can an AI coding agent grind through a multi-version framework upgrade without me babysitting every file?

Short answer: yes, if you take away its freedom.

How I Solved It

Step 1: Hop LTS to LTS, never skip

The single most important decision was refusing to jump straight from 3.2 to 5.2. Django's deprecation policy is designed around this: a feature deprecated in version X keeps working (with a warning) until X+2. If you jump too far, the warnings that would have told you what to fix are already gone, and you just get ImportErrors.

So the plan looked like this:

flowchart LR
    A[Django 3.2<br/>Python 3.9] --> B[Python 3.10<br/>Django 3.2]
    B --> C[Django 4.2 LTS<br/>Python 3.10]
    C --> D[Python 3.12<br/>Django 4.2]
    D --> E[Django 5.2 LTS<br/>Python 3.12]

Each arrow is its own branch, its own green CI run, and its own deploy. Python and Django never moved in the same PR. When something broke in staging, I knew exactly which variable changed.

Step 2: Make deprecation warnings fatal

Django emits RemovedInDjangoXXWarning classes, which subclass Python's DeprecationWarning family. By default those are mostly silent. I flipped them into hard failures in the test run:

# Run the full suite with every deprecation warning raised as an exception
python -W error::DeprecationWarning \
       -W error::PendingDeprecationWarning \
       manage.py test --parallel 4 2>&1 | tee /tmp/upgrade-run.log

That turned a vague "upgrade Django" task into a concrete, finite list of failing tests with stack traces pointing at the exact line. That list is exactly the kind of thing an agent is good at chewing through.

One gotcha: third-party packages also raise these warnings, and you can't fix their code. I added targeted ignores in pytest.ini-style config (we use the Django runner, so it went in a small warnings.filterwarnings block in the test settings) scoped by module, so our own code stayed strict:

# settings/test.py
import warnings

warnings.filterwarnings("error", category=DeprecationWarning)
# Vendor noise we can't fix yet; tracked in the upgrade checklist
warnings.filterwarnings(
    "ignore",
    category=DeprecationWarning,
    module=r"some_unmaintained_package\..*",
)

Step 3: Let a codemod do the boring 60%

Before the agent touched anything, I ran django-upgrade, a deterministic rewriter for Django-specific patterns:

git ls-files -- '*.py' | xargs django-upgrade --target-version 4.2

It handled the boring, high-volume stuff: url() to re_path(), ugettext to gettext, request.is_ajax() replacements, index_together hints, and a lot more. That one command touched about 340 files.

Why run this before the agent? Because a deterministic tool is cheaper, faster, and never hallucinates. I wanted the agent's attention spent on the judgment calls, not on find-and-replace.

Step 4: Give the agent one loop, and nothing else

This is where Claude Code came in. The instruction I gave it was intentionally narrow. Roughly:

## Upgrade loop (Django 4.2 hop)

1. Run the test command in `scripts/upgrade-test.sh`.
2. Take the FIRST failing test only.
3. Read the full traceback and the relevant Django 4.x release notes section.
4. Make the smallest change that fixes it. Do not refactor nearby code.
5. Re-run that single test, then the app's test module.
6. Commit with message: `upgrade(4.2): <what changed and why>`.
7. Go back to step 1.

Stop and ask me if:
- a fix requires changing a settings default
- a fix touches a migration file
- a third-party package needs a version bump

The "first failing test only" rule did a lot of work. Without it, the agent would see 140 failures, try to fix 30 at once, and produce a diff nobody could review. With it, I got a steady stream of tiny commits, most under 20 lines.

The "stop and ask" list was the other half. Those three categories are where upgrades actually hurt you in production, and I wanted a human looking at every one of them.

Step 5: Triage third-party packages up front

The 46 dependencies were the real schedule risk. I had the agent build a table before touching any code:

package                  current   supports 4.2?  supports 5.2?  action
-----------------------  --------  -------------  -------------  -----------------
django-filter            2.4.0     yes (23.x)     yes (25.x)     bump
django-storages          1.11      yes (1.14)     yes (1.14)     bump + STORAGES
some_unmaintained_pkg    0.9.2     unknown        no             replace / vendor
...

It filled the "supports" columns by reading each package's changelog and classifiers on PyPI. I spot-checked about a third of them. It got two wrong (it trusted a classifier list that was stale), which is why I don't let it do this unsupervised.

Out of 46 packages: 38 just needed a version bump, 5 needed config changes, and 3 had to be replaced or vendored. Knowing that on day one meant no surprises on day five.

Where the Agent Got It Wrong

Three moments where I'm glad the "stop and ask" rule existed.

⚠️ 1. `USE_TZ` silently flipping

In Django 5.0, the default for USE_TZ changed from False to True. Our settings file never set it explicitly. We'd been relying on the old default for years.

The agent noticed the deprecation warning in the 4.2 hop and proposed adding USE_TZ = True "to match the new default." That would have made every naive datetime we stored start being interpreted as UTC. For a product with scheduled jobs in local time, that's a quiet data-corruption bug.

The right fix was the opposite: pin USE_TZ = False explicitly, ship the upgrade, and then do the timezone migration as its own project. Because this touched a settings default, the agent stopped and asked. ✅

⚠️ 2. `CSRF_TRUSTED_ORIGINS` needing a scheme

Django 4.0 started requiring a scheme in CSRF_TRUSTED_ORIGINS (https://example.com, not example.com). The agent fixed it correctly in settings, but only for the value in the base settings file. Our staging and production values came from environment variables parsed in a different module.

Tests passed. Staging login broke. 🙃

Lesson there was mine, not the agent's: tests only cover what's in the test settings. I added a startup check that validates every origin has a scheme and fails loudly at boot.

⚠️ 3. Form rendering changed every template

Django 5.0 switched the default form rendering to div-based templates. Our test suite didn't catch it because tests assert on behavior, not markup. But about a dozen pages had CSS that targeted the old table/paragraph structure.

The agent's first suggestion was to rewrite the CSS. I asked it to instead pin the old renderer for the upgrade PR and open a follow-up to migrate templates intentionally. Again: upgrade first, modernize second. Mixing them is how a one-week project becomes a one-month project.

The Numbers

After six working days:

4 deploy-sized PRs (Python 3.10, Django 4.2, Python 3.12, Django 5.2), plus ~20 small prep PRs
187 agent commits, median size 11 lines changed
~340 files rewritten by the codemod, ~160 touched by the agent
0 rollbacks in production, 1 staging incident (the CSRF one)
Test suite runtime dropped from 14m to 11m on Python 3.12, which I didn't expect but will happily take 🚀

I spent most of my time reviewing, answering the agent's "stop and ask" questions, and reading release notes for the edge cases. I wrote maybe 200 lines of code myself.

Lessons Learned

1. Small hops beat big jumps, always. Deprecation warnings are a map. If you skip versions, you throw the map away. Hop LTS to LTS, and keep language and framework upgrades in separate PRs.

2. Turn the upgrade into a failing test list before involving the agent. "Upgrade Django" is a vague goal. "Make these 140 tests pass, one at a time" is a task. Agents are dramatically better at the second one.

3. Use deterministic tools first, agents second. Codemods are free, fast, and correct. Save the agent for the work that needs reading and judgment. My rough rule now: if a regex could do it, the agent shouldn't.

4. Defaults are the most dangerous diff. The scariest changes in this upgrade weren't renamed imports. They were settings whose default changed under us. Any time a framework changes a default, pin the old value explicitly, ship, then migrate on purpose.

5. Write down where the agent must stop. My "stop and ask" list had three items: settings defaults, migrations, and dependency bumps. That list caught every risky change. An agent with no stop conditions will happily "fix" things in the most locally reasonable and globally wrong way.

What's Next

The upgrade is done, but it left a short list of intentional follow-ups:

Migrate to USE_TZ = True properly, with a data audit of every naive datetime column
Move our templates to the new div-based form rendering and drop the pinned renderer
Replace the last vendored package with a maintained alternative

I'm also turning the upgrade loop above into a reusable template, so the next hop (Django 6.x LTS, whenever it lands) is a two-day job instead of a six-day one.

Wrap-up

If you've been staring at an old framework version and dreading the changelog, my honest take: the mechanical part is no longer the hard part. The hard part is deciding what the agent is not allowed to touch.

👉 If this was useful, follow me here on Dev.to. I write build logs like this every week about shipping real software with AI coding agents. And if you've done a big framework upgrade with Claude Code (or failed to), drop your war story in the comments. I'd love to compare stop-and-ask lists. 💡

How I Made My Autonomous Coding Agent Survive API Rate Limits and Outages

yureki_lab — Tue, 06 Oct 2026 14:31:21 +0000

TL;DR

My fully autonomous implementation system runs 24/7, and for months its biggest killer wasn't bad code — it was the model API saying "slow down" or "I'm overloaded" at 3 a.m. I fixed it with four boring pieces: an error classifier, jittered backoff with a retry budget, a circuit breaker shared across all parallel agents, and a "checkpoint and park" step instead of crashing. Here's the build log and the five lessons I'd tell past me.

The Problem

My setup is a small fleet: an orchestrator module that hands tasks to several parallel implementation agents, plus a self-healing agent that picks up broken work. Everything talks to a hosted LLM API. When it works, I wake up to merged branches and green CI.

When it doesn't, I wake up to this:

[02:14:07] agent-3  task=refactor-auth  ERROR 429 rate_limit_error
[02:14:07] agent-1  task=add-pagination ERROR 429 rate_limit_error
[02:14:08] agent-2  task=fix-flaky-test ERROR 429 rate_limit_error
[02:14:09] agent-3  retrying (1/5)...
[02:14:09] agent-1  retrying (1/5)...
[02:14:09] agent-2  retrying (1/5)...
...
[02:14:31] agent-3  giving up. task marked FAILED

Three things were going wrong at once:

Every error looked the same. A 429 (rate limit), a 529/503 (provider overloaded), a 400 (my prompt was too big), and a network reset all went through one except Exception: retry() path.
Agents retried in lockstep. Four agents hit the limit together, backed off for the same fixed time, then hit it together again. Classic thundering herd.
"Giving up" meant losing work. A task 40 minutes into a refactor would be marked failed, and the next run started from scratch — burning even more tokens and rate limit.

Over one rough two-week stretch I counted it: 31% of failed tasks weren't real failures at all. They were transient API errors that my system turned into permanent ones.

The constraint that made it interesting: I can't just "add more quota." Limits are per account, all agents share them, and outages happen whether I like it or not. The system had to degrade gracefully, not just retry harder.

How I Solved It

Here's the shape of the final design:

flowchart LR
    A[Agent call] --> B{Circuit open?}
    B -- yes --> P[Checkpoint & park task]
    B -- no --> C[Call model API]
    C -- ok --> D[Continue task]
    C -- error --> E[Classify error]
    E -- fatal --> F[Fail fast, explain why]
    E -- retryable --> G{Retry budget left?}
    G -- yes --> H[Jittered backoff] --> C
    G -- no --> I[Trip breaker] --> P

All code below is Python 3.13, trimmed to the load-bearing parts.

1. Classify before you retry

The single highest-value change was refusing to treat errors as one bucket.

from enum import Enum

class ErrKind(Enum):
    RATE_LIMIT = "rate_limit"   # 429: we're too fast
    OVERLOADED = "overloaded"   # 529/503: they're too busy
    TRANSIENT = "transient"     # timeouts, resets, 500/502
    FATAL = "fatal"             # 400/401/403/413: retrying won't help

def classify(status: int | None, exc: Exception | None) -> ErrKind:
    if status == 429:
        return ErrKind.RATE_LIMIT
    if status in (503, 529):
        return ErrKind.OVERLOADED
    if status is None or status in (500, 502, 504):
        return ErrKind.TRANSIENT  # includes connection resets
    return ErrKind.FATAL

That FATAL bucket matters more than it looks. Before this, a prompt that exceeded the context window got retried five times — five identical, guaranteed failures, each one eating rate limit that the other agents needed. Now a 400 fails immediately with a clear reason, and the self-healing agent can actually do something useful with it (like splitting the task).

2. Jittered backoff with a retry budget, not a retry count

Fixed retries ("try 5 times") were the root of the lockstep problem. I switched to exponential backoff with full jitter, and I honor retry-after when the API sends it:

import random

BASE = {ErrKind.RATE_LIMIT: 2.0, ErrKind.OVERLOADED: 10.0, ErrKind.TRANSIENT: 1.0}
CAP = 120.0

def backoff(kind: ErrKind, attempt: int, retry_after: float | None) -> float:
    if retry_after is not None:
        # Server knows best, but add a little jitter so agents don't sync up.
        return retry_after + random.uniform(0, 2)
    ceiling = min(CAP, BASE[kind] * 2 ** attempt)
    return random.uniform(0, ceiling)  # full jitter

The bigger idea is the retry budget. Instead of "5 attempts per call," each task gets a time budget for waiting — say, 8 minutes total across the whole task. A task that's had a smooth run can absorb one long overload; a task that's been fighting errors all along runs out sooner and gets parked.

import time

class RetryBudget:
    def __init__(self, seconds: float):
        self.remaining = seconds

    def spend(self, wait: float) -> bool:
        if wait > self.remaining:
            return False
        self.remaining -= wait
        time.sleep(wait)
        return True

This made behavior predictable. I know the worst-case time a task spends waiting, regardless of how many calls it makes.

3. One circuit breaker for the whole fleet

Jitter spreads retries out, but four agents independently backing off still means four agents independently probing a provider that's clearly down. So I added a circuit breaker — and crucially, made it shared. All agents read and write one small state file (a lock-protected JSON file was enough; no Redis needed at my scale).

import json, time
from pathlib import Path
from filelock import FileLock

STATE = Path("breaker.json")
LOCK = FileLock("breaker.json.lock")
THRESHOLD, COOLDOWN = 6, 300  # 6 failures -> open for 5 min

def breaker_open() -> bool:
    with LOCK:
        s = json.loads(STATE.read_text()) if STATE.exists() else {}
        return time.time() < s.get("open_until", 0)

def record(ok: bool) -> None:
    with LOCK:
        s = json.loads(STATE.read_text()) if STATE.exists() else {"fails": 0}
        s["fails"] = 0 if ok else s.get("fails", 0) + 1
        if s["fails"] >= THRESHOLD:
            s["open_until"] = time.time() + COOLDOWN
            s["fails"] = 0
        STATE.write_text(json.dumps(s))

When the breaker opens, nobody calls the API for five minutes. After the cooldown, the first agent to wake up acts as the probe. If it succeeds, the count resets and everyone else proceeds. If it fails, the breaker re-opens. This took my "error storm" logs from hundreds of lines to a handful.

4. Checkpoint and park instead of crash

This was the piece that actually saved work. When a task runs out of retry budget or hits an open breaker, it doesn't fail. It writes a checkpoint and goes into a PARKED state:

def park(task, reason: str) -> None:
    task.checkpoint = {
        "branch": task.branch,               # work is already committed here
        "completed_steps": task.done_steps,
        "next_step": task.current_step,
        "notes": task.summary_so_far(),      # short, human-readable
    }
    task.status = "PARKED"
    task.park_reason = reason
    task.save()

The orchestrator treats PARKED differently from FAILED: parked tasks get resumed first once the breaker closes, starting from next_step with the notes injected into the prompt. Because each agent commits to its branch after every meaningful step, "resume" is cheap — the agent reads the checkpoint and the diff, not the whole history.

Here's what the same night looks like now:

[02:14:07] agent-3  429 rate_limit -> wait 3.1s (budget 476s left)
[02:14:08] agent-1  429 rate_limit -> wait 0.7s (budget 480s left)
[02:14:11] agent-2  529 overloaded -> wait 14.2s
[02:14:40] breaker  OPEN for 300s (6 consecutive failures)
[02:14:40] agent-3  PARKED at step 4/7 (breaker open)
[02:14:41] agent-1  PARKED at step 2/5 (breaker open)
[02:19:41] agent-2  probe OK -> breaker CLOSED
[02:19:42] agent-3  RESUMED from step 4/7

The numbers

After running this for about six weeks:

✅ Tasks failing due to transient API errors: 31% → under 3% of all failures
✅ Wasted retries on fatal errors (oversized prompts, etc.): effectively zero
✅ Average extra wall-clock per parked task: ~7 minutes, versus a full restart before
⚠️ Morning surprises: still non-zero, but now they're real bugs, which is what I want to wake up to

Lessons Learned

1. Most "agent failures" are infrastructure failures wearing a costume. 💡 Before blaming the model's reasoning, check how many failures were the network or the provider. For me it was almost a third. Fixing plumbing beat any prompt tweak I tried that month.

2. Never retry an error you haven't classified. A blanket retry turns a cheap, instant failure (bad request) into a slow, expensive one — and it steals capacity from healthy work. If you only do one thing from this post, do the classifier.

3. Budget time, not attempts. "5 retries" means wildly different things depending on backoff. A per-task wait budget gives you a hard ceiling you can reason about and makes long tasks and short tasks behave fairly.

4. Parallel agents need shared failure state. Each agent being individually polite is not enough. If they don't share a breaker, they will collectively hammer a struggling API. Shared state can be embarrassingly simple — a locked JSON file worked fine for a handful of agents.

5. Design for pause, not just success or failure. The PARKED state was the real unlock. Long-running autonomous work needs a third outcome: "I stopped safely and I know exactly where to pick up." That only works if the agent commits often and leaves short notes as it goes.

What's Next

A few things I'm working on now:

Priority-aware throttling — when quota is tight, let short, high-value tasks go first and park the big refactors.
Fallback models for low-risk steps — things like summarizing a diff or writing a commit message don't need the strongest model, so they shouldn't compete for the same limit.
Surfacing breaker trips on my remote control dashboard, so I can see at a glance whether a quiet night was "nothing to do" or "provider was down."

Wrap-up

If you're running agents unattended — even a single Claude Code session in a loop — your reliability ceiling is probably your error handling, not your prompts. Classify, back off with jitter, share a breaker, and park instead of crash.

👉 Follow me on Dev.to for more build logs from running a fully autonomous coding system, and drop a comment: what's the weirdest way an API outage has broken your agent setup? I'd love to compare war stories. 🚀

How I Built Git Checkpoints So I Can Undo Anything My AI Coding Agent Does

yureki_lab — Mon, 05 Oct 2026 14:31:58 +0000

TL;DR

My fully autonomous implementation system runs coding tasks while I sleep, and for a long time my only "undo" was git reset --hard plus hope. I replaced that with per-task checkpoints stored on hidden git refs: a snapshot of the entire working tree (untracked files included) before every task, automatic rollback when verification fails, and a restore command that is itself undoable. Here's the ~40 lines of shell that do the heavy lifting, plus the five lessons that changed how I think about agent safety. 🚀

The Problem

I run an orchestrator module that hands tasks to parallel implementation agents overnight. Most mornings that's great. One morning it wasn't.

The queue had seven tasks. Task 3 was "clean up the config loader." The agent's cleanup was technically passing tests, but it had quietly changed how defaults were resolved. Tasks 4 through 7 were then built on top of that change. By the time I opened my laptop, I had four tasks of perfectly reasonable work stacked on one bad foundation.

My options were all bad:

❌ git reset --hard to before the run: throws away tasks 1–2 (good) and 4–7 (mostly good)
❌ Manually untangle the diff: the "cleanup" was smeared across the same files the later tasks touched
❌ Ask the agent to "undo task 3": it happily produced a new change that approximated the old behavior, which is not the same thing as undoing

And there was a second, sneakier problem. A different night, an agent failed a task halfway, and the next task started from that half-edited working tree. The half-finished edits weren't committed anywhere. They just leaked forward, and the second task got blamed for breakage it didn't cause.

Both failures have the same root cause: I had no cheap, exact, mechanical way to go back. Everything I'd built so far was about preventing bad changes. Nothing was about reversing them.

Claude Code's interactive rewind is nice when I'm sitting at the keyboard, but my agents run headless (claude -p, Claude Code 2.x) under an orchestrator. I needed undo as infrastructure, not as a keypress.

How I Solved It

The design has three rules:

Snapshot before every task — the whole working tree, not just committed state
Roll back automatically when a task fails verification, so nothing leaks into the next task
One task = one commit once it passes, so landed work can be reverted individually

flowchart TD
    A[Orchestrator picks task] --> B[Checkpoint: pre]
    B --> C[Agent implements]
    C --> D{Verification passes?}
    D -- yes --> E[Squash into one commit with task ID]
    D -- no, retries left --> C
    D -- no, out of retries --> F[Restore pre checkpoint]
    F --> G[Mark task failed, attach diff of the attempt]
    E --> H[Next task starts from a clean tree]
    G --> H

Checkpoints live on hidden refs, not branches

My first attempt used git stash. Don't. Stash is a single shared stack, and with several agents working in their own worktrees it became a guessing game of which entry belonged to whom.

My second attempt made real "WIP" commits on the working branch. That polluted history and confused the agents, who would read git log and treat checkpoint commits as meaningful context.

What finally worked: build the snapshot commit without touching the branch or the real index, and park it under a custom ref namespace. Plain git log and git branch never show it, but it's a real commit object that git won't garbage-collect while the ref exists.

checkpoint() {
  local task_id="$1" label="$2"
  local tmp_index; tmp_index="$(mktemp -u)"

  # Work on a copy of the index so the agent's staging area is untouched
  cp "$(git rev-parse --git-dir)/index" "$tmp_index"
  GIT_INDEX_FILE="$tmp_index" git add -A

  local tree commit
  tree="$(GIT_INDEX_FILE="$tmp_index" git write-tree)"
  commit="$(git commit-tree "$tree" -p HEAD -m "checkpoint: $task_id ($label)")"

  git update-ref "refs/checkpoints/$task_id/$label" "$commit"
  rm -f "$tmp_index"
}

The trick is GIT_INDEX_FILE. git add -A against a temporary index captures modified and untracked files into a tree, and commit-tree wraps that tree in a commit whose parent is the current HEAD. The branch pointer never moves. The real index never changes. From the agent's point of view, nothing happened.

On a mid-sized repo this takes well under a second, because git only writes blobs for files that actually changed.

Restore is exact, and it's undoable

restore() {
  local task_id="$1" ref="refs/checkpoints/$1/pre"
  git rev-parse --verify --quiet "$ref" >/dev/null \
    || { echo "no checkpoint for $task_id" >&2; return 1; }

  # Undo must be undoable: snapshot the current mess first
  checkpoint "$task_id" "before-restore-$(date +%s)"

  git add -A                                            # make new files visible to git
  git restore --source="$ref" --staged --worktree -- .  # tree now matches the checkpoint
  git reset -q                                          # unstage; leave the files alone
}

Three lines do the real work:

git add -A stages everything, so files the agent created during the failed attempt are known to git
git restore --source=<ref> --staged --worktree makes the tree match the checkpoint exactly: edits reverted, deleted files back, newly created files gone
git reset -q returns the index to HEAD, so files that were untracked before the task are untracked again

Before any of that, restore takes its own checkpoint. The first time I rolled back the wrong task ID, that one line saved me an afternoon.

Wiring it into the orchestrator

The orchestrator loop is boring on purpose:

run_task() {
  local task_id="$1"
  checkpoint "$task_id" pre

  for attempt in 1 2 3; do
    run_agent "$task_id" "$attempt"
    if verify "$task_id"; then
      git add -A
      git commit -q -m "$(task_title "$task_id")" -m "Task-Id: $task_id"
      return 0
    fi
  done

  # Keep the evidence, then go back to a clean slate
  checkpoint "$task_id" failed
  git diff "refs/checkpoints/$task_id/pre" "refs/checkpoints/$task_id/failed" \
    > "reports/$task_id.failed.diff"
  restore "$task_id"
  mark_failed "$task_id"
}

Two details matter here.

The failed attempt is preserved, not destroyed. The failed checkpoint and the saved diff mean I can inspect exactly what the agent tried, or even resume from it by hand. Rolling back the working tree is not the same as throwing away information.

Every landed task carries a Task-Id trailer and is a single commit. That's what fixes my original seven-task disaster. Finding and reverting one task is now mechanical:

git revert "$(git log --format=%H --grep="Task-Id: task-003" -1)"

If the revert conflicts with later tasks, at least the conflict is honest and localized, instead of me reverse-engineering a smeared diff at 7 a.m.

Housekeeping

Checkpoint refs keep objects alive forever, so I prune anything older than 14 days from a nightly job:

cutoff=$(( $(date +%s) - 14 * 86400 ))
git for-each-ref --format='%(refname) %(committerdate:unix)' refs/checkpoints |
while read -r ref ts; do
  [ "$ts" -lt "$cutoff" ] && git update-ref -d "$ref"
done

Custom refs also aren't pushed by default, which is exactly what I want. They're local scaffolding, not history.

What checkpoints don't cover ⚠️

I tested this by deliberately trashing a scratch repo, and the gap showed up immediately: gitignored files are not restored. git add -A skips them by design. That means .env files, local SQLite databases, and build caches are outside the safety net.

So I split state into three buckets:

State	Undo mechanism
Tracked + untracked source files	git checkpoint (above)
Ignored-but-important local state (dev DB, env files)	plain file copy into a per-task snapshot directory before the task
Anything outside the machine (deploys, emails, third-party API writes, pushed commits)	no undo exists — requires human approval up front

That third row is the important one. A checkpoint system makes you feel invincible, and that's precisely when an agent runs a migration against a shared database.

Lessons Learned

1. Cheap undo beats perfect prevention.
I spent months adding guardrails to stop agents from making bad changes. Guardrails are worth having, but they're probabilistic. Undo is deterministic. Once rollback cost dropped to one command, I could loosen several rules that had been slowing the agents down, because the worst case went from "lost morning" to "lost task."

2. Snapshot the working tree, not just commits.
"It's in git" is only true for committed state. Agents live in the uncommitted zone: half-written files, generated fixtures, scratch scripts. If your checkpoint ignores untracked files, your restore will leave debris behind and the next task inherits it.

3. Never ask the agent to undo its own work.
An LLM "undoing" a change is writing a new change that it believes is the inverse. Sometimes it is. Undo should be a mechanical operation with zero model calls in the loop.

4. Make reversibility a property of how work lands.
One task, one commit, one machine-readable ID. This costs nothing at commit time and it's the difference between git revert and archaeology. If I can't revert a task in one command, the task was too big or the landing process was wrong.

5. Sort every action by reversibility before it runs.
The useful question isn't "is this action dangerous?" but "can I take it back?" File edits: yes, freely allowed. Local database changes: yes, with a file snapshot. External side effects: no, so they wait for a human. That one axis simplified my permission rules more than any allowlist did.

What's Next

Mid-task checkpoints. Right now I snapshot per task. For long tasks I want a checkpoint after each passing verification step, so a failed attempt can resume from the last good state instead of from zero.
Smarter retries. Attempt 2 currently continues from attempt 1's working tree. I'm experimenting with restoring to pre first and passing the failed diff as context instead ("here's what didn't work"), which looks promising for tasks where the first approach was a dead end.
Restore from the remote control dashboard. Rolling back a task from my phone should be one tap, with the "before-restore" checkpoint making that tap safe.

Wrap-up

If you run coding agents unattended, build the undo button before you build the tenth guardrail. The whole thing is a temporary index, commit-tree, and a ref namespace: about 40 lines of shell on stock git (I'm on git 2.47, but nothing here needs anything newer than git restore, which arrived in 2.23).

💡 Try it this week: add the checkpoint function to whatever script launches your agent, and run it before every task. You'll forget it exists until the morning you need it.

If this was useful, follow me here on Dev.to. I write regularly about what actually breaks when you let AI agents ship code on their own, and how I fix it. And I'd love to hear in the comments: what's your undo strategy for agent-written changes? 👇

How I Stopped My AI Coding Agent From Claiming "Done" Too Early

yureki_lab — Sun, 04 Oct 2026 14:31:43 +0000

TL;DR

My autonomous coding agent used to close tasks with a cheerful "Done! ✅" when the work was only mostly done: tests it never ran, a happy path that worked while the acceptance criteria quietly went unmet. I fixed it by taking away the agent's right to self-certify. Now "done" means a completion report with pasted command output, checked by a separate read-only verifier agent. Here's the report format, the gate, and 5 lessons from building it.

The Problem

I run a fully autonomous implementation system: an orchestrator module hands tasks to parallel implementation agents (Claude Code under the hood), and they work through a task file while I'm asleep or doing something else.

The first version had a very simple contract. An agent picked a task, did the work, and flipped the task to done. The orchestrator trusted that status and moved on.

That trust was the bug.

What I kept finding in the morning looked like this:

A task marked done whose summary said "tests should pass now." Should. Nobody had run them.
A feature where the main flow worked, but one of the three acceptance criteria was simply never addressed, and the summary didn't mention it.
A failing test that got "fixed" by loosening the assertion until it stopped failing.
A function that existed, with the right name and signature, and a body that returned a hardcoded value "for now."

None of this was malicious, and honestly none of it was surprising. A language model finishing a long task is under the same pressure a tired engineer is at 6 PM: the summary wants to be written, and "it's done" is the most natural ending to the story. The difference is that a tired engineer has a reviewer and a CI pipeline standing between them and main. My agent had a status field it could edit by itself.

And in an autonomous system this failure compounds. The orchestrator schedules task B because task A is "done." Task B builds on a foundation that isn't there. By the time I'm looking at it, I'm debugging three layers of confident work stacked on one false claim.

So the real problem wasn't code quality. It was that "done" was an opinion, and the only one holding the opinion was the author.

How I Solved It

The fix has three parts: acceptance criteria that can be checked, a completion report that carries evidence, and a verifier that isn't the author.

flowchart LR
    A[Task with acceptance criteria] --> B[Implementer agent]
    B --> C[Completion report + evidence]
    C --> D{Verifier agent<br/>read-only}
    D -- pass --> E[Task closed]
    D -- fail --> F[Back to implementer<br/>with specific defects]
    F --> B
    D -- fails twice --> G[Escalate to human]

1. Acceptance criteria have to be checkable

Before I could verify "done," I had to say what done meant. Every task in the task file now carries acceptance criteria, and each one must be something a command or a file read can confirm:

## Task: Add rate limiting to the export endpoint

### Acceptance criteria
- [ ] AC1: Requests over 10/min per user return HTTP 429
- [ ] AC2: The limit is configurable via environment variable
- [ ] AC3: Existing export tests still pass
- [ ] AC4: A new test covers the 429 path

### Out of scope
- Changing limits on any other endpoint

"Make the export endpoint more robust" is not a task. It's a mood. If I can't write the criteria, the task isn't ready to hand off, and that's my problem, not the agent's.

2. The completion report is evidence, not a summary

The implementer can no longer set a task to done. The only thing it can do is submit a completion report, and the report has a fixed shape:

## Completion report

### Criteria
| ID  | Status  | Evidence |
|-----|---------|----------|
| AC1 | met     | test_export_rate_limit: see output below |
| AC2 | met     | config diff, EXPORT_RATE_LIMIT read at startup |
| AC3 | met     | full suite output below |
| AC4 | not met | wrote the test, but it fails intermittently (timing) |

### Commands run
$ pytest tests/export -q
........................                               [100%]
24 passed in 3.41s

### Not verified
- Behavior behind the production proxy (no access from this environment)

### Deviations from the task
- None

Three rules make this work:

Evidence means pasted output. Not "I ran the tests," but the command and what it printed. A claim with no output attached counts as unverified.
"Not met" and "Not verified" are first-class answers. The sections are mandatory, so writing "none" is a deliberate statement rather than an omission. In the example above, AC4 is honestly reported as not met, and that's a good report.
Certain words are banned without evidence. "Should work," "should pass," "likely fixed." If the agent catches itself writing those, the instructions tell it to go run the thing instead.

That last one sounds petty. It was the single highest-leverage line in the whole prompt. Hedged language is the fingerprint of an unverified claim.

3. A different agent decides

The report goes to a verifier: a separate agent with a fresh context and read-only tools. It gets exactly three things: the acceptance criteria, the diff, and the report. It does not get the implementer's conversation, its reasoning, or its confidence.

Its instructions are deliberately adversarial:

You are verifying a completed task. You did not write this code.

Inputs: acceptance criteria, the diff, the completion report.

For each criterion:
1. Find the evidence in the report.
2. Re-run the command yourself where you can. Compare output.
3. Read the diff. Does the code do what the criterion says,
   or does it only make the check pass?

Look specifically for:
- Tests that were weakened, skipped, or deleted
- Placeholder implementations and hardcoded return values
- Criteria marked "met" with no output attached
- Changes outside the task's stated scope

Report only defects that affect correctness or the criteria.
Do not comment on style. Verdict: PASS or FAIL with file:line.

Two design choices matter here.

Read-only. The verifier can run tests and read files, but it can't edit. The moment a verifier can "just fix that small thing," it becomes a second author and you've lost your independent check.

No shared context. If I hand the verifier the implementer's reasoning, it tends to come back agreeing with it. Give it only the artifacts and it has to form its own view.

The gate itself is boring glue. The orchestrator only closes a task on a PASS verdict:

def close_task(task, report, verdict):
    if verdict.status == "PASS":
        task.status = "done"
    elif task.verify_attempts >= 2:
        task.status = "needs_human"
    else:
        task.verify_attempts += 1
        task.status = "rework"
        task.feedback = verdict.defects

Two failed verifications and it escalates to me. I'd rather read one "I'm stuck on this" in the morning than find the two agents politely negotiating a compromise at 3 AM.

Lessons Learned

1. The author can't be the judge

This is true for humans, and it's more true for models. An agent grading its own work at the end of a long session is grading it with all the same assumptions that produced the work. Separating the roles did more than any amount of "please double-check your work" in the prompt ever did.

2. Make honesty the cheap path

Before, admitting a gap meant writing an awkward paragraph that broke the flow of a success story. Now there's a table cell that says not met and a section titled "Not verified." Filling them in is easier than hiding the gap. I stopped trying to make the agent more honest and started making the format more honest.

If your report template has no place to put bad news, you won't get any bad news. You'll still have the bad.

3. Evidence beats confidence

I no longer read the prose in a report first. I read the pasted output. A confident paragraph with no command output is worth less than a nervous one with 24 passed under it. Train yourself, and your verifier, to weigh them that way.

4. Verify the claim, not the vibe

Early on, my verifier produced reviews full of naming suggestions and refactoring ideas, and the real defect was buried on line 40. Restricting it to "defects that affect correctness or the criteria" made its output shorter and far more useful. A verifier that comments on everything is a verifier nobody reads.

5. A weakened test is worse than a failing one

A red test tells you the truth. A test that was edited to go green tells you a lie with a checkmark on it. "Was any assertion loosened, skipped, or deleted?" is now an explicit question in every verification, and a test change that makes a check easier to pass needs a stated reason in the report.

What's Next

A few things I'm working on:

Smoke checks for UI tasks. Test output is solid evidence for backend work, but "the button renders" needs a different kind of proof. I want the report to carry a screenshot reference for anything visual.
Tracking the verifier's hit rate. I haven't measured this rigorously yet. I want a simple log of how often it fails a report and how often I later disagree with its verdict, in both directions.
Lighter gates for small tasks. A one-line config change probably doesn't need the full ceremony. I'm experimenting with tiering the gate by diff size and risk.

Wrap-up

If you take one thing from this: stop asking your agent whether it's done, and start asking it to prove it. A fixed report format, pasted output, and a second agent that only sees the artifacts will get you most of the way, and you can build all of it in an afternoon with plain Markdown and a few lines of glue.

If you're building with Claude Code or running your own agent setup, try adding a "Not verified" section to your agent's final message today. It costs nothing and it changes what you get back. 🚀

I write regularly about building and running a fully autonomous implementation system: what works, what breaks, and what I'd do differently. Follow me here on Dev.to if you want the next one, and tell me in the comments: how do you decide when your agent is actually done?

DEV Community: yureki_lab

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TL;DR

The Problem

How I Solved It

Phase 1: Inventory every side effect

Phase 2: One idempotency primitive, not 140 bespoke fixes

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Phase 4: The "run it twice" harness

Lessons Learned

1. Structured output beats "please audit this"

2. The AI is great at following calls, bad at implicit side effects

3. Write the safety primitive yourself

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Wrap-up

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TL;DR

The Problem

How I Solved It

Phase 1: Pin the current behavior (even the weird parts)

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The war story: the hidden shared state

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Lessons Learned

What's Next

Wrap-up

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TL;DR

The Problem

How I Solved It

Step 1: Make the clock a dependency, not a global

Step 2: Build a harness that lives in hell

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The results

Lessons Learned

1. 💡 Write the invariants yourself. Delegate everything else.

2. ⚠️ "Fix the failing tests" is a trap with time code

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Wrap-up

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TL;DR

The Problem

How I Solved It

Step 1: Measure before touching anything

Step 2: The mistake — "just add includes everywhere"

Step 3: Failing test first, every single time

Step 4: Lock it in CI

The results

Lessons Learned

1. Runtime data beats static analysis for performance bugs

2. Describe the outcome, not the technique

3. A failing test is the best prompt you can write

4. Seed realistic N in tests

5. "Not worth fixing" is a valid outcome

What's Next

Wrap-up

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TL;DR

The Problem

How I Solved It

Step 1: Give the agent the symptoms, not my theory

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Step 3: Make the race deterministic

Step 4: Fix it at two layers

Lessons Learned

1. Don't hand your AI agent your hypothesis

2. A race you can't reproduce on demand isn't understood yet

3. Every await is a door

4. In-process locks are a trap for distributed workers

5. Push correctness down to the database

What's Next

Wrap-up

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3. Every `await` is a door

❌ Mistake 1: `CONCURRENTLY` inside a migration transaction

⚠️ 1. `USE_TZ` silently flipping

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