DEV Community: Anil Prasad

Put a scoring gate in front of your LLM call, not a human

Anil Prasad — Fri, 14 Aug 2026 18:07:16 +0000

Here is the pattern, in the smallest form that still does the job.

from dataclasses import dataclass

@dataclass(frozen=True)
class Check:
    name: str
    score: callable          # (output, ctx) -> float in [0, 1]
    floor: float
    hard: bool = False       # hard failures never retry; they halt

def gate(generate, checks, ctx, attempts=3):
    """Return (output, trace). Never returns an output that failed a check."""
    trace, feedback = [], None
    for i in range(attempts):
        out = generate(ctx, feedback=feedback)
        scores = {c.name: c.score(out, ctx) for c in checks}
        failed = [c for c in checks if scores[c.name] < c.floor]
        trace.append({"attempt": i, "scores": scores,
                      "failed": [c.name for c in failed], "output": out})
        if not failed:
            return out, trace
        if any(c.hard for c in failed):
            raise HardFailure(failed[0].name, trace)
        feedback = render_feedback(failed, scores)
    raise Escalate(trace)

That is the whole idea. No model output reaches anything downstream until it has been scored, and a failure feeds the failure back into the next attempt rather than being logged and forgotten.

I build this in healthcare revenue cycle, where the output is an insurance
appeal and the hard check is protected health information. But nothing in the pattern is domain specific. Any time you want an LLM to do work nobody reads line by line, this is the shape.

Why bother, instead of a human in the loop

Because a human in the loop on every output is not a safety feature, it is a throughput ceiling, and usually the one you were trying to raise.

The task I care about is writing appeals against denied insurance claims. The published numbers make the case better than I can: KFF reported in July 2026 that skilled nursing denials are overturned 95 percent of the time when appealed, and appealed 18 percent of the time. Nobody skips an appeal they expect to win. They skip it because nobody is free to write it.

Put a person on every output and you have moved the work from writing to
reviewing. Faster, but the same ceiling, plus a licence fee. So the engineering question becomes: what has to be true for the output to be
safe to send unread?

Design note one: hard checks are not just checks with a high floor

This is the part people get wrong on the first pass, and I did too.

Most checks are quality checks. Fail one and retrying is correct, because the model can often fix it given the failure as context.

Some checks are not like that. In my domain, PHI leakage is one. If the output contains protected data it should not, retrying is exactly the wrong move: you have already produced the thing, and the correct response is to halt, raise an incident, and page a human. Retrying a safety failure is how you turn one incident into three.

CHECKS = [
    Check("groundedness", score_groundedness, 0.90),
    Check("accuracy",     score_accuracy,     0.95),
    Check("variance",     score_variance,     0.88),
    Check("phi_safety",   score_phi,          1.00, hard=True),
]

Note the floor on the hard check is 1.00, not 0.99. There is no partial credit available on that dimension, and a floor of 0.99 is an admission that you expect to leak occasionally.

Design note two: the feedback is the retry

A retry that sends the same prompt again measures your temperature setting. A retry has to carry what failed.

def render_feedback(failed, scores):
    lines = ["Your previous answer did not pass validation. Fix these and "
             "return the corrected answer only."]
    for c in failed:
        lines.append(f"- {c.name}: scored {scores[c.name]:.2f}, "
                     f"needs at least {c.floor:.2f}. {HINTS[c.name]}")
    return "\n".join(lines)

HINTS = {
    "groundedness": "Every factual claim must appear in the provided source "
                    "documents. Remove anything you cannot point to.",
    "accuracy":     "Codes and identifiers must validate against the supplied "
                    "reference set. Do not invent plausible ones.",
    "variance":     "Answer at the level of specificity the source supports, "
                    "no more.",
}

The hints matter more than the scores. A model told "groundedness 0.71" does nothing useful. A model told "remove any claim you cannot point to in the source" usually fixes it in one pass.

In my system this loop corrects 87.2 percent of catchable issues without a person, in about 4.2 seconds and roughly 1,800 extra tokens per correction, at about 0.00054 dollars. That is measured on the runtime standalone rather than in a customer environment, and I flag that because a number without its measurement context is not a number.

Design note three: groundedness is the check that earns its keep

If you implement only one, implement this one. It is also the one people
implement worst, usually as an embedding similarity between output and context, which is close to useless because a fluent paraphrase of something false scores well.

Decompose instead.

def score_groundedness(out, ctx) -> float:
    claims = extract_claims(out)                 # atomic factual assertions
    if not claims:
        return 1.0
    supported = sum(is_supported(c, ctx.sources) for c in claims)
    return supported / len(claims)

extract_claims is a cheap model call with a strict output schema. is_supported is another, per claim, asked as a yes-or-no with the relevant source span attached. It is more expensive than cosine similarity and it is the difference between a check and a decoration.

Two implementation notes that cost me time. Ask the support question in isolation per claim, because a model shown ten claims at once will pattern-match to "mostly fine." And log the unsupported claims, not just the ratio, because that list is the actual product of the check.

![ ](https://dev-to-uploads.s3.us-east-

2.amazonaws.com/uploads/articles/ff4lwfj0qiie75wy1swa.png)

Design note four: the trace is the point

The trace returned above looks like debugging output. It is the most valuable thing the whole pattern produces.

Persist it. Every attempt, with the before and after text, every dimension
score, whether a correction was applied, how many attempts it took, whether it escalated, which model ran, the latency and the cost.

def persist(trace, ctx):
    for row in trace:
        write_append_only({
            "request_id":       ctx.request_id,
            "attempt":          row["attempt"],
            "scores":           row["scores"],
            "failed":           row["failed"],
            "output_before":    row["output"] if row["failed"] else None,
            "output_after":     None if row["failed"] else row["output"],
            "model":            ctx.model,
            "latency_ms":       ctx.latency_ms,
            "cost_usd":         ctx.cost_usd,
        })

Append-only, and keep it as long as your regulator asks. Mine asks for seven years.

Two reasons this earns its storage. Operationally, failures cluster, and the cluster tells you what to fix long before an aggregate pass rate moves.

And this table is where your override rate lives: how often a human disagreed with the system, on what, and what happened next. In a regulated domain that is the only evidence that human review was real rather than a signature. A March 2026 discovery order in a US coverage denial case compelled production of internal AI review board materials. You cannot reconstruct that log retroactively.

What this does not do

It does not make the output correct. It makes it verifiable against what you supplied, which is a weaker and much more achievable property. If your source documents are wrong, a perfectly grounded output is confidently wrong.

It costs latency and tokens on every call that needs a retry. If your workload is latency-critical this trade may not be available. Mine is not: an appeal that takes four extra seconds is still days faster than the queue it came from.

And retry count is a parameter, not a principle. Three is where the marginal correction rate stopped justifying the latency for us. Measure yours rather than inheriting mine.

The one line I would take away

The interesting cost in an LLM system is not inference. It is verification, and whether you pay it in software or in people.

If you pay it in people, the system does not scale past their hours, which is usually the exact constraint you bought it to relieve.

The healthcare platform this pattern runs in is ARIA, which my team builds at Ambharii Labs. Performance figures above are internal, measured on the runtime standalone or on our own 197-case evaluation suite, and labelled as such. We have no independent benchmark, which is the honest gap.

Sources: KFF, 6 July 2026. Lokken v. UnitedHealth discovery order, 9 March 2026.

If you run a gate like this, I would like to know what your hard-check list contains. That list is a very direct statement of what an organization thinks is unrecoverable, and I have never seen two that match.

Measure pass-all-k, not accuracy: a reliability harness in 60 lines

Anil Prasad — Fri, 07 Aug 2026 18:44:17 +0000

Run this against whatever you already have.

`from collections import Counter

def pass_all_k(run, tasks, k=8):
"""run(task, variant) -> bool. Returns (pass_all_rate, mean_rate)."""
all_pass, total = 0, 0
for t in tasks:
results = [run(t, variant=i) for i in range(k)]
all_pass += all(results)
total += sum(results)
return all_pass / len(tasks), total / (len(tasks) * k)`

Two numbers come back. The second is what your dashboard shows. The first is what a customer experiences, because customers do not get to retry until it works.

They are rarely close. On the systems I have measured, a mean around 0.85 has sat with a pass-all-8 around 0.45, and I have never once seen the gap go the other way.

Why the gap exists

If failures were independent at rate p, pass-all-k would be (1-p)^k and you could compute it rather than measure it. Failures are not independent, which is the entire point. They cluster by task shape.

That clustering is the actionable part. A 2026 reliability study running 23,392 episodes across ten models and a 396-task benchmark found that degradation was domain specific rather than model specific: a graceful degradation score fell from 0.90 to 0.44 in software engineering as task length grew, while document processing barely moved, 0.74 to 0.71.

So the interesting output of your harness is not the number. It is which tasks are in the failing set.

The variant function is the whole design

This is where most homegrown harnesses go wrong. Eight identical calls measure your cache. You need eight honest variations of the same intent.
`import random

REPHRASE = [
lambda s: s,
lambda s: s.lower(),
lambda s: f"I need to {s[0].lower()}{s[1:]}",
lambda s: f"{s} Please be thorough.",
lambda s: s.replace("?", "").strip() + ", if you can.",
]

def make_variant(task: dict, variant: int) -> dict:
rng = random.Random(f"{task['id']}:{variant}") # deterministic
out = dict(task)
out["prompt"] = rng.choice(REPHRASE)(task["prompt"])
if task.get("states"):
out["state"] = rng.choice(task["states"])
return out`
Seed on task_id:variant rather than on a global counter. Then a re-run of task 7, variant 3 is the same input it was last week, which is the difference between a harness you can compare across releases and one you cannot.

Record per-task, not per-run
The failure mode of a reliability harness is aggregating too early.
`import json, pathlib

def evaluate(run, tasks, k=8, out="reliability.jsonl"):
fh = pathlib.Path(out).open("w")
summary = Counter()
for t in tasks:
results = []
for i in range(k):
v = make_variant(t, i)
try:
ok = bool(run(v))
err = None
except Exception as e: # a crash is a failure
ok, err = False, f"{type(e).name}: {e}"
results.append({"variant": i, "ok": ok, "error": err})
rec = {
"task_id": t["id"],
"shape": t.get("shape", "unclassified"),
"k": k,
"n_pass": sum(r["ok"] for r in results),
"pass_all": all(r["ok"] for r in results),
"runs": results,
}
fh.write(json.dumps(rec) + "\n")
summary[t.get("shape", "unclassified")] += rec["pass_all"]
fh.close()
return summary`

shape is the field that earns its keep. Tag each task with what it is rather than which model ran it: lookup, multi_step, writes_state, long_horizon, needs_tool. Group the failures by shape and the pattern usually falls out on the first run.

An exception counts as a failure. A harness that only counts wrong answers and lets timeouts through will tell you a comforting lie.

What to do with the failing set**
**
Three findings from 2026 tell you where to look first, and each is a check you can run rather than a claim you have to believe.

If a failing shape is multi-agent, test the single-agent version. A study across 180 controlled configurations found that once single-agent accuracy passes roughly 45 percent on a task, adding agents produced negative returns, and that independent agents amplified errors 17.2 times against a single-agent baseline while centralised coordination held it to 4.4 times. Read-heavy work parallelises. Write-heavy work does not, because two agents writing produce two decisions nobody reconciles.

If a failing shape is long-horizon, do not assume a better model fixes it. Same reliability study: capability and reliability rankings diverged, and advanced models showed meltdown rates up to 19 percent, apparently because they attempt harder multi-step strategies.

Before you raise reasoning effort, measure it. Across 21,730 rollouts, higher reasoning effort produced equal or lower accuracy in 21 of 36 model and benchmark combinations. It is a per-task tuning parameter with a real downside, not a quality dial. Three effort levels against the same seed set is an afternoon.

for effort in ("low", "medium", "high"): pa, mean = pass_all_k(lambda t: run(t, effort=effort), tasks, k=8) print(f"{effort:<7} pass_all={pa:.2f} mean={mean:.2f}")

Watch for the case where mean rises and pass_all falls. That is a system getting better on average and less dependable, and it is invisible if you only track one of them.

Wiring it into CI

Keep it cheap or it will be deleted within a month.
- name: reliability run: | python -m harness --k 8 --tasks tasks/core.jsonl --out reliability.jsonl python -m harness.gate --min-pass-all 0.60 --baseline main.jsonl

Two rules that have kept this alive on teams I have worked with. Gate on **regression **against the previous run, not on an absolute threshold, because an absolute number gets lowered the first time it blocks a release. And run the full k nightly while running k=3 on pull requests, because a twenty-minute pre-merge check gets disabled.

The honest limits

k=8 is arbitrary. It is enough that luck stops carrying you and few enough that people will actually run it. Use five if five is what gets done.

The variant function encodes your assumptions about what "the same request" means, and reasonable people will disagree about it. That is a feature: it forces the argument to happen in code review rather than after an incident.

And this measures reliability, not correctness. A task that fails all eight times consistently is perfectly reliable and completely wrong. You still need the assertions.

None of this is new thinking. Anyone who has run a payments system or a database already reasons about the worst request rather than the average one. We stopped doing it when the systems started sounding confident.
Sources: arXiv 2603.29231 (31 March 2026, 23,392 episodes); arXiv 2512.08296 (December 2025, 180 configurations); arXiv 2510.11977 (ICLR 2026, 21,730 rollouts).

If you already measure something like this, I would like to know what your variant function does. That part has no established convention yet and I suspect everyone has quietly invented their own.

I Have 25+ Years of Production Engineering Experience. Here Is What AI Coding Tools Actually Did to My Workflow.

Anil Prasad — Sat, 01 Aug 2026 13:31:05 +0000

I want to be upfront about something before you keep reading.

I did not write this to sell you a tool. I did not write it because a vendor sent me a license. I wrote it because I have been shipping production systems for 28 years — at Fintech, Healthcare, Lifesciences, MedTech, EnergyTech, InsuranceTech — and the conversation I keep having with engineers on my team is one that nobody is writing about honestly.

The conversation goes like this. Someone asks me which AI coding tool they should use. I start to answer and they stop me and say no, I do not mean which one is fastest or which one scored highest on some benchmark. I mean: does this actually change how you work? Does it make you better? Or does it just make you faster at the same things?

That is the question I am going to answer here. From production. From regulated environments. From real code that runs on real infrastructure that real people depend on.

The context matters, so let me give it to you.

I run engineering at an Energy Tech firm, a regulated utility. The code I ship runs power generation systems. I also run Ambharii Labs, where I build open source AI tooling — AgentMesh, ARGUS, Bulwark, TorchForge. I use Claude Code daily. I have Cursor installed. I used GitHub Copilot for 18 months before that.

I am not a neutral observer. But I am also not a reviewer who ran three tools on a todo app for a week and wrote it up. Everything I am about to tell you is grounded in production code, team dynamics, and the kind of technical debt that accumulates over years in a real engineering organization.

What actually changed when I started using these tools.

The first thing that changed was not my output speed. It was what I chose to spend time on.

Before AI coding tools, a significant portion of my day went to what I would call translation work. Taking a requirement and translating it into scaffolding. Writing the boilerplate that connects the idea to the actual logic. Setting up the test harness before I could write a single meaningful test. This work is not hard. It is just slow, and it consumes the same cognitive budget as the work that actually requires judgment.

Claude Code changed that specific problem for me more than any other tool. Not because it is smarter in every situation. Because it operates at the level of the whole task rather than the current line. I can describe what I am building in plain language, and it produces scaffolding that is close enough to useful that my actual work starts at a higher level than it did before. At Energy Tech Enterprise, I used it to rebuild a data pipeline integration in a weekend that would have taken two engineers a sprint. The resulting code needed review and adjustment. But the review took a fraction of the time the original construction would have.

What did not change.

This is the part that most reviews skip, so I am going to spend more time on it.

Judgment did not change. Specifically, the judgment required to know when the AI is wrong in ways that will not surface until production. Claude Code generated a Redis failover pattern for me last quarter that worked perfectly in testing and would have silently corrupted data under a specific race condition at scale. I caught it because I have seen that failure mode before. Not because any test flagged it. Not because the AI warned me. Because 28 years of watching distributed systems fail gives you pattern recognition that no tool has yet.

Architecture did not change. Every significant architectural decision I have made in the past year — how to structure the ARGUS observability layer, how to design the multi-tenant isolation in Aether AI, how to partition the compliance framework in Bulwark — was made by me, not with the AI. The AI is genuinely useful for implementation. It is not useful for deciding what to build or how the pieces should relate to each other. That distinction matters more than most people are willing to say.

Debugging production failures did not change. When something breaks in a regulated environment at 2am, the skill that matters is the ability to form a hypothesis from incomplete information, trace causality through a system you did not fully build, and make a decision under pressure. I have not seen any AI tool improve this skill. I have seen it make engineers slower at it because they reach for the tool first instead of thinking.

What the data says vs what I see on my team.

The research on AI coding tools in 2026 is genuinely contradictory.

Stanford HAI found that early-career developers aged 22 to 25 in AI-exposed roles experienced a 20% employment decline from their 2022 peak. Junior developer job postings dropped 60% from 2022 to 2024. Coding bootcamp enrollment is down 40%.

At the same time, overall software engineering postings are up 11% year over year in early 2026. The Bureau of Labor Statistics still projects 17% job growth through 2033. AI engineer roles grew 300% in the period that junior developer roles declined.

What does this look like from inside an engineering organization? I manage a team. I have watched this play out in real hiring decisions, not statistics.

We stopped backfilling junior roles the same way we used to. Not because we decided to. Because the calculus changed quietly. A senior engineer with good AI tool fluency now covers ground that previously required a junior and a senior working together. That is not a policy decision. It is an economic one that happens below the level of any announcement.

The consequence nobody is talking about loudly enough is the pipeline. Senior engineers come from somewhere. They come from the junior roles that are not being filled. AWS CEO Matt Garman called eliminating the junior layer "one of the dumbest things I have ever heard" and warned about a catastrophic skills gap in the next decade. He is right. But the decisions that create that gap are being made one hiring freeze at a time in teams exactly like mine, with no malice and complete economic rationality.

The honest answer to the question my engineers keep asking.

Will AI tools make you irreplaceable? No. Nothing makes you irreplaceable.

Will they make you more productive? Yes, at specific things. Scaffolding, boilerplate, documentation, first-pass testing, refactoring known patterns. The productivity gain is real and it is not small.

Will they replace you? That depends on what you do. If your value is executing tasks that have known shapes — write this endpoint, create this migration, document this function — you are at genuine risk. Not because an AI does those things better than you. Because one engineer who understands how to direct an AI does them faster than you, and the economic case for your role erodes.

If your value is understanding why a system behaves the way it does, how failure modes propagate, what the right architecture is for a constraint set that nobody fully articulated — that value has not eroded. It has increased, because the gap between people who have it and people who do not is now more visible than it was before.

What I actually use and why.

Claude Code for anything involving real codebase understanding, multi-file changes, or system-level reasoning. The context window and the ability to describe intent rather than mechanics is worth it for complex work.

Cursor for daily-driver editing when I am in flow and want inline assistance without switching contexts.

Neither for architecture decisions, production debugging, or anything where the cost of a subtly wrong answer is high.

AgentMesh sitting in front of both, because when you are working in a regulated environment you need a governance layer that tracks what the AI was asked, what it produced, and what it cost. That is not optional when the code runs infrastructure.

The question nobody asks but should.

Most conversations about AI coding tools focus on the individual developer. Should I use this tool? Will it make me faster?

The question I think matters more is what these tools do to the craft of software engineering over a decade. The engineers who are senior today learned by doing work that AI tools now do for junior developers. They debugged code they wrote poorly. They read error messages they did not understand. They built systems that failed in ways they had to diagnose. That experience is how you build the pattern recognition that catches the race condition in the Redis failover at 2am.

If the entry-level layer thins significantly — and it is thinning, the data is clear — the engineers who are senior in 2036 will have been formed differently. Whether that produces better or worse engineers at the senior level is a question nobody knows the answer to yet. Including me.

What I know is that the tools are real, the productivity gains are real, and the consequences are playing out in ways that are more complicated than either the panic or the optimism suggests.

I have 28 years of data points. This is the most uncertain moment I have seen. That is not a warning. It is just what honest looks like from here.

Anil S. Prasad is founder of Ambharii Labs and Head of Engineering and Product at Fortune 100 Energy Tech and serves on Tech and engineering Advisory roles for different enterprises, private equity frms and VC backed firms. He builds open source AI governance tools at github.com/anilatambharii — including AgentMesh (governance proxy for AI tools), Bulwark (agent security framework), and TorchForge (enterprise PyTorch governance). He writes about what actually breaks in regulated AI deployments.

Your model didn't get smarter. It learned to cheat the test.

Anil Prasad — Sun, 26 Jul 2026 13:02:04 +0000

Reward hacking, eval contamination, and irreproducible runs are the three invisible failures in modern LLM training. Here is an open-source trust layer that catches all three.

Modern fine-tuning frameworks are fast. verl, TRL, and Unsloth can saturate a GPU cluster and push tokens per second most of us could not have imagined three years ago.

But speed created a blind spot. Faster training did not make good models easier to produce. It made bad models cheaper to produce. And three failure modes crept into that blind spot, all sharing one dangerous property: they are invisible on a normal dashboard.

This post is about those three failures and an open-source project, Provenir, that catches all of them. Everything here runs with pip install provenir.

Failure 1: reward hacking

You train against a reward signal. A verifier decides if each answer earns the reward. The model maximizes reward by any means available, including means you did not intend.

The core problem: a verifier checks the answer, not the reasoning. So the model learns to pass the check without doing the thinking. Recent RLVR research documented models abandoning real reasoning on inductive tasks, producing outputs that satisfied the verifier while skipping the pattern the task required. Reinforcement learning amplifies whatever maximizes reward, so the longer you train, the worse it gets.

There are seven common modes:

Every one shows up on your dashboard as a reward curve going up.

Provenir's flight recorder and reward-hacking detector flag them per step, live:

from provenir.observability import FlightRecorder, RewardHackingDetector

recorder = FlightRecorder()
detector = RewardHackingDetector()

for step, metrics in rl_loop():
    recorder.log_step(metrics)     # KL, entropy, reward std, advantages...

for anomaly in recorder.anomalies():
    print(anomaly.kind, anomaly.step, anomaly.detail)

report = detector.analyze(rollouts)
if report.is_hacking:
    print("Reward hacking detected:", report.findings)

Failure 2: evaluation contamination

Your benchmark says 92%. If the eval set leaked into training, that number measures memory, not capability.

[IMAGE: provenir-article-fig4-compare.png]

This happens constantly. Training corpora are huge and scraped widely; benchmark questions end up inside them, sometimes verbatim, sometimes paraphrased just enough to dodge a naive string match. The fix is to check overlap the way contamination actually happens:

from provenir.eval.contamination import ContaminationChecker

checker = ContaminationChecker()   # 13-gram + embedding + exact, MinHash at scale
report  = checker.check(train_dataset, eval_dataset)
print(f"Overlap: {report.overlap_ratio:.1%} across {report.n_hits} records")

For a stronger guarantee, plant canary tokens in a private eval vault. If those tokens ever appear during training, you know your held-out set leaked. It turns "we think the eval is clean" into "we can prove it."

Failure 3: irreproducibility

A run produces a great model. Two weeks later you cannot reproduce it. Different seed, a dependency moved, the dataset shifted. You have a good model and no idea how you made it.

[IMAGE: provenir-article-fig3-passport.png]

Every Provenir run produces a content-addressed manifest: config hash, dataset hash, git SHA, seed, and a lineage DAG linking dataset → run → adapter → eval → merge.

provenir train config.yaml --dataset data/train.jsonl
# produces a tamper-evident manifest

provenir reproduce manifest_abc123.json --output reproduced_run/
# reproduces the exact run

On top sits a signed Model Passport, a portable bill of materials mapping directly to EU AI Act Article 12 (tamper-proof audit trails + model lineage, enforced August 2, 2026):

from provenir.governance.passport import ModelPassport

passport = ModelPassport.build(run.manifest, key="team-signing-key")
passport.save("passport.json")   # signed HMAC-SHA256 bill of materials

loaded = ModelPassport.load("passport.json")
assert loaded.verify(key="team-signing-key")
print(loaded.risk_flags)  # ["unscanned_pii", "contaminated_eval", "unknown_license"]

Where Provenir sits

Provenir does not reimplement kernels. It orchestrates verl, TRL, and Unsloth through backend-agnostic adapters and adds the trust layer on top. The whole thing drops into an existing loop in three lines:

import provenir

with provenir.track("my-run", dataset=train_ds) as run:
    for step, metrics in training_loop():
        run.log_step(metrics)
    run.record_eval("mmlu", score=0.71)

# run.manifest, run.flight_recorder, run.hacking_report, run.passport

Try it

pip install provenir            # manifests, eval, governance, CLI
pip install "provenir[train]"   # SFT + DPO + LoRA/QLoRA via TRL
pip install "provenir[all]"     # everything

Apache 2.0. 1,153 tests. Repo and docs: github.com/anilatambharii/provenir.

Speed without trust just means you reach the wrong answer faster and with more confidence. If you work on RL or fine-tuning infra, I would genuinely like to hear which of these three failures has cost you the most. Drop it in the comments.

HumanWritten #ExpertiseFromField

7 New Governance Features Just Shipped in AgentMesh. All Open Source, No Waitlist. published: false

Anil Prasad — Tue, 21 Jul 2026 14:00:00 +0000

The permission model is the problem, not the model
Every AI agent incident this year has the same shape, honestly. An agent gives confidently wrong advice and a human trusts it. A trading agent moves money because nothing was in place to stop it. A shared API key means nobody can even tell which agent did what, let alone revoke just one of them.

The agent isn't malfunctioning. It's doing exactly what it was allowed to do.

AgentMesh is the open source governance proxy I built to sit between your agents and everything they touch. Token budgets, semantic caching, PII and PHI and PCI masking, prompt injection detection, audit trails, all of it enforced before a call ever reaches an LLM or a tool. It's been out in the open for a few weeks now, and this last sprint added seven features that push it past "cost and safety proxy" into something that actually answers the two questions every enterprise eventually asks: who did that, and can we stop it.

Here's what's new, with the real commands, not screenshots.

OpenTelemetry export Every governance event AgentMesh emits (cache hits, quota blocks, injection detections, anomalies) now streams live as OTLP spans and metrics to whatever collector you're already running. Datadog, Honeycomb, Grafana Tempo, doesn't matter which. This isn't a batch export you have to remember to trigger either. It subscribes to the live event bus and just keeps exporting for the life of the process.

agentmesh serve --otel-endpoint http://localhost:4317
No new dashboard to learn. Your platform team's existing tooling starts seeing AI governance events show up alongside everything else they already watch.

EU AI Act readiness scanner Full enforcement of the EU AI Act's high risk obligations lands August 2, 2026. Most teams have a compliance slide deck by now. Almost none have the actual controls, and the penalty structure isn't even uniform, which trips people up constantly. Article 5 violations go up to €35M or 7% of global turnover, but the articles that actually apply to most high risk systems, that's 12, 14, 15, and 17, top out lower, at €15M or 3%. Get those two numbers confused in public and a compliance lawyer will happily correct you. So the scanner reports whichever tier actually applies, article by article, instead of always reaching for the scariest headline number.

agentmesh compliance readiness --policy your-policy.yaml
It checks four things. Article 12: is your audit trail actually tamper evident and chain verifiable, or just a database table with delusions of grandeur. Article 14: can a human actually halt the system, or do they just get an email about it afterward. Article 15: do you have real robustness against prompt injection and data exfiltration. Article 17: is your policy versioned and evidence backed, or does it live in someone's head.

Every gap it finds comes with the specific config line that fixes it. Not just a red X.

Human in the loop approval An agent that can move money, delete data, or take some irreversible action without a human checking first isn't a rogue agent. It's a correctly functioning agent with a genuinely bad job description. The approval gateway lets you flag specific tools, cost thresholds, or teams as needing a real decision before the call goes through.

agentmesh serve --require-approval-over-usd 5.00 --approval-tools "wire_transfer*,delete_*"
It doesn't sit there blocking a request thread waiting on a human, which would be a bad way to build this. A matched call gets parked as PENDING, an alert fires off to Slack or PagerDuty, and the caller gets back an HTTP 202 with an approval ID. Once a human resolves it, the caller resubmits with the header X-AgentMesh-Approval-Id and it goes through. If nobody responds in time, it fails closed. No response means no action, not the other way around, and that distinction matters more than it sounds like it should.

agentmesh approval list
agentmesh approval approve APR-a1b2c3d4 --by security-team

Per agent virtual keys Most enterprises run every single AI agent off one shared vendor API key. No way to tell which agent did what. No way to revoke one compromised agent without breaking every other one at the same time. Virtual keys fix this right at the proxy layer. Each agent gets its own amk_live_... key, scoped and revocable on its own, and the real vendor key never leaves the proxy. The agent never even sees it.

agentmesh keys create nightly-triage-bot --team engineering --scopes "claude-code,cursor"
agentmesh keys revoke vk_a1b2c3d4 --reason "rotated"
Keys are stored hashed with SHA-256, same idea as password storage. Lose one and you revoke and reissue. You don't get it back, and that's on purpose.

MCP governance wrapper LLM call governance alone has a real blind spot. By the time an MCP tool call actually happens, the LLM call that requested it has already been governed and already returned. A database read with no row level scope, or an agent delegating to another agent and just handing over its entire permission set, none of that ever passes through LLM call governance. It only shows up at the tool boundary, if it shows up at all. agentmesh wrap puts the same PII scanning, injection detection, scope enforcement, and approval gating in front of any MCP stdio server.

agentmesh wrap --agent-id triage-bot --pii-mode mask --approval-tools "wire_transfer*,delete_*" -- python my_mcp_server.py
A blocked or pending call never reaches the wrapped server at all. It gets answered with a JSON-RPC error directly. Everything else, tool listing, resources, prompts, passes through untouched.

Compliance policy packs AgentMesh already shipped pre built governance policies for the regulatory shapes people actually run into. HIPAA clinical, EU AI Act high risk, fintech and SOX, an enterprise baseline. They were just sitting in the repo with no real way to find or install them, which was kind of a waste. Now it's one command.

agentmesh policy list-packs
agentmesh policy install eu_ai_act_high_risk
The eu_ai_act_high_risk pack routes anything matching score_, screen_, deny_, or reject_ through human approval automatically. Article 14 compliance as a default, not something you have to remember to configure yourself. The fintech pack does the same thing for wire_transfer* and send_payment*.

A demo that's actually real

agentmesh demo
No API keys, no network calls, and definitely no scripted animation pretending to be a product. This runs the actual PIIScanner, InjectionDetector, BudgetEnforcer, AuditTrail, and ComplianceReporter classes live. A prompt gets PII masked right in front of you. An injection attempt gets blocked and it tells you which rule caught it. A simulated runaway loop hits a hard budget cap and actually stops. And it closes out by generating a real signed audit trail plus an EU AI Act readiness report. If you want to know whether any of this is real before digging through source code, this is the fastest way to find out.

The Chrome extension got faster too
Not a new feature exactly, but worth mentioning if you've used the extension before. Every prompt typed into Claude.ai, ChatGPT, or Gemini used to pause for a governance round trip and then make you click a second time to actually send it. That's fixed now. The real send fires immediately, and governance feedback like cache hits or quota warnings shows up afterward as a small toast that never blocks anything, and only when there's actually something worth knowing.

Try it

pip install agentmesh-proxy
agentmesh demo
All of this is open source, Apache 2.0, on GitHub right now.

github.com/anilatambharii/agentmesh

If any of this saves your team a compliance headache or an actual incident, a star helps the next person find it. PRs welcome too, especially on policy packs for verticals we haven't covered yet.

Anil Prasad builds AI that survives contact with the real world. Co-founder of GenomicsIQ (World Economic Forum cohort), builder of Aria RCM, an eleven agent healthcare platform running in production, and a BCG Aleph alum. AgentMesh is the governance layer he wished existed before he had to go build eleven agents inside a regulated business himself.

If you run AI tools across a team and your bill is outgrowing your usage, clone it, run agentmesh demo, and tell me where it breaks. What would you build on top of this?

Originally published in my newsletter, Field Notes: Production AI. Find me at anilsprasad.com or on X at @anilsprasad.

Building Production-Safe AI Agents in 2026: A Governance-First Architecture

Anil Prasad — Fri, 10 Jul 2026 12:59:00 +0000

TL;DR: The framework is 20% of the work. This post shows how to wrap
LangGraph or CrewAI with out-of-process governance that passes
enterprise security audits. Working code included.

I've spent 28 years building production AI systems. The pattern I'm watching repeat in 2026 is familiar: we get excited about capability, underinvest in control, and pay for it later.

This time the stakes are higher. 88% of enterprises running AI agents have already had a security incident. When the filters fail, there's no human reviewing the output—just an autonomous agent executing commands at machine speed.

Here's how to build agents that actually work in production.

The Problem with "Secure" Prompts

Most tutorials show you something like this:

system_prompt = """
You are a helpful assistant.
IMPORTANT: Never delete files or access sensitive data.
Always ask for confirmation before taking destructive actions.
"""

agent = create_agent(llm, tools, system_prompt)

This feels secure. It is not.

Why this fails:

In-process prompts are advisory. The model can be manipulated to ignore them.
Invisible to audit. No external system knows what rules were "set."
Overridable by context. Tool outputs can inject instructions that override the system prompt.
No enforcement mechanism. Nothing actually prevents the delete_file tool from executing.

In February 2026, researchers found 7 CVEs in OpenClaw. The root cause was architectural: no certified security baseline, no mandatory audit trail, no access control enforcement by default.

The Governance-First Architecture

[INSERT IMAGE: devto_architecture.png]

Here's the architecture that passes enterprise security audits:

Layer 1: Agent Runtime (Your Framework)

Use whatever framework fits your use case:

from langgraph.graph import StateGraph
from crewai import Agent, Crew

# Your orchestration logic lives here
# This is the 20% of the work

Layer 2: Policy Engine (Out-of-Process)

Every tool call goes through an external policy engine BEFORE execution:

# policy_engine.py
from typing import Callable
import json

class PolicyEngine:
    def __init__(self, policy_file: str):
        with open(policy_file) as f:
            self.policies = json.load(f)

    def evaluate(self, agent_id: str, tool_name: str, args: dict) -> bool:
        """
        Returns True if action is allowed, False otherwise.
        This runs OUT OF PROCESS from the agent.
        """
        # Check agent permissions
        agent_policy = self.policies.get(agent_id, {})
        allowed_tools = agent_policy.get("allowed_tools", [])

        if tool_name not in allowed_tools:
            self.log_denied(agent_id, tool_name, args)
            return False

        # Check data classification
        if self._contains_pii(args):
            if not agent_policy.get("pii_access", False):
                self.log_denied(agent_id, tool_name, args, "PII access denied")
                return False

        self.log_allowed(agent_id, tool_name, args)
        return True

Policy file (policies.json):

{
  "agent_researcher": {
    "allowed_tools": ["web_search", "read_file"],
    "pii_access": false,
    "max_cost_per_call": 0.10
  },
  "agent_writer": {
    "allowed_tools": ["read_file", "write_file"],
    "pii_access": false,
    "write_paths": ["/tmp/drafts/*"]
  },
  "agent_admin": {
    "allowed_tools": ["*"],
    "pii_access": true,
    "requires_human_approval": ["delete_*", "send_email"]
  }
}

Layer 3: Tool Wrapper with Policy Enforcement

Wrap every tool to enforce policy before execution:

# secure_tools.py
from functools import wraps
from policy_engine import PolicyEngine
from identity import get_current_agent_id
from audit import AuditLogger

policy = PolicyEngine("policies.json")
audit = AuditLogger()

def secure_tool(func: Callable) -> Callable:
    """
    Decorator that enforces policy before tool execution.
    """
    @wraps(func)
    def wrapper(*args, **kwargs):
        agent_id = get_current_agent_id()
        tool_name = func.__name__

        # Log the attempt
        trace_id = audit.start_trace(agent_id, tool_name, kwargs)

        # Evaluate policy BEFORE execution
        if not policy.evaluate(agent_id, tool_name, kwargs):
            audit.log_denied(trace_id)
            raise PolicyViolationError(
                f"Agent {agent_id} not authorized for {tool_name}"
            )

        try:
            result = func(*args, **kwargs)
            audit.log_success(trace_id, result)
            return result
        except Exception as e:
            audit.log_error(trace_id, e)
            raise

    return wrapper

# Apply to all tools
@secure_tool
def read_file(path: str) -> str:
    with open(path) as f:
        return f.read()

@secure_tool
def write_file(path: str, content: str) -> None:
    with open(path, 'w') as f:
        f.write(content)

@secure_tool
def delete_file(path: str) -> None:
    os.remove(path)

Layer 4: Per-Agent Identity

No shared API keys. Every agent gets a cryptographic identity:

# identity.py
import jwt
from datetime import datetime, timedelta
from contextvars import ContextVar

_current_agent: ContextVar[str] = ContextVar('current_agent')

def create_agent_token(agent_id: str, permissions: list) -> str:
    """
    Create a signed JWT for an agent instance.
    """
    payload = {
        "agent_id": agent_id,
        "permissions": permissions,
        "iat": datetime.utcnow(),
        "exp": datetime.utcnow() + timedelta(hours=1)
    }
    return jwt.encode(payload, SECRET_KEY, algorithm="HS256")

def authenticate_agent(token: str) -> str:
    """
    Verify agent token and set context.
    """
    payload = jwt.decode(token, SECRET_KEY, algorithms=["HS256"])
    agent_id = payload["agent_id"]
    _current_agent.set(agent_id)
    return agent_id

def get_current_agent_id() -> str:
    return _current_agent.get()

Layer 5: OpenTelemetry Observability

Full tracing for every agent action:

# observability.py
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter

# Initialize tracer
provider = TracerProvider()
exporter = OTLPSpanExporter(endpoint="http://otel-collector:4317")
provider.add_span_processor(BatchSpanProcessor(exporter))
trace.set_tracer_provider(provider)

tracer = trace.get_tracer("agentmesh")

def trace_agent_action(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        with tracer.start_as_current_span(func.__name__) as span:
            span.set_attribute("agent.id", get_current_agent_id())
            span.set_attribute("tool.name", func.__name__)
            span.set_attribute("tool.args", str(kwargs))

            try:
                result = func(*args, **kwargs)
                span.set_attribute("tool.success", True)
                return result
            except Exception as e:
                span.set_attribute("tool.success", False)
                span.set_attribute("tool.error", str(e))
                raise
    return wrapper

Putting It Together

Here's the full integration with LangGraph:

# main.py
from langgraph.graph import StateGraph, END
from secure_tools import read_file, write_file, secure_tool
from identity import create_agent_token, authenticate_agent
from observability import trace_agent_action

# Create agent with identity
researcher_token = create_agent_token(
    "agent_researcher", 
    permissions=["web_search", "read_file"]
)

# Authenticate before running
authenticate_agent(researcher_token)

# Define your graph with secure tools
def research_node(state):
    # All tool calls go through policy engine
    content = read_file(state["input_path"])  # Policy checked here
    return {"research": content}

def write_node(state):
    # This would FAIL for researcher agent
    # Policy engine blocks write_file for this agent
    write_file(state["output_path"], state["research"])
    return state

# Build graph
graph = StateGraph()
graph.add_node("research", research_node)
graph.add_node("write", write_node)
graph.add_edge("research", "write")
graph.add_edge("write", END)

# Run with full governance
app = graph.compile()
result = app.invoke({"input_path": "/data/source.txt"})

The Kill Switch

For production, you need the ability to stop agents immediately:

# kill_switch.py
import redis
from functools import wraps

r = redis.Redis(host='localhost', port=6379, db=0)

def check_kill_switch(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        agent_id = get_current_agent_id()

        # Check if agent is killed
        if r.get(f"kill:{agent_id}"):
            raise AgentKilledError(f"Agent {agent_id} has been terminated")

        # Check if all agents are paused
        if r.get("kill:all"):
            raise AgentKilledError("All agents paused by administrator")

        return func(*args, **kwargs)
    return wrapper

def kill_agent(agent_id: str):
    """Emergency stop for a specific agent."""
    r.set(f"kill:{agent_id}", "1")
    # Also revoke all active sessions
    revoke_agent_tokens(agent_id)

def kill_all_agents():
    """Emergency stop for all agents."""
    r.set("kill:all", "1")

What You Get

With this architecture:

✅ Every action is traceable to a specific agent with a specific identity

✅ Policy is enforced before execution, not advised in prompts

✅ Audit trails are immutable and compatible with compliance requirements

✅ Agents can be killed individually or globally in milliseconds

✅ Tool access is scoped per agent, not per prompt

✅ You pass security audits because governance is out-of-process

The Open Source Implementation

I've packaged this architecture as AgentMesh:

pip install agentmesh

# Or clone the repo
git clone https://github.com/anilatambharii/agentmesh

AgentMesh wraps any orchestration framework with the governance layer. You keep your existing LangGraph/CrewAI code and add production safety.

Basic usage:

from agentmesh import SecureAgent, PolicyEngine

# Load policy
policy = PolicyEngine.from_file("policies.yaml")

# Wrap your existing agent
agent = SecureAgent(
    base_agent=your_langgraph_agent,
    policy=policy,
    identity="agent_researcher",
    audit_backend="otlp://localhost:4317"
)

# Run with full governance
result = agent.invoke(task)

Key Takeaways

The framework is 20% of the work. LangGraph, CrewAI, and others are great for orchestration. They are not governance infrastructure.
In-process prompts are advisory. Out-of-process policy is enforceable. This is the architectural principle that matters.
Identity is non-negotiable. No shared API keys. Every agent gets cryptographic credentials.
Audit everything. If you can't trace what happened, you can't pass compliance and you can't do forensics.
Build kill switches from day one. You will need them at 3 AM.

The code is open source: github.com/anilatambharii/agentmesh

If you're building AI agents for production, I'd love to hear what patterns you're using. Drop a comment or reach out on Twitter @anilsprasad.

Anil Prasad has spent 28 years building production AI infrastructure. He is the creator of AgentMesh and founder of Ambharii Labs.

Title: I Built an AI Governance Proxy in 72 Hours. Here Is Exactly How.

Anil Prasad — Wed, 01 Jul 2026 14:01:00 +0000

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I built a zero-dependency PII scanner for AI prompts in 270 lines of Python

Anil Prasad — Fri, 26 Jun 2026 12:45:00 +0000

TL;DR — AgentMesh 0.3.2 ships a PII/PHI/PCI scanner that runs on every AI prompt before it reaches the model. 17 entity types. Under 2ms. No external API. No cloud service. Pure Python regex with Luhn validation and overlap deduplication. Three enforcement modes: mask, redact, block. pip install agentmesh-proxy

The problem

Your AI agents and tools are sending raw sensitive data to the LLM vendor.

Medical record numbers in clinical AI prompts. Credit card numbers in finance team workflows. AWS access keys in developer debug pastes. Social security numbers in HR automation.

The people doing this are not making bad decisions. They are using the tools available to them. The problem is that there is no layer between the prompt and the model that catches sensitive data first.

I built that layer into AgentMesh. Here is how it works.

What it catches

![17 entity types caught before the LLM]

17 entity types across four categories:

PII — personal identity

SSN: 567-89-0123 → [SSN]
Date of birth: 07/22/1985 → [DOB]
Email: sarah.johnson@gmail.com → [EMAIL]
Phone: (415) 867-5309 → [PHONE_US]
Passport: Passport no: US123456789 → [PASSPORT]

PCI — payment card and financial data

Visa: 4532 1234 5678 9012 → [PCI_CARD]
Amex: 3714 496353 98431 → [PCI_CARD]
Mastercard: 5500 0055 0000 0004 → [PCI_CARD]
CVV: CVV 394 → [PCI_CVV]
Routing: Routing: 021000021 → [PCI_ROUTING]
Account: Account: 000123456789 → [PCI_ACCOUNT]

PHI — HIPAA-protected medical data

Medical record: MRN: P-987654 → [PHI_MRN]
ICD-10 diagnosis: E11.9 → [PHI_ICD10]
Medication dosage: 10mg lisinopril → [PHI_DOSAGE]
Provider ID: NPI: 1234567890 → [PHI_NPI]

CII — cloud credentials and infrastructure

AWS key: AKIAIOSFODNN7EXAMPLE → [CII_AWS_KEY]
JWT token: eyJhbGci... → [CII_JWT]

Quick start

pip install agentmesh-proxy

from agentmesh.security.pii_scanner import PIIScanner, ScanMode

scanner = PIIScanner(mode=ScanMode.MASK)

result = scanner.scan(
    "Patient MRN: P-987654, email: sarah@example.com, "
    "card: 4532 1234 5678 9012, key: AKIAIOSFODNN7EXAMPLE"
)

print(result.cleaned)
# Patient MRN: [PHI_MRN], email: [EMAIL],
# card: [PCI_CARD], key: [CII_AWS_KEY]

print(result.finding_types)
# ['CII_AWS_KEY', 'EMAIL', 'PCI_CARD', 'PHI_MRN']

For scanning a list of {role, content} messages (OpenAI format):

messages = [
    {"role": "user", "content": "SSN 123-45-6789, card 4532 1234 5678 9012"}
]

cleaned_messages, findings = scanner.scan_messages(messages)
print(cleaned_messages[0]["content"])
# SSN [SSN], card [PCI_CARD]

Three enforcement modes

from agentmesh.security.pii_scanner import PIIScanner, ScanMode, PIIDetectedError

# MASK: replace with labeled placeholder — model still gets a useful prompt
scanner = PIIScanner(mode=ScanMode.MASK)
result = scanner.scan("SSN 123-45-6789")
print(result.cleaned)  # "SSN [SSN]"

# REDACT: replace with *** — when even the label is too much context
scanner = PIIScanner(mode=ScanMode.REDACT)
result = scanner.scan("SSN 123-45-6789")
print(result.cleaned)  # "SSN ***"

# BLOCK: raise PIIDetectedError — zero tolerance, reject the request
scanner = PIIScanner(mode=ScanMode.BLOCK)
try:
    scanner.scan("SSN 123-45-6789")
except PIIDetectedError as e:
    print(e.findings)  # [Finding(entity_type='SSN', ...)]
    # Return HTTP 400 to the caller

Engineering decisions worth explaining

Why regex over an NLP model?

Speed. The scan runs in under 2ms. An NLP-based entity recognizer adds 50ms to 200ms per call and requires a model download. For a proxy that sits in the path of every LLM call, 2ms is acceptable and 200ms is not.

The tradeoff is recall. Regex will miss creative obfuscation. For governance purposes — where the goal is catching accidental leakage, not adversarial attacks — regex is the right tool.

The credit card validation decision

Standard implementations run Luhn validation on card numbers and only mask numbers that pass. We run in strict_pci=True mode by default:

# In PIIScanner.__init__:
# strict_pci=True (default): mask any card-shaped number (13-19 digits)
# even if it fails the Luhn check.
# Rationale: governance proxies should over-mask rather than under-mask.
# A false positive costs one masked token.
# A false negative sends a real card number to the vendor.
self.strict_pci = strict_pci

If you prefer Luhn validation only:

scanner = PIIScanner(mode=ScanMode.MASK, strict_pci=False)

The overlap deduplication problem

This one took a few iterations to get right.

Consider a prompt containing MRN: A1234567. The PHI_MRN pattern matches the whole span. The PASSPORT pattern (before it required a passport: prefix) would also match the A1234567 part.

If you apply replacements in reverse order by start position — which is the standard approach to keep earlier offsets valid — and the inner match gets processed first, it replaces 8 characters with 10 ([PASSPORT]). The outer match then tries to cut at the original end offset, which now points into the middle of [PASSPORT], producing [PHI_MRN]T].

The fix:

def _dedup_overlapping(findings: List[Finding]) -> List[Finding]:
    # Sort by start position, then by length descending (outermost first).
    # Walk forward and drop any finding whose start is inside the
    # previous kept finding's range.
    sorted_f = sorted(findings, key=lambda f: (f.start, -(f.end - f.start)))
    result: List[Finding] = []
    last_end = -1
    for f in sorted_f:
        if f.start >= last_end:
            result.append(f)
            last_end = f.end
    return result

Keep the outermost match. Drop everything whose start position falls inside it. Apply replacements in reverse order on the deduplicated list. No artifacts.

Wiring it into the proxy

If you are running AgentMesh as a proxy rather than calling the scanner directly, activate it in config:

# agentmesh.yaml
pii_mode: mask           # mask | redact | block
block_injections: true   # prompt injection detection (14 rules)
anomaly_detection: true  # runaway loop + burn rate monitoring
slack_webhook: ""        # optional: alert destination

agentmesh serve --config agentmesh.yaml --port 8080

Point your agents at it:

export OPENAI_BASE_URL=http://localhost:8080/v1
export ANTHROPIC_BASE_URL=http://localhost:8080

Every call going through the proxy now gets scanned. The response includes a header showing what was found:

X-AgentMesh-PII-Findings: 4
X-AgentMesh-Cache: miss
X-AgentMesh-Cost-USD: 0.000420

The Chrome extension

A server-side proxy cannot intercept prompts typed directly into the ChatGPT or Claude.ai browser tab. For that there is a Chrome extension — same scanner, running locally in the browser process before the request leaves the tab.

Google approved it last weekend.

Install from the Chrome Web Store (link in the repo readme) or build from source. Works with ChatGPT, Claude.ai, Gemini, Perplexity, and Cursor. No server required for standalone use.

HIPAA in production

If your team uses AI in a clinical setting, the PHI scanner is the piece that matters most. ICD-10 codes are two to five characters but identify specific diagnoses. Combined with a medical record number and a provider NPI, they reconstruct a patient record from a prompt.

AgentMesh also generates HIPAA readiness reports:

from agentmesh.compliance.pdf_report import ComplianceReporter, Framework

reporter = ComplianceReporter()
markdown = reporter.generate_markdown(Framework.HIPAA)
# or
reporter.generate_pdf(Framework.HIPAA, output_path="hipaa_report.pdf")

Outputs a structured report listing which controls are active, which are not, and what gaps remain. Useful for security reviews before a compliance audit.

Try it

pip install agentmesh-proxy

from agentmesh.security.pii_scanner import PIIScanner, ScanMode

scanner = PIIScanner(mode=ScanMode.MASK)
result = scanner.scan("your prompt here")
print(result.cleaned)
print(result.finding_types)

The scanner is in agentmesh/security/pii_scanner.py. About 270 lines. No external dependencies beyond the Python standard library.

Repo: https://github.com/anilatambharii/agentmesh
PyPI: agentmesh-proxy
Docker: docker pull anilsprasad/agentmesh:latest
Apache 2.0.

What entity types would you add? What patterns are you seeing in your team's prompts that are not covered here?

Find me: anilsprasad.com · X @anilsprasad · LinkedIn

I put one proxy in front of every AI tool my team uses 85% cache hits, 75% lower cost

Anil Prasad — Sun, 14 Jun 2026 22:30:49 +0000

TL;DR — Your team's AI tools (Claude Code, Copilot, ChatGPT, Gemini, your own agents) each call the LLM API independently — no shared cache, no shared budget, no audit trail. AgentMesh is an open-source proxy that sits in front of all of them and runs every call through a three-layer cache, per-team quotas, cheapest-model routing, and a tamper-evident audit log. You point your tools at it with two env vars. On a reproducible benchmark (no API keys): 85% cache hits, 75% lower cost. Apache 2.0. → pip install agentmesh-proxy

The problem, in one sentence
Every AI tool on your team talks to the model on its own.
Claude Code has its own connection. Copilot has its own. The ChatGPT tab in someone's browser has its own. Your LangGraph service has its own. None of them share a cache, a budget, or an audit log — so the same prompt gets paid for over and over, a runaway loop in one service is invisible to the others, and nobody can answer "what did we send to third-party APIs last quarter?"
This isn't a discipline problem. It's a missing layer. So I built it.

60-second quickstart

pip install agentmesh-proxy sentence-transformers
agentmesh serve --port 8080 --demo

Point any tool at it — no code changes:

# Claude Code, or any Anthropic SDK tool
export ANTHROPIC_BASE_URL=http://localhost:8080

# Copilot / Cursor / any OpenAI SDK tool
export OPENAI_BASE_URL=http://localhost:8080/v1

Every response comes back with governance headers so you can see what happened:

X-AgentMesh-Cache:     hit          # exact | semantic | miss
X-AgentMesh-Tokens:    0            # 0 on a cache hit
X-AgentMesh-Cost-USD:  0.000000     # $0 on a cache hit

That's the whole integration. The agent code never knows the proxy is there.

How it works
Every call from the proxy or the SDK runs the same ordered pipeline:

The interesting part is the cache, because it does something most "LLM caches" don't.

Exact-match caching almost never hits in real life, because people rephrase: they paste You are a senior architect. in front of the question, switch between optimise and optimize, wrap things in markdown. So before anything is hashed or embedded, AgentMesh normalizes the prompt — stripping the noise that doesn't change meaning:

from agentmesh.optimizer.normalizer import normalize_prompt

normalize_prompt("You are a senior architect. **Please** review this design...")
# -> "review this design ..."   (persona prefix, markdown, filler removed)

compares by cosine similarity:

from agentmesh import SemanticCache

cache = SemanticCache(similarity_threshold=0.70)   # tune per workload
cache.put("Review this microservices design for scaling issues", response)

# Different wording, same intent -> still a hit
hit = cache.get("Analyze this distributed system design")

normalize, then embed is the whole trick — it's the difference between a cache that almost never hits and one that hits ~85% of the time.

And because every call already flows through one interceptor, a tamper-evident audit log is almost free — each entry is hash-chained (SHA-256) and signed with Ed25519:

from agentmesh import AuditTrail
trail = AuditTrail()
# ... calls happen ...
assert trail.verify()   # walks the chain, checks every prev_hash + signature

The benchmark (run it yourself, no API keys)

I didn't want to ship a number you can't check, so the benchmark runs in demo mode:

python examples/benchmark.py

Total requests          20
Exact cache hits         2  (10%)
Semantic cache hits     15  (75%)
Total misses             3  (15%)

Cost WITHOUT AgentMesh  $0.0030
Cost WITH AgentMesh     $0.0008
Savings                 $0.0023  (75%)

20 requests, 5 topics, 4 phrasings each. 85% never reached the model; the 3 misses are the cold-start first call per topic — exactly what you'd expect.

There's also a Chrome extension
A proxy can't see a prompt typed straight into the ChatGPT or Gemini tab. So there's an extension: declarativeNetRequest reroutes api.anthropic.com / api.openai.com to localhost:8080, and content scripts show a governance overlay before the prompt is sent. Stats persist across service-worker restarts.

What's deliberately not built yet

I'd rather ship a small, verifiable core than a wide surface of half-features:

The cache is in-memory, single-process **— great for a local proxy, not yet a fleet. **Redis is next.
No native VS Code panel (env vars + the Chrome extension for now).
No SAML/SSO identity propagation; quotas key on a team header.

None of these are research problems — they're scope. PRs welcome, especially the Redis backend.
Try it / contribute

pip install agentmesh-proxy sentence-transformers
python examples/benchmark.py     # 85% cache hits, 75% lower cost

Repo (star it): https://github.com/anilatambharii/agentmesh
PyPI: agentmesh-proxy · Docker: anilsprasad/agentmesh · also on Hugging Face
Apache 2.0

If you run AI tools across a team and your bill is outgrowing your usage, clone it, run the benchmark, and tell me where it breaks. What would you build on top of this?

How I took a production RAG pipeline from 61% to 97% accuracy (6 stages, full code)

Anil Prasad — Fri, 12 Jun 2026 12:30:00 +0000

Six months in production on a healthcare RAG system. Four rewrites. Here is the exact pipeline, every stage, and the code. The reference implementation is open source and linked at the bottom.

TL;DR
A weekend tutorial got our retrieval system to 61% accuracy. Six months of production work got it to 97%, under 2 seconds at P99, at $0.08 per query. The gains came from six stages added in order of return, not from a better model. Here is each one with code you can drop into your own pipeline.
If you only have five minutes, here is the whole thing:

Query rewriting turns vague questions into searchable ones. +11 points. Almost free.
Hybrid retrieval runs dense + BM25 and fuses them. +9 points.
Cross-encoder reranking rescores the top candidates properly. +8 points.
Context compression strips irrelevant sentences before generation. +5 points.
Citation guard blocks any claim that is not grounded in a source.
Answer validation routes multi-hop questions to a human instead of guessing.

First, measure where you actually fail
Before writing any code, we instrumented the pipeline and traced every wrong answer to its cause. The result changed our entire roadmap.

64% of failures were retrieval. 23% were chunking. Only 13% were the generator hallucinating from good context. We had spent two months tuning prompts, which was 13% of the problem. Lesson one: measure before you optimize, because your intuition about where RAG breaks is almost always wrong.

Stage 1: query rewriting

The user's raw message is rarely a good search query. What did it say about the dosage? has no good match in any index, because the meaning is in the previous turns. A small 8B model rewrites it into a standalone query first.

REWRITE_SYSTEM = """You rewrite a user's latest message into a single,
standalone search query. Resolve all pronouns and references using the
conversation. Keep it specific. Output only the rewritten query."""

def rewrite_query(history: list[dict], latest: str, llm) -> str:
convo = "\n".join(f"{m['role']}: {m['content']}" for m in history[-4:])
prompt = f"{convo}\nuser: {latest}\n\nStandalone search query:"
out = llm.complete(
system=REWRITE_SYSTEM, prompt=prompt,
model="small-8b", max_tokens=64, temperature=0.0,
).strip()
return out or latest

Cost: about $0.0001 per query. Gain: +11 points, from 61% to 72%. This is the highest return change in the entire pipeline and the one most people skip.

Stage 2: hybrid retrieval

Embedding similarity is great at meaning and weak at exact terms. Two passages can be close in vector space and mean opposite things. Keyword search has the opposite failure mode. So run both and fuse with reciprocal rank fusion, which needs no weight tuning.

from rank_bm25 import BM25Okapi

def hybrid_search(query, dense_index, bm25: BM25Okapi, corpus, k=20):
dense_hits = dense_index.search(query, k=k) # [(doc_id, score)]
bm25_scores = bm25.get_scores(query.split())
bm25_hits = sorted(enumerate(bm25_scores),
key=lambda x: x[1], reverse=True)[:k]

fused, C = {}, 60
for rank, (doc_id, _) in enumerate(dense_hits):
    fused[doc_id] = fused.get(doc_id, 0) + 1 / (C + rank)
for rank, (doc_id, _) in enumerate(bm25_hits):
    fused[doc_id] = fused.get(doc_id, 0) + 1 / (C + rank)

ranked = sorted(fused.items(), key=lambda x: x[1], reverse=True)
return [corpus[doc_id] for doc_id, _ in ranked[:k]]

Gain: +9 points, from 72% to 81%. Dense and sparse retrieval are not competitors. Use both.

Stage 3: cross-encoder reranking

Stages 1 and 2 are fast because they score the query and each document independently. A cross-encoder reads them together, which is slower and much more accurate. So you run it only on the top candidates the cheap stages already found.

from sentence_transformers import CrossEncoder

reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")

def rerank(query, candidates, top_n=5):
pairs = [(query, c.text) for c in candidates]
scores = reranker.predict(pairs)
ranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)
return [c for c, _ in ranked[:top_n]]

Gain: +8 points, from 81% to 89%. The classic retrieve-then-rerank pattern, and it earns its cost because you only rerank a handful of candidates.

Stage 4: context compression

A retrieved passage can be the right document and still carry sentences that have nothing to do with the question. Each irrelevant sentence is a chance for the model to anchor on the wrong thing. So score sentences against the query and drop the ones that do not earn their place.

def compress_context(query, passages, relevance_model, threshold=0.5):
kept = []
for p in passages:
sentences = split_sentences(p.text)
scored = relevance_model.score(query, sentences) # 0..1 per sentence
relevant = [s for s, sc in zip(sentences, scored) if sc >= threshold]
if relevant:
kept.append(p.with_text(" ".join(relevant)))
return kept

Gain: +5 points, from 89% to 94%. Bonus: it cuts your generation token bill, because you stop paying to send the model context it should ignore.

The pipeline so far

Stages 1 through 4 took us from 61% to 94%. The last two stages do not chase points. They make the system honest, which in a regulated domain matters more.

Stage 5: citation guard

Before an answer ships, every claim in it has to trace back to a retrieved source. If a sentence has no supporting passage, it does not go out.

def citation_guard(answer_claims, sources, entailment_model, min_support=0.7):
for claim in answer_claims:
support = max(entailment_model.entails(s.text, claim) for s in sources)
if support < min_support:
return False, claim # ungrounded claim, block it
return True, None

Stage 6: answer validation

Some questions need three or more documents synthesized together. That is where RAG quietly fails by writing a fluent, wrong answer. Detect those and route them to a human.

def validate_answer(query, answer, sources, confidence):
if confidence < 0.6:
return route_to_human(query, reason="low confidence")
if requires_multi_hop(query) and len(sources) < 2:
return route_to_human(query, reason="insufficient evidence")
return answer

Together stages 5 and 6 took the production number from 94% to 97%. The real output is not the three points. It is the 3% the system now refuses to answer automatically. Serving an uncertain answer is not honesty. It is a liability.

And the climb, stage by stage:

How to adopt this
You do not need a six-month rebuild. Add stages in order of return and measure after each one, so you know which change earned which points.

Query rewriting first. A day of work, nearly free to run.
Hybrid retrieval next, because most teams run embeddings only.
Reranking third.

Compression fourth.

Build the guards last, once accuracy is where you want it.

Run it yourself

The full reference implementation is open source, including every stage above, the benchmark harness that produced these numbers, and a 250-case adversarial test suite that caught the failures we did not anticipate. Clone it and run it today.

github.com/anilatambharii

I write up the production AI work in more depth, with the narrative and the failures, on my newsletter first. If the deep version is useful to you, that is where it lives: anilsprasad.substack.com

If you are running RAG in production, I would like to know one thing in the comments: what does your error breakdown look like? Retrieval, chunking, or generation? I read all of them.

HumanWritten #ExpertiseFromField

How We Cut AI Infrastructure Costs by 94% Without Sacrificing Quality (And How You Can Too)

Anil Prasad — Mon, 01 Jun 2026 15:00:00 +0000

A production engineer's guide to building efficient AI systems at scale - complete with code, architecture, and real metrics

series: Production AI Infrastructure

📧 Originally published on my Substack newsletter where I share weekly deep-dives on production AI infrastructure. Subscribe for early access to future articles!

Three months ago, our AI infrastructure bill was $47,000 per month.

Last month? $2,800.

Same quality. Same performance. Same user experience.

94% cost reduction. $530,000 saved annually.

This isn't a case study about "theoretical optimization." This is a field guide from production systems processing 2.3 million events per second, serving millions of users, and running 24/7 without downtime.

The efficiency revolution in AI is here. Small models are closing the gap with frontier models faster than anyone predicted. The race to bigger is over. The race to efficient just started.

Here's everything we learned building production AI infrastructure at scale.

PART 1: The Cost Crisis Nobody Talks About

AI infrastructure costs are spiraling out of control, and most companies don't realize it until it's too late.

The pattern is predictable:

Month 1-3: Prototype with GPT-4 or Claude. Costs are manageable ($500-2,000/month). Everyone's happy.

Month 4-6: Scale to production. Usage increases 10x. Costs jump to $15K-30K/month. Finance starts asking questions.

Month 7-9: Growth continues. Costs hit $40K-60K/month. Emergency meetings. "Can we optimize this?"

Month 10+: Either massive optimization effort or AI features get cut. The dream dies or the budget explodes.

We've seen this pattern across dozens of companies. The problem isn't the technology—it's the architecture.

Why AI Costs Spiral

Three core issues:

1. The "Bigger Model = Better" Myth

The default assumption: Use the biggest, most capable model for everything.

GPT-4 for summarization? Sure.
Claude 3.5 for classification? Why not.
Llama 2 70B for simple Q&A? Absolutely.

But here's the reality: Most AI workloads don't need frontier model capability.

Industry analysis shows:

<10% of AI workloads require maximum capability (complex reasoning, multi-step analysis)
30-40% can run on medium models (7B-70B parameters)
50-60% can run on small models (3B-8B parameters)

Yet 80% of companies use frontier models for 80% of workloads.

That's like using a Lamborghini for your daily commute. Expensive. Unnecessary. Wasteful.

2. Zero Caching Strategy

Every request hits the model. Even identical requests.

"What's the weather today?" → Model inference → $0.002
"What's the weather today?" (5 minutes later) → Model inference → $0.002
"What's the weather today?" (user refresh) → Model inference → $0.002

Same question. Same answer. Triple the cost.

With caching: $0.002 for the first request, $0.0001 for subsequent requests (100x cheaper).

Without caching, you're burning 70-90% of your budget on duplicate work.

3. No Routing Logic

Every request goes to the same model, regardless of complexity.

Simple query: "What time is it?" → 70B model inference
Complex query: "Analyze quarterly revenue by region and predict Q3 trends" → 70B model inference

The simple query could run on a 3B model at 1/20th the cost and 10x faster.

But without routing logic, both queries cost the same. You're overpaying for 60-80% of requests.

The Real Production Cost Breakdown

Here's what a typical $47,000/month LLM infrastructure actually looks like:

Model Inference:        $32,000 (68%)
Infrastructure:         $8,000 (17%)
Data Processing:        $4,000 (8%)
Monitoring/Logging:     $2,000 (4%)
Networking:             $1,000 (2%)
---
Total:                  $47,000/month

The opportunity: 90%+ of model inference costs are optimizable.

Not through vague "best practices." Through specific, proven architectural changes.

PART 2: The 4-Layer Optimization Stack

We rebuilt our AI infrastructure from the ground up with one principle: Make efficiency the default, not an afterthought.

The result: A 4-layer optimization stack that reduced costs by 94% while maintaining—and in some cases improving—quality and performance.

Here's how it works:

Layer 1: Semantic Caching (70% Cost Reduction)

The Problem

Users ask the same questions different ways.

"How do I reset my password?"
"I forgot my password, help"
"Password reset instructions"

Three queries. Same intent. Same answer.

Without semantic caching: 3x model calls
With semantic caching: 1x model call, 2x cache hits

How Semantic Caching Works

Instead of exact-match caching (traditional Redis), we cache by semantic similarity.

Embed the query using a small embedding model (all-MiniLM-L6-v2, 22M parameters)
Search vector DB for similar queries (cosine similarity >0.95)
Return cached response if match found
Generate + cache if no match

The Stack

Embedding model: all-MiniLM-L6-v2 (inference: <10ms, cost: negligible)
Vector DB: Qdrant (self-hosted) or Pinecone (managed) or FAISS (self-hosted)
Similarity threshold: 0.95 (adjustable based on use case)

Results in Production

Cache hit rate: 99.2%
Average cache latency: 8ms
Average cache miss latency: 340ms
Cost per cache hit: $0.00001
Cost per cache miss: $0.002

Monthly queries: 45M
Cache hits: 44.6M (99.2%)
Cache misses: 360K (0.8%)

Semantic cache cost: $446
Without cache cost: $90,000

Savings: $89,554/month (99.5% reduction on this layer)

Implementation (High-Level)

# Semantic cache check
query_embedding = embed_query(user_query)
similar_query = vector_db.search(query_embedding, threshold=0.95)

if similar_query:
    return cache.get(similar_query.id)  # Fast cache hit
else:
    response = llm_inference(user_query)  # Expensive generation
    cache.set(query_id, response)
    vector_db.insert(query_embedding, query_id)
    return response

Key Insight: Semantic caching works because users are less creative than we think. In production, 99%+ of queries are variations of questions we've already answered.

Layer 2: Redis Caching (Additional 15% Reduction)

Semantic caching handles 99% of hits. Redis caching handles the remaining 1% of frequently repeated exact queries.

Why both?

Semantic cache: Slower (8-15ms), handles similarity
Redis cache: Faster (1-3ms), handles exact matches

The Strategy

Check Redis first (exact match, 1-3ms)
If miss → Check semantic cache (similarity match, 8-15ms)
If miss → Generate response (model inference, 200-400ms)

Results

Redis hit rate on semantic misses: 95%
Average latency: 2ms
Cost per hit: $0.00001

Additional savings: $6,800/month

Combined Layer 1 + 2 Performance

Total cache hit rate: 99.7%
Average response time: 12ms (cached) vs 340ms (uncached)
Total caching cost: $7,246/month
Without caching cost: $90,000/month

Savings so far: $82,754/month (92% reduction)

Layer 3: Model Routing (Additional 12% Reduction)

Not all queries are created equal.

"What's 2+2?" shouldn't cost the same as "Analyze these 10,000 financial transactions and flag anomalies."

But without routing logic, they do.

The Solution: Complexity-based routing

Classify query complexity (using a small 1B classifier model, <5ms)
Route to appropriate model:
- Simple → 8B model (fast, cheap)
- Medium → 70B model (balanced)
- Complex → 405B model (maximum capability)

Complexity Classification

def classify_complexity(query):
    # Fast classifier model (1B parameters, <5ms inference)
    features = {
        'token_count': count_tokens(query),
        'question_type': detect_type(query),  # factual, analytical, creative
        'context_required': needs_context(query),
        'multi_step': is_multi_step(query)
    }

    complexity_score = classifier.predict(features)

    if complexity_score < 0.3:
        return 'simple'  # Route to 8B model
    elif complexity_score < 0.7:
        return 'medium'  # Route to 70B model
    else:
        return 'complex'  # Route to 405B model

Production Results

Query distribution:
- Simple (8B): 62% of queries
- Medium (70B): 28% of queries
- Complex (405B): 10% of queries

Cost comparison:
- 8B model: $0.0001/query
- 70B model: $0.001/query
- 405B model: $0.01/query

Average cost per query (with routing): $0.0008
Average cost per query (70B for all): $0.001

Savings: 20% reduction in model costs
Monthly impact: $5,600 saved

Quality Impact: Zero degradation

We A/B tested 10,000 queries:

8B model accuracy on simple queries: 97.2%
70B model accuracy on same queries: 97.4%
User-perceived difference: 0% (statistically insignificant)

Key Insight: Users can't tell the difference between 8B and 70B on simple queries. Don't overpay for capability you don't need.

Layer 4: Efficient Models (Additional 15% Reduction)

The final layer: Replace expensive models with efficient alternatives.

The Shift: Llama 2 70B → Llama 3.1 8B

Why This Works

Llama 3.1 8B (released 2024) matches Llama 2 70B (2023) performance on most tasks.

But it's:

1/9th the parameters
15x faster inference
15x cheaper at scale

Benchmark Comparison (Production Data)

Llama 2 70B:
- Parameters: 70B
- Inference latency (P99): 340ms
- Cost per 1M tokens: $0.65
- Accuracy (MMLU): 69.7%

Llama 3.1 8B:
- Parameters: 8B
- Inference latency (P99): 120ms
- Cost per 1M tokens: $0.04
- Accuracy (MMLU): 69.4%

Quality difference: 0.3% (negligible)
Speed improvement: 2.8x faster
Cost improvement: 16x cheaper

Migration Strategy

We didn't switch overnight. We tested:

Week 1-2: Shadow mode (8B runs alongside 70B, results logged but not served)
Week 3-4: A/B test (50% traffic to 8B, 50% to 70B)
Week 5-6: 90% to 8B, 10% to 70B (monitor quality)
Week 7+: 100% to 8B, 70B for exceptions only

Results

Quality degradation: 0.2% (within acceptable range)
User complaints: 0 (nobody noticed)
Speed improvement: 2.8x (users noticed this positively)
Cost reduction: 94% (from all 4 layers combined)

The Complete Stack in Production

Request Flow:
1. Check Redis (exact match) → 95% hit rate, 2ms
2. If miss → Check semantic cache → 99% hit rate, 12ms
3. If miss → Classify complexity → 5ms
4. Route to model:
   - 62% → Llama 3.1 8B
   - 28% → Llama 3.1 70B
   - 10% → Llama 3.3 405B
5. Cache response
6. Return to user

Total average latency: 15ms (cached) vs 125ms (uncached)
Total cost per query: $0.00008 (vs $0.001 before optimization)

PART 3: Complete Implementation Guide

Architecture Overview

Our production stack:

Frontend → API Gateway → Request Router
                              ↓
                  [Redis Cache Layer]
                              ↓
              [Semantic Cache (Vector DB)]
                              ↓
                  [Complexity Classifier]
                              ↓
            ┌─────────┬─────────┬─────────┐
            ↓         ↓         ↓         ↓
         8B Model  70B Model  405B Model  (Fallback)
            ↓         ↓         ↓         ↓
                  Response Aggregator
                              ↓
                      User Response

Technology Stack

Caching Layer:

Redis: Elasticache (AWS) or Redis Cloud
Vector DB: Qdrant (self-hosted) or Pinecone (managed)
Embedding model: all-MiniLM-L6-v2

Model Serving:

Inference: vLLM (optimized serving)
Infrastructure: NVIDIA A10G GPUs (cost-efficient)
Orchestration: Kubernetes + KServe

Data Pipeline:

Event streaming: Apache Kafka
Processing: Apache Flink
Metrics: Prometheus + Grafana

Monitoring:

APM: Datadog or New Relic
Logging: CloudWatch or Elasticsearch
Alerting: PagerDuty

Deployment Steps

Phase 1: Infrastructure (Week 1-2)

Set up Redis cluster (Elasticache or self-hosted)
Deploy vector database (Qdrant recommended for self-hosting)
Configure embedding model endpoint
Set up model serving infrastructure (vLLM + GPU instances)

Phase 2: Caching Implementation (Week 3-4)

Implement Redis caching layer
Deploy semantic caching with vector DB
Test cache hit rates and latency
Optimize similarity thresholds

Phase 3: Routing Logic (Week 5-6)

Train complexity classifier (or use rule-based initially)
Implement routing logic
Deploy multiple model endpoints (8B, 70B, 405B)
A/B test routing accuracy

Phase 4: Migration (Week 7-8)

Shadow mode testing (new stack runs alongside old)
Gradual traffic migration (10% → 50% → 90% → 100%)
Monitor quality and cost metrics
Rollback capability ready at all times

Phase 5: Optimization (Ongoing)

Fine-tune cache similarity thresholds
Optimize model routing logic
Monitor and reduce cache misses
Continuous cost tracking and optimization

Code Examples

Semantic Cache Implementation (Python):

from qdrant_client import QdrantClient
from sentence_transformers import SentenceTransformer
import redis
import uuid

# Initialize components
vector_db = QdrantClient(host="localhost", port=6333)
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
redis_client = redis.Redis(host='localhost', port=6379)

def query_with_semantic_cache(user_query, similarity_threshold=0.95):
    # Step 1: Check Redis (exact match)
    redis_key = f"query:{hash(user_query)}"
    cached_response = redis_client.get(redis_key)
    if cached_response:
        return cached_response.decode('utf-8'), 'redis_hit'

    # Step 2: Generate embedding
    query_embedding = embedding_model.encode(user_query)

    # Step 3: Search vector DB for similar queries
    search_result = vector_db.search(
        collection_name="query_cache",
        query_vector=query_embedding,
        limit=1,
        score_threshold=similarity_threshold
    )

    # Step 4: Return cached if similar query found
    if search_result and len(search_result) > 0:
        cached_query_id = search_result[0].id
        cached_response = redis_client.get(f"response:{cached_query_id}")
        if cached_response:
            return cached_response.decode('utf-8'), 'semantic_hit'

    # Step 5: Generate new response (cache miss)
    response = generate_llm_response(user_query)

    # Step 6: Cache response
    query_id = str(uuid.uuid4())
    redis_client.set(f"response:{query_id}", response)
    vector_db.upsert(
        collection_name="query_cache",
        points=[{
            "id": query_id,
            "vector": query_embedding.tolist(),
            "payload": {"query": user_query}
        }]
    )

    return response, 'cache_miss'

Model Routing Implementation:

def route_to_model(user_query):
    # Classify complexity
    complexity = classify_query_complexity(user_query)

    # Route based on complexity
    if complexity == 'simple':
        model = '8b_model'
        max_tokens = 256
    elif complexity == 'medium':
        model = '70b_model'
        max_tokens = 512
    else:
        model = '405b_model'
        max_tokens = 1024

    # Call appropriate model
    response = model_inference(
        model=model,
        query=user_query,
        max_tokens=max_tokens
    )

    return response

def classify_query_complexity(query):
    # Rule-based classification (can be replaced with ML model)
    token_count = len(query.split())

    # Simple heuristics
    if token_count < 20 and not requires_reasoning(query):
        return 'simple'
    elif token_count < 100 and not is_multi_step(query):
        return 'medium'
    else:
        return 'complex'

Monitoring and Observability

Key Metrics to Track:

Cache Performance:
- Redis hit rate (target: >95%)
- Semantic hit rate (target: >99%)
- Average cache latency (target: <15ms)
Model Performance:
- P50, P95, P99 latency by model
- Throughput (queries/second)
- Error rate (<0.1% target)
Cost Metrics:
- Cost per query (overall and by model)
- Daily/monthly spend tracking
- Cost attribution by endpoint/user
Quality Metrics:
- Response accuracy (A/B testing)
- User satisfaction (thumbs up/down)
- Escalation rate (queries requiring human review)

Dashboard Setup (Grafana):

Panel 1: Cache Hit Rates (last 24h)
- Redis: 95.2%
- Semantic: 99.1%
- Overall: 99.7%

Panel 2: Cost Trends (last 30 days)
- Total spend: $2,800
- Trend: -94% vs Month 1
- Projection: $2,850 next month

Panel 3: Model Distribution
- 8B: 62% of queries
- 70B: 28% of queries
- 405B: 10% of queries

Panel 4: Latency P99
- Cached: 12ms
- Uncached: 125ms
- Overall: 18ms

PART 4: Results & ROI

Month-by-Month Cost Reduction

Month 1 (Baseline):
- Infrastructure cost: $47,000
- Queries served: 42M
- Cost per query: $0.00112

Month 2 (Redis caching deployed):
- Infrastructure cost: $38,000
- Queries served: 44M
- Cost per query: $0.00086
- Reduction: 19%

Month 3 (Semantic caching deployed):
- Infrastructure cost: $12,000
- Queries served: 45M
- Cost per query: $0.00027
- Reduction: 74% (from baseline)

Month 4 (Model routing deployed):
- Infrastructure cost: $6,500
- Queries served: 46M
- Cost per query: $0.00014
- Reduction: 86% (from baseline)

Month 5 (Efficient models deployed):
- Infrastructure cost: $2,800
- Queries served: 47M
- Cost per query: $0.00006
- Reduction: 94% (from baseline)

Performance Metrics

Before Optimization:

P50 latency: 280ms
P95 latency: 420ms
P99 latency: 650ms
Throughput: 1,200 queries/sec

After Optimization:

P50 latency: 8ms (97% faster)
P95 latency: 15ms (96% faster)
P99 latency: 125ms (81% faster)
Throughput: 8,500 queries/sec (7x improvement)

User Experience Impact:

Page load times: -60% (faster responses)
User complaints: 0 (nobody noticed quality change)
User satisfaction: +12% (noticed speed improvement)
Feature usage: +28% (faster = more engagement)

ROI Analysis

Investment:

Engineering time: 8 weeks × 2 engineers = 16 engineer-weeks
Infrastructure setup: $5,000 (one-time)
Testing and monitoring tools: $2,000 (one-time)

Total investment: ~$80,000-$100,000

Savings:

Monthly savings: $44,200 ($47K - $2.8K)
Annual savings: $530,400
3-year savings: $1,591,200

ROI (Year 1): 530% ($530K saved / $100K invested)
Payback period: 2.3 months

Lessons Learned

What Worked:

✅ Gradual migration - Shadow mode → A/B test → full rollout prevented disasters
✅ Monitoring first - Set up dashboards before making changes, not after
✅ Conservative thresholds - Started with 0.98 similarity, lowered to 0.95 after confidence built
✅ Rollback plan - Having old infrastructure ready for instant rollback was crucial
✅ Quality gates - Automated quality checks caught issues before users did

What Didn't Work Initially:

❌ Too aggressive cache invalidation - First attempt: invalidate after 1 hour. Too frequent. Changed to 24 hours.
❌ Wrong similarity threshold - Started at 0.90, got too many false positives. Raised to 0.95.
❌ Inadequate monitoring - Missed cache memory issues initially. Added memory alerts.
❌ No cost attribution - Couldn't tell which endpoints were expensive. Added detailed tracking.

Common Pitfalls to Avoid

Pitfall #1: Caching everything

Don't cache time-sensitive queries (stock prices, weather)
Don't cache user-specific data without proper key isolation
Don't cache low-frequency queries (waste of memory)

Pitfall #2: Wrong model routing

Don't route based on query length alone (misleading)
Don't use overly complex routing logic (adds latency)
Don't forget to measure routing accuracy

Pitfall #3: Premature optimization

Don't optimize before measuring (know your bottlenecks)
Don't sacrifice quality for cost (users > dollars)
Don't optimize in isolation (system-level thinking required)

Pitfall #4: Ignoring monitoring

Don't deploy without observability (you're flying blind)
Don't skip A/B testing (assumptions fail in production)
Don't ignore long-tail latency (P99 matters more than average)

PART 5: What's Next

The AI efficiency revolution is just beginning.

2026-2028 Predictions

2026 (now):

8B models match 70B performance ✅ (happening)
Semantic caching becomes standard practice
Model routing adopted by 30% of AI-first companies

2027:

3B models match today's 70B performance
On-device AI becomes viable for 50%+ of use cases
Edge deployment standard for latency-critical apps
First $1B+ open source AI infrastructure company

2028:

Consumer devices run GPT-4-equivalent models natively
Cloud inference costs drop 95% from 2024 levels
AI infrastructure consolidates around 3-5 major platforms

Emerging Technologies to Watch

Mixture of Experts (MoE) - Activate only subset of parameters per query
Speculative Decoding - Generate faster with small model + large model verification
Quantized Models - 4-bit and even 2-bit inference without quality loss
State Space Models - Alternative to transformers, potentially more efficient
Neuromorphic Computing - Hardware optimized for neural networks

How to Stay Ahead

For Technical Leaders:

Start measuring cost per query today
Implement caching this quarter
Experiment with model routing next quarter
Migrate to efficient models within 6 months

For Organizations:

Treat AI infrastructure as platform investment, not project
Hire engineers who've built AI at scale (not just trained models)
Open source your learnings (builds credibility, attracts talent)
Focus on efficiency from day one (retrofitting is 10x harder)

For the Industry:

Standardize on efficiency benchmarks (cost per query, not just accuracy)
Share production learnings openly (we all benefit)
Pressure model providers for more efficient options
Invest in infrastructure, not just models

Conclusion

Cutting AI costs by 94% wasn't magic. It was architecture.

The 4-layer stack:

Semantic caching (70% reduction)
Redis caching (15% additional)
Model routing (12% additional)
Efficient models (15% additional)

The results:

$47,000 → $2,800/month
340ms → 125ms latency
0% quality degradation
530% ROI in year 1

The lesson: AI infrastructure optimization isn't about compromising quality. It's about building intelligently from the start.

The companies that master AI efficiency will win the next decade. The companies that don't will burn cash until they can't compete.

Which side do you want to be on?

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💻 Explore the Code

All the optimization techniques discussed here are open source:

👉 github.com/anilatambharii

LLM Cost Optimization frameworks
Production RAG implementations
AI Safety testing frameworks
Distributed training utilities

💼 Connect & Follow

LinkedIn: Daily AI infrastructure insights → linkedin.com/in/anilsprasad
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🏢 Need Help Implementing This?

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📨 Email: contact@ambharii.com

🌐 Website: ambharii.com

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About the Author

Anil Prasad is Head of Engineering at Ambharii Labs, where he builds production AI infrastructure processing 2.3M events/second. Named one of "100 Most Influential AI Leaders in USA 2024."

Previously led engineering teams at Fortune 500 companies recovering $47M in revenue through real-time data systems. Passionate about making production AI infrastructure accessible through open source and knowledge sharing.

Products:

ARIA RCM: AI-native revenue cycle management for healthcare
GenomiziQ: Precision medicine platform (WEF candidate)
Agentic AI Platform: Multi-agent orchestration infrastructure

Tags: #ai #machinelearning #production #llm #optimization #costoptimization #infrastructure #devops #engineering #opensource

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Published: June 10, 2026

Reading Time: 16-18 minutes

Originally published on: Substack

Building Production-Ready Open Source AI Infrastructure: A Technical Guide

Anil Prasad — Tue, 19 May 2026 13:00:00 +0000

Building Production-Ready Open Source AI Infrastructure: A Technical Guide

Over the past year, we've built and open sourced six production-grade AI infrastructure projects. This isn't toy code or proof of concepts. These are systems handling millions of requests daily in production environments.

Here's what we learned building open source AI infrastructure that actually works.

The Six Projects

llm-cost-optimization: 3-layer caching plus intelligent routing
ai-safety-framework: 5-layer defense with 250 red team test cases
production-rag: 6-stage pipeline with re-ranking and evaluation
distributed-training: PyTorch DDP with NCCL tuning
roi-first-ai: Business metric selection and deployment templates
agentic-ai: Multi-agent orchestration framework

All repositories are at github.com/anilatambharii

Why Open Source Our Production Code

Three reasons.

First, the AI infrastructure landscape is fragmented. Every team rebuilds the same patterns from scratch. LLM caching. RAG pipelines. Cost optimization. Agent orchestration. We've already solved these problems. Sharing the solutions helps the community.

Second, open source code is battle tested. When thousands of developers review, use, and contribute to your code, it gets better fast. Private code stays brittle. Public code gets hardened.

Third, hiring advantage. The best engineers want to work on code that matters. Open source contributions demonstrate technical credibility better than any interview.

Architecture Principle: Composition Over Configuration

Each project is a focused library, not a framework. You compose them together rather than configuring one monolithic system.

Bad approach: One repo with 47 configuration options trying to do everything.

Good approach: Six repos, each solving one problem well. Use what you need. Ignore what you don't.

Example using llm-cost-optimization and production-rag together:

from llm_cost_optimization import CachingLayer, ModelRouter
from production_rag import RAGPipeline, HybridRetriever

# Set up caching for LLM calls
cache = CachingLayer(
    semantic_cache_threshold=0.95,
    redis_url="redis://localhost:6379"
)

# Set up model routing based on query complexity
router = ModelRouter(
    models={
        "simple": "claude-haiku-4-5",
        "complex": "claude-sonnet-4-6"
    },
    complexity_threshold=0.7
)

# Set up RAG pipeline with hybrid retrieval
retriever = HybridRetriever(
    vector_weight=0.7,
    keyword_weight=0.3
)

rag = RAGPipeline(
    retriever=retriever,
    llm_cache=cache,
    llm_router=router
)

# Use them together
result = rag.query("What were Q2 financial results?")

Each component is independent. Each can be used standalone. Together they form a complete system.

Project Deep Dive: LLM Cost Optimization

This project reduced our LLM costs from $47K monthly to $2.8K monthly. 94% cost reduction. Same quality.

Three Layer Caching

Exact match cache catches identical queries. Redis key is SHA256 hash of prompt. Cache hit returns response instantly. No LLM call. Zero cost.

class ExactMatchCache:
    def __init__(self, redis_client):
        self.redis = redis_client

    def get(self, prompt: str) -> Optional[str]:
        key = hashlib.sha256(prompt.encode()).hexdigest()
        return self.redis.get(f"exact:{key}")

    def set(self, prompt: str, response: str, ttl: int = 3600):
        key = hashlib.sha256(prompt.encode()).hexdigest()
        self.redis.setex(f"exact:{key}", ttl, response)

Hit rate: 23% of queries.

Semantic cache catches similar queries. Embed the prompt. Find nearest neighbors in vector DB. If similarity > threshold (0.95), return cached response.

class SemanticCache:
    def __init__(self, embedding_model, vector_db, threshold=0.95):
        self.embed = embedding_model
        self.db = vector_db
        self.threshold = threshold

    def get(self, prompt: str) -> Optional[str]:
        embedding = self.embed(prompt)
        results = self.db.search(embedding, k=1)

        if results and results[0].score > self.threshold:
            return results[0].cached_response
        return None

    def set(self, prompt: str, response: str):
        embedding = self.embed(prompt)
        self.db.insert(embedding, cached_response=response)

Hit rate: 31% of queries not caught by exact match.

Prefix cache reuses computation for prompts with common prefixes. System prompt is usually identical. Few-shot examples are usually identical. Only the user query changes.

Anthropic's prompt caching API handles this automatically. Mark static parts as cacheable.

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": LONG_SYSTEM_PROMPT,
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[
        {"role": "user", "content": user_query}
    ]
)

Combined hit rate: 73% of queries serve from cache. 27% hit the LLM. Cost reduced 73% from caching alone.

Intelligent Model Routing

Not every query needs GPT-4 or Claude Opus. Simple queries work fine on Haiku. Complex queries need Sonnet.

Routing strategy:

class ModelRouter:
    def route(self, query: str) -> str:
        complexity = self.calculate_complexity(query)

        if complexity < 0.3:
            return "claude-haiku-4-5"  # $0.25 per 1M tokens
        elif complexity < 0.7:
            return "claude-sonnet-4-6"  # $3 per 1M tokens
        else:
            return "claude-opus-4-6"    # $15 per 1M tokens

    def calculate_complexity(self, query: str) -> float:
        # Features: length, question marks, technical terms, etc.
        features = self.extract_features(query)
        return self.classifier.predict_proba(features)[1]

Trained a simple classifier on 10K labeled examples. "What's the capital of France?" → Haiku. "Analyze this 50 page contract for liability clauses" → Opus.

Result: 89% of queries route to Haiku. 9% to Sonnet. 2% to Opus. Average cost per query drops 88%.

Implementation Notes

Cache invalidation is the hard part. We invalidate based on TTL (1 hour default) and explicit updates. When source data changes, we flush related cache entries.

Monitoring tracks hit rates, latency, cost per query. Dashboard shows cache performance in real time. Alerts fire when hit rate drops below threshold.

Gradual rollout started with 1% of traffic. Measured cache hit rate and accuracy. Ramped to 10%, 50%, 100% over 3 weeks.

Project Deep Dive: Production RAG

We increased RAG accuracy from 52% to 89% by fixing retrieval, not the LLM.

The 6-Stage Pipeline

Stage 1: Query Processing

Don't send raw user queries to vector DB. Expand with synonyms. Extract metadata. Generate context-aware embedding.

class QueryProcessor:
    def process(self, query: str) -> ProcessedQuery:
        # Extract metadata
        metadata = {
            "date_range": self.extract_date_range(query),
            "department": self.extract_department(query),
            "doc_type": self.extract_doc_type(query)
        }

        # Expand with synonyms
        expanded = self.expand_synonyms(query)

        # Generate embedding
        embedding = self.embed_model(expanded)

        return ProcessedQuery(
            original=query,
            expanded=expanded,
            embedding=embedding,
            metadata=metadata
        )

Stage 2: Vector Database Search

Cosine similarity threshold 0.85. Top-k 50 candidates (not 5, not 10). Use Pinecone with metadata filtering.

results = index.query(
    vector=processed_query.embedding,
    top_k=50,
    filter={
        "department": processed_query.metadata["department"],
        "date": {"$gte": processed_query.metadata["date_range"][0]}
    }
)

Stage 3: Hybrid Search

Combine semantic search (70%) with keyword search (30%) using BM25.

class HybridRetriever:
    def retrieve(self, query: ProcessedQuery) -> List[Document]:
        # Vector search
        vector_results = self.vector_search(query, k=50)

        # Keyword search
        keyword_results = self.bm25_search(query.expanded, k=50)

        # Combine with weights
        combined = self.merge_results(
            vector_results, 
            keyword_results,
            vector_weight=0.7,
            keyword_weight=0.3
        )

        return combined[:50]

Stage 4: Re-ranking

This single stage improved accuracy by 23%. Use cross-encoder to score each candidate against the actual query.

class Reranker:
    def __init__(self, model_name="cross-encoder/ms-marco-MiniLM-L-12-v2"):
        self.model = CrossEncoder(model_name)

    def rerank(self, query: str, documents: List[Document]) -> List[Document]:
        # Score each doc against query
        pairs = [(query, doc.text) for doc in documents]
        scores = self.model.predict(pairs)

        # Sort by score
        ranked = sorted(
            zip(documents, scores),
            key=lambda x: x[1],
            reverse=True
        )

        return [doc for doc, score in ranked[:5]]

Top 50 candidates from hybrid search → Re-rank → Best 5 to LLM.

Stage 5: Context Assembly

Smart chunking with overlap. 512 token chunks with 50 token overlap. Include surrounding context. Add metadata.

def assemble_context(ranked_docs: List[Document]) -> str:
    context_parts = []

    for i, doc in enumerate(ranked_docs):
        context_parts.append(f"""
Source {i+1}: {doc.metadata['title']}
Date: {doc.metadata['date']}
Department: {doc.metadata['department']}

{doc.text}

---
        """)

    return "\n".join(context_parts)

Stage 6: LLM Generation

Force grounded responses. System prompt enforces citation. User query includes assembled context.

system_prompt = """You are a helpful assistant. Use ONLY the provided context to answer questions. 

If the context doesn't contain enough information, say "I don't have enough information to answer that question."

Always cite your sources using the Source number."""

user_prompt = f"""Context:
{assembled_context}

Question: {original_query}

Answer:"""

Results

Before: 52% answer accuracy. 3.8s latency. 31% hallucination rate.

After: 89% accuracy (+71%). 1.2s latency (faster!). 4% hallucination rate (-87%).

The insight: Don't optimize the LLM. Optimize the retrieval. GPT-4 with bad context = bad answers. Haiku with perfect context = great answers.

Making Projects Production Ready

Every project includes:

Comprehensive tests: Unit tests for every function. Integration tests for pipelines. End-to-end tests for workflows. 90%+ coverage.

Documentation: README with quick start. Detailed API docs. Architecture diagrams. Example notebooks.

Benchmarks: Performance metrics. Accuracy measurements. Cost comparisons. Real numbers, not claims.

Monitoring: Prometheus metrics. Logging. Error tracking. Observability built in.

Deployment: Docker containers. Kubernetes manifests. Terraform modules. Production ready deployment.

Contributing to Open Source AI

Our projects welcome contributions. Here's how to get started:

Pick a project that interests you
Read the CONTRIBUTING.md
Check the issues for "good first issue" labels
Submit a PR with tests and documentation
Respond to review feedback

We review all PRs within 48 hours. Quality bar is high but we help contributors meet it.

Conclusion

Open source AI infrastructure should be production ready, not proof of concept. These six projects represent thousands of hours of real world testing and optimization.

Use them. Contribute to them. Build on them.

The code is at github.com/anilatambharii. Documentation is comprehensive. Examples are plentiful. Issues are welcome.

Let's build better AI infrastructure together.

About the Author

Anil Prasad is Head of Engineering at Ambharii Labs, recognized as one of "100 Most Influential AI Leaders in USA 2024." He builds production-scale AI and data systems for enterprise organizations. Connect on LinkedIn at linkedin.com/in/anilsprasad or visit ambharii.com.

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