DEV Community: Dhruvi

How We Debug Issues That Only Happen Once Every Few Days

Dhruvi — Fri, 15 May 2026 12:45:23 +0000

The hardest bugs are not the ones that happen constantly.

The hardest ones are:

once every few days
under unknown conditions
with no obvious pattern Especially in systems that run continuously.

Because by the time you notice the issue, the original state is already gone.

Early on, I used to approach these bugs the wrong way.

I would immediately start reading logs and trying to reproduce the issue locally.

Most of the time, that went nowhere.

Because these problems usually depend on:

timing
retries
load
specific data states
interactions between systems

Things that almost never exist in your local environment the same way.

What changed for me was realizing:

The goal is not “find the bug immediately.”

The goal is:
make the system observable enough that the bug exposes itself next time.

So instead of guessing, we start adding visibility around the problem.

Things like:

tracking state transitions
storing retry history
recording execution timing
correlating events across systems

Not permanent debugging noise.

Just enough context to reconstruct what actually happened later.

Another thing I learned:

Rare bugs are often not random.

They usually happen when multiple small conditions align:

a delayed queue
a retry arriving late
stale data
another service slowing down

Individually, nothing breaks.

Together, something weird appears for 30 seconds and disappears again.

One mistake I made a lot before:

Trying to “fix” the issue too early.

When you don’t fully understand intermittent bugs, quick fixes usually just hide the symptom temporarily.

So now I spend more time understanding:

what sequence created the issue
what state the system was in
why recovery didn’t happen automatically

Only then do we change the flow.

The interesting part is that debugging these issues slowly changes how you design systems.

You stop building only for normal operation.

You start building for investigation too.

Because eventually, every long-running system develops behaviors you didn’t predict.

At BrainPack, a lot of debugging work involves understanding interactions between systems that only fail under very specific timing conditions. The more AI workflows and automations are layered on top, the more important observability and recoverability become.

A Tool That Saves Me Time Every Single Week

Dhruvi — Mon, 11 May 2026 12:50:54 +0000

One thing that saves me an absurd amount of time is building small internal debugging endpoints.

Not dashboards.
Not full admin panels.

Just tiny routes or tools that answer very specific questions fast.

Things like:

“show the last sync status for this customer”
“replay this failed webhook”
“show all retries for this workflow”
“compare the data between these two systems”

Early on, I used to debug everything directly from logs and databases.

It worked when systems were smaller.

But once multiple services, queues, integrations, and retries are involved, simple issues start taking way too long to trace manually.

So now, whenever I notice:
“I keep checking this manually”

I usually turn it into a small internal tool.

The interesting part is that these tools are rarely complicated.

Sometimes it’s:

one endpoint
one query
one button

But removing 20 minutes of repeated investigation every day adds up fast.

Especially in systems that run continuously.

Another thing I realized:

The best internal tools are usually built by the people operating the system directly.

Because they come from real friction.

Not assumptions about what might be useful.

A lot of engineering time is lost not on fixing problems, but on figuring out where the problem actually is.

Anything that shortens that feedback loop becomes valuable very quickly.

At BrainPack, a lot of our internal tooling comes directly from operating live enterprise systems continuously. Once AI workflows and multiple integrations are involved, reducing investigation time becomes just as important as reducing failure rates.

The Hidden Cost of “Quick Fixes” in Enterprise Systems

Dhruvi — Thu, 07 May 2026 13:27:45 +0000

Most enterprise systems don’t become messy all at once.

They become messy one quick fix at a time.

A temporary script.
A manual spreadsheet.
A copied database table.
A workflow someone added “just for now.”

Individually, none of these seem dangerous.

But after a few years, the organization is running on layers of patches nobody fully understands anymore.

The problem with quick fixes is that they solve the immediate issue while quietly increasing system complexity.

And complexity compounds.

What starts as:

one workaround
becomes:
multiple duplicate processes
inconsistent data
hidden dependencies
workflows that only one person understands

At some point, nobody trusts the system anymore.

So teams create even more manual processes to compensate.

That’s usually when things start slowing down operationally.

One thing I noticed working on these systems:

The biggest cost is rarely technical debt itself.

It’s operational uncertainty.

People stop knowing:

which system is correct
what process is actually being used
whether automations can be trusted

And once trust disappears, everything becomes slower because humans start double checking everything manually.

The tricky part is that most quick fixes are not bad decisions at the time.

The business needed something fast.
The team solved the problem.
Everyone moved on.

But systems that run continuously remember every shortcut forever.

What changed how I approach this:

I stopped asking:
“does this solve the problem?”

Now the question is:
“what does this make harder six months from now?”

Because in long running systems, future complexity is usually more expensive than the original issue.

A lot of the work we do at BrainPack starts with untangling years of accumulated workarounds across existing systems. AI only becomes useful once the underlying operations are predictable enough to trust again.

Why Logging Is Not Enough When You Operate Systems Continuously

Dhruvi — Mon, 04 May 2026 17:08:59 +0000

At some point, logs stop helping.

Not because logging is bad.
Because the system is doing too much.

When you’re running something continuously, across multiple systems, logs turn into noise fast.

You still log everything.
You just can’t rely on it to understand what’s actually happening.

The expectation

Early on, logging feels like the answer.

Something breaks → check logs → find the issue → fix it

Clean. Linear. Works in small systems.

What actually happens

In production, it looks like this:

thousands of log lines per minute
multiple services writing at the same time
retries creating duplicate entries
partial failures that don’t throw clear errors

You open logs and see everything.

Which means you see nothing.

The real problem

Logs tell you what happened.

They don’t tell you:

what state the system is in
what is currently broken
what needs attention right now

And when things run continuously, that’s what you actually need.

What we started doing instead

We still log. But we stopped treating logs as the source of truth.

1. Track state, not just events

Instead of just writing logs like:

“order created”
“order failed”

We track:

current status of the order
where it is in the flow
what’s pending

So at any moment, we can answer:

what’s stuck right now

2. Surface problems, don’t search for them

Logs require you to go looking.

In real systems, you don’t have time for that.

So we build:

alerts when something is off
dashboards that show broken flows
queues that show backlog

The system tells you where to look.

3. Group by flow, not by line

Logs are isolated lines.

But real issues happen across a sequence.

So we group things by:

request
entity
workflow

Instead of reading 100 lines, you follow one story.

That’s where things start making sense again.

4. Accept that some issues won’t be obvious

Some problems don’t throw errors.

They just… stop moving.

A process gets stuck.
A sync silently fails.

Logs might show nothing critical.

So you need signals like:

time thresholds
missing updates
“this should have finished by now”

What changed for me

I used to think:

if it’s logged, we can debug it

Now I think:

if we need logs to notice something is broken, we’re already late

Logs are for digging deeper.

Not for discovering the problem.

In systems that run all the time, you don’t watch everything manually.

The system needs to show you where it’s struggling.

Otherwise, you’re just scrolling and hoping you notice the right line.

This is something we run into a lot at BrainPack, where multiple systems are always moving and interacting. AI workflows depend on knowing the current state of everything, not just what happened, so observability has to go beyond logs.

How We Design Systems That Keep Working Even When One Part Fails

Dhruvi — Thu, 30 Apr 2026 13:14:46 +0000

In real systems, something is always failing.

An API times out.
A database slows down.
A third-party service returns garbage.

If your system depends on everything working perfectly, it won’t last long in production.

So the goal is not preventing failure.

It’s designing so failure doesn’t break everything.

The wrong assumption

A lot of systems are built like this:

Step 1 → Step 2 → Step 3 → Done

If Step 2 fails, the whole flow stops.

In controlled environments, this works.

In production, it creates fragile systems that break on the first issue.

What we do instead

We design flows that can survive failure and continue.

Not perfectly. But safely.

1. Break the dependency chain

Instead of one long synchronous flow, we split things into independent steps.

Each step:

does one thing
stores its state
can be retried

So if something fails, you don’t lose everything.

You just retry that part.

## 2. Accept partial success

This one is uncomfortable at first.

Sometimes:

part of the system succeeds
another part fails

Instead of rolling everything back, we:

keep what succeeded
fix what failed

Because in distributed systems, “all or nothing” is rarely realistic.

3. Make retries safe

Failures lead to retries.

Retries lead to duplication if you’re not careful.

So every step needs to be safe to run again:

no duplicate records
no repeated side effects
no broken state

If retries are safe, failure becomes manageable.

4. Isolate external dependencies

Anything outside your control will fail eventually.

So we isolate them:

queues between systems
timeouts and fallbacks
delayed execution when needed The goal is simple

If one system goes down, everything else should keep moving.

5. Design for recovery, not perfection

Instead of asking:

how do we make this never fail

We ask:

how does this recover when it fails

That changes everything.

You stop chasing edge cases and start building systems that handle them naturally.

What changed for me

I stopped treating failure as an exception.

Now it’s part of the normal flow.

Every system I build assumes:

something will fail
it will fail at the wrong time
and it will fail more than once

So the system needs to absorb that without collapsing.

In systems that run continuously, reliability doesn’t come from everything working.

It comes from everything being able to keep going when something doesn’t.

This is something we deal with constantly at BrainPack, designing systems that keep operating even when parts of the infrastructure fail. AI workflows only work if the underlying systems can recover and continue without breaking the overall flow.

What Actually Breaks When You Connect AI to Real Enterprise Data

Dhruvi — Mon, 27 Apr 2026 13:15:43 +0000

Connecting AI to real enterprise data sounds straightforward.

Give it access to your systems.
Let it read data.
Let it take actions.

In reality, this is where things start breaking.

Not because the AI is wrong.
Because the data and systems underneath are not stable enough.

The assumption that fails

Most people assume:

if the data exists, AI can use it

In real systems, data exists in inconsistent states.

Same entity
different systems
different values

An order might be:

completed in one system
pending in another
duplicated somewhere else

AI doesn’t know which one is “correct”. It just sees all of them.

1. Inconsistent data

Enterprise systems are rarely in sync.

You have:

ERPs
CRMs
spreadsheets
custom tools

Each one updates at different times. Some fail silently.

So when AI queries across them, it gets conflicting answers.

This leads to:

wrong insights
incorrect decisions
broken automations

The issue isn’t AI accuracy.
It’s data consistency.

2. Missing context

AI works on what it can see.

But a lot of enterprise logic lives outside the data:

manual processes
unwritten rules
team-specific workflows

Example:
A record looks valid in the system.
But internally, everyone knows it shouldn’t be processed yet.

AI has no way to infer that unless the logic is formalized.

So it acts on incomplete understanding.

3. Unreliable actions

Reading data is one problem. Acting on it is another.

When AI triggers actions:

create orders
update records
send communications

It depends on underlying systems behaving predictably.

But those systems:

retry
timeout
partially fail

Without safeguards, AI actions can:

execute twice
fail halfway
create inconsistent states

4. Timing issues

Enterprise systems are not real-time in a clean way.

There are delays:

sync jobs
queues
batch updates

AI might:

read data before it’s updated
act on stale information
trigger workflows too early

Everything looks correct individually.
But the sequence is wrong.

What changed for me

I stopped thinking of AI as the hard part.

The hard part is making the environment predictable enough for AI to operate.

You need:

consistent data
clear state
reliable execution

Without that, AI just amplifies existing problems faster.

The shift

AI doesn’t fix messy systems.

It exposes them.

If your data is inconsistent, AI will surface conflicting answers.
If your workflows are fragile, AI will break them faster.

This is the kind of problem we deal with constantly at BrainPack, turning fragmented and inconsistent systems into something AI can actually operate on. The AI layer only works once the underlying infrastructure becomes predictable enough to trust.

The Code Pattern That Keeps Our Integrations Stable in Production

Dhruvi — Thu, 23 Apr 2026 16:31:30 +0000

When you connect real systems, ERPs, APIs, AI workflows, things don’t behave cleanly.

Requests retry.
Webhooks get sent twice.
Sometimes something succeeds, but you don’t get the response.

And then you see it:

duplicate orders
repeated emails
workflows triggering twice

This is normal in production.

The pattern that keeps this under control is idempotency.

The rule

Every action should be safe to run more than once.

Same input → same result.

If the same request hits your system twice, nothing should break and nothing extra should happen.

Where things usually go wrong

1. Partial execution
Something starts, then crashes halfway.
A retry comes in and runs everything again.

If you’re not careful, you create duplicates.

So instead of “just create”, you always check:

does this already exist?
should I update instead?

2. Multi-step flows
Most integrations don’t stop at one system.

You might:

create something in one system
then send it to another

If it fails in the middle, the retry should continue from where it stopped, not start from zero.

3. Side effects
This is where it gets visible.

Things like:

sending emails
charging payments
triggering automations

If these run twice, users notice immediately.

So you need to control when they run and make sure they don’t fire again on retries.

What changed for me

I stopped assuming things run once.

Now I assume:

everything can retry
everything can duplicate
things can fail halfway

So the question is always:

what happens if this runs again?

In systems that run all the time, this isn’t an edge case.

This is how the system behaves every day.

And once you build with that in mind, a lot of production issues just stop showing up.

This is the kind of problem we deal with constantly at BrainPack, making unpredictable systems stable enough to layer AI on top of them. If the underlying operations are not reliable under retries, nothing built above them can be trusted.