Give an AI Coding Agent Your Error Monitoring Data

Giving an AI coding agent your error monitoring data turns a vague bug report into a specific one. Instead of "users say checkout sometimes fails", the agent gets a stack trace, the exact line, how many users hit it, which browser and release it started in, and what happened in the twenty seconds before. That is the difference between an agent guessing at a plausible fix and an agent fixing the actual defect.

The catch is that error monitoring payloads are full of things you should not hand over casually. This is a guide to doing it usefully without leaking your customers' data into a context window.

Start with one issue, not the whole feed

The instinct is to connect the agent to the monitoring tool and let it browse. Resist it. A live error feed is thousands of events, mostly duplicates, mostly noise, and dumping it into context buries the one thing that matters.

Export a single issue instead. One issue contains everything useful:

• The exception type and message

• The stack trace with your own frames identified

• The release or commit where it first appeared

• Event count and affected user count

• Breadcrumbs, the sequence of actions leading up to the failure

• Environment tags: runtime version, browser, OS, region

That is a complete diagnostic picture in a few kilobytes. It also has a natural boundary, which matters for the next section.

Strip these fields before the agent sees them

Error payloads collect whatever was in scope when the exception fired. That routinely includes real customer data.

The stack trace itself is almost always safe and is the part carrying the diagnostic value. The metadata around it is where the exposure lives. If you have not thought about this boundary before, the same reasoning applies to giving an agent read-only production access, where the fix is scoping rather than trust.

A practical shortcut: most monitoring tools let you configure server-side scrubbing rules. Setting those once is better than remembering to redact by hand every time, because the version you forget to redact is the one that matters.

What to include beyond the error itself

A stack trace tells the agent where the program stopped. It does not tell it what the code was supposed to do. Three additions raise the hit rate substantially.

The frequency and shape. "412 events, 38 users, started in release 4.9.2, all on Safari" is a different bug from "3 events, 3 users, spread over four months". The first is a regression with a suspect commit. The second is probably an edge case in input handling. Say which one it is.

The relevant source file, not just the path. The trace names a file and line. Paste the surrounding function. Agents reason far better with the actual code than with a path they have to go find, especially in an unfamiliar codebase. Our guide on getting an agent to explain a legacy codebase covers the broader version of this problem.

What normal looks like. If the breadcrumbs show a failed API call, include what a successful one returns. Without it the agent cannot tell whether the handler is wrong or the response is.

A prompt structure that works

Order matters. Put the constraint before the data, because the agent reads the whole thing but weights the framing.

You are diagnosing a production error. Do not propose a fix until you
have named the specific cause and cited the line that produces it.
If the trace is insufficient to determine the cause, say what
additional evidence you need.

ERROR
<exception type, message, stack trace>

IMPACT
<event count, user count, first-seen release, environment tags>

SEQUENCE
<breadcrumbs>

SOURCE
<the function containing the failing line, plus its direct callers>

EXPECTED BEHAVIOUR
<one sentence on what this code is supposed to do>

The first paragraph does most of the work. Without it, an agent handed a stack trace will produce a patch immediately, and a patch produced before a diagnosis is a guess with good syntax. Making it state the cause first gives you something you can check independently of the code it writes.

Reviewing what comes back

Two specific things to verify before merging a monitoring-driven fix.

First, that the fix addresses the cause and not the symptom. Wrapping the failing call in a try/catch makes the error stop appearing in your dashboard while leaving the defect in place. The error count going to zero is not evidence of a fix, it is evidence that the reporting stopped.

Second, that the fix does not swallow adjacent errors. A broad catch around a narrow bug hides the next three bugs that occur in the same block. Ask specifically what else that handler will now absorb.

For everything else about reading agent output critically, see our guide to AI coding tools and how to write a bug report for an AI coding agent. If the error you are chasing turns out to be a security issue rather than a defect, switch to your AI incident response plan instead of patching in place.

FAQ

Should I connect the agent directly to my monitoring tool?

Only with a read-only, scoped credential, and even then exporting one issue at a time gives better results. A live connection tends to fill the context with duplicate events rather than diagnostic detail.

Is a stack trace alone enough?

Often, for a crash with a clear cause. For anything intermittent or environment-specific, the breadcrumbs and the frequency data are what turn a guess into a diagnosis.

What about customer data in the error payload?

Assume it is there until you have checked. Emails, tokens in query strings, request bodies and captured local variables are the usual offenders. Configure server-side scrubbing in the monitoring tool so it is handled before you ever export.

How do I tell whether the fix actually worked?

Watch the event count for the specific issue across the next release, not the total error count. And confirm the fix changed behaviour rather than only changing what gets reported.