AI Agent Failure Triage & Repro Builder

Stop guessing why the agent failed — and check whether it actually learned from the failure.

A failed agent run often leaves you with fragments: a tool error, a transcript, a retry loop, stale state, a permission problem, or an output that simply does not match the task.

The expensive part is not seeing that it failed. It is figuring out:

• where the failure actually occurred
• what the evidence really supports
• what signal should have changed the agent’s next action
• whether the agent adapted or simply repeated the same mistake
• who or what carries the cost if the pattern continues
• how to reproduce it safely
• what proves the correction will hold next time

AI Agent Failure Triage & Repro Builder turns a failed run into a structured debugging deliverable instead of a plausible story.

What it does

• Reconstructs the first meaningful divergence between intended and observed behavior.

• Separates direct evidence from inference and unknowns.

• Classifies the failure layer with explicit confidence.

• Identifies the correction signal the agent received from a tool, test, user, state change, or environment.

• Checks whether the next action adapted, repeated, bypassed, overcorrected, suspended, escalated, or only changed cosmetically.

• Traces where the cost of non-correction lands: extra calls, latency, compute, operator verification, downstream repair, or production risk.

• Builds the smallest safe reproduction or a reproduction plan.

• Chooses the next diagnostic action with the highest information value.

• Converts a sufficiently understood failure into a reusable regression case.

• Adds a binding test: what must behave differently on the next comparable run for the fix to count as real.

• Ends with a clear operational decision: RETRY, RECONFIGURE, FIX PROMPT, FIX TOOLING, VERIFY, ESCALATE, or STOP.

Why use it

Most debugging tools stop at

“What caused the error?”

This skill also asks

“The system received corrective information. Did that information actually change what happened next?”

That matters for AI agents because a workflow can appear self-correcting while repeatedly making the same tool call, retrying the same stale plan, or shifting verification work back to the operator.

Use it when

• an agent keeps retrying the same failed action
• a tool call breaks after a schema or API change
• the agent’s plan no longer matches tool state
• a run fails only in one environment or provider
• permissions or authentication may be blocking execution
• you need a minimal repro before filing an issue
• you want to turn an incident into a regression test
• you need to know whether a fix actually changed future behavior
• you need to decide whether to retry, fix, verify, escalate, or stop

What you get

1. Failure summary
2. Failure layer + confidence
3. Observed / inferred / unknown evidence
4. Correction signal
5. Correction response
6. Failure cost
7. Minimal reproduction
8. Next diagnostic action
9. Regression case
10. Binding test
11. Operational decision
12. What would change the diagnosis

Known limitations

This skill does not guarantee root cause from incomplete evidence, independently access systems the host agent cannot access, certify security or production readiness, or replace incident-response procedures for high-risk systems.

When the evidence is insufficient, it returns a verification path instead of inventing certainty.