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AI Agent Workflow Repair

Stop agents from passing the wrong test

Trace one wrong outcome to the missing check. Fix it, rerun the same failure, then write down how it works.

SHOW US WHAT'S BROKEN

Methodology demonstration from our own platform — not a client engagement.

Approval workflow / repair case Wrong decision caught
Passed Correct
RecommendationREJECT DecisionAPPROVE JournalWRONG ACTION
Format passed Decision conflicted Wrong action blocked

AI agent workflow repair

The short answer

Nocturnal Marketing audits AI agent workflows for Colorado Springs and Front Range businesses. We repair the failure and return approval rules, a decision history, and a tested fallback the owner can rerun.

101 renderer tests passed after the approval fix
12 backend tests passed against the same decision
0 wrong journal actions written by the blocked packet
4 workflow failures reproduced and repaired

Failure patterns

Four ways a passing workflow gets the result wrong

Each case began with a workflow that cleared its checks. A business rule still failed. We traced the wrong result to its permissive step.

Showing Reject turned into approve

Control Room regression test record with exact override assertions and fresh test results
Repair evidence Inspect the full case ↗

Reject turned into approve

What should have happened
Reject is the default whenever the system recommends rejection. Approval requires a reviewer to record a clear reason for overriding it.
What actually happened
The packet said approve even though both the recommendation and rationale said reject. Because the format was valid, the contradiction nearly reached the journal.
What we changed
The renderer and backend now reload the source recommendation. Reject remains the default. Repeated, missing, or stripped override evidence blocks approval.

The handoff

What your team keeps

A repair is complete when your team can reproduce the failure and rerun the tests. The handoff also documents stop conditions and recovery.

Failure trace

One redacted record shows how the wrong result moved from input through recovery.

The fix

The smallest change that blocks the wrong result without removing the creative work the workflow does well.

Tests built to break it

We test repeated reasons, missing sources, stale packets, incomplete handoffs, then replay attempts.

What you keep

You get the runbook, logs, stop conditions, rollback steps, test command, plus the next safe change.

Proof before promises

We broke it. Fixed it. Tested it.

These failures shaped the service. Each case names the break, isolates the cause, records the repair, then shows the regression test.

Before we scope it

Frequently asked questions

What is an AI agent workflow audit?

It reproduces one wrong outcome, finds the missing check, fixes it, and reruns the same failure before handing it back.

Do you replace the entire agent?

No. The repair targets the first permissive control that allowed the wrong business result.

What does the owner receive?

The owner keeps the approval rules, a record of why each decision was made, test cases, readable logs, fallback steps, and a way to rerun the test.

The result that matters

Change the outcome, not the badge

Passing tests are evidence, not the result. The repair stops the wrong action and preserves the trace. Your team gets a safe replay path.

PASS

the packet passed its technical checks

WRONG

the workflow still chose the wrong result

BLOCK

the repaired workflow stopped the wrong action

REPLAY

your team can reproduce the failure from the record

Show us where it went wrong

Bring the workflow that passed and still failed.

Send one redacted record, the result you expected, and what actually happened. We will find the first decision worth testing and tell you whether a scoped repair is the right fit.

SHOW US WHAT'S BROKEN