AI Incident Postmortem Template for Agent and Model Failures

When an agent issues a refund it should not have, answers a customer from a stale policy, or takes an action because a document told it to, the investigation needs a structure conventional software postmortems lack: the behavior came from a model reading content and choosing actions within a specification and a permission model, and the fix belongs in whichever of those failed. This template structures that investigation. It supports the incident process in the AI incident response checklist and the governance in the agentic AI governance whitepaper.

What does the template contain?

Section	Content
1. Summary	One paragraph: what happened, impact, root cause class, status
2. Impact	Customers, records, money, compliance, and trust affected, quantified
3. Timeline	Detection to closure with system versions and autonomy level
4. Trace reconstruction	What the system read, retrieved, called, decided, and produced
5. Root cause	Classified across specification, data, model, tools, controls
6. Contributing factors	What made it worse or let it persist
7. Why controls did not catch it	Gates, sampling, monitoring, permissions
8. Remediation	What was done for affected parties
9. Actions	Owner, date, verification for each
10. Evaluation updates	Cases added; thresholds changed
11. Lessons and class review	Pattern across similar incidents

How is the trace reconstructed?

From the audit trail: the trigger, the inputs and their trust labels, retrieved content with sources, each tool call with parameters and results, the model's reasoning where captured, approvals requested and granted, and the outputs. The reconstruction identifies the exact point where behavior diverged from the specification. If the trace is incomplete, that is a finding in its own right; the design that makes traces complete is in how to build an AI audit trail.

How is the root cause classified?

Class	Typical finding	Typical fix
Specification	The case was not covered; a rule was ambiguous	Spec update; golden-set cases
Data	Stale, wrong, or injected content was read	Content curation; trust labeling; injection handling
Model	A model or prompt change shifted behavior	Regression gate enforcement; rollback; routing change
Tools and integrations	A tool returned wrong data or held broad permissions	Tool fix; permission scoping; classification
Controls	A gate, sampling, or alert existed on paper only	Enforcement at the gateway; monitoring

Most incidents have one root cause and two or three contributing factors from other classes. Record all of them.

Why did the control layer not catch it?

Every AI postmortem asks this separately, because the model will always have some failure rate and the controls exist to bound its consequence. Was the action classified correctly? Was the gate enforced mechanically or by prompt? Did sampling cover this category? Did monitoring alert? Was the kill switch used promptly? Answers here produce the most durable fixes, described in the agent identity and access control whitepaper and AI agent kill switch design.

What actions should follow?

• Specification updates and new golden-dataset cases, with a regression run recorded.
• Permission and classification changes; gate additions.
• Monitoring and alert additions for the incident's signature.
• Process changes for exception handlers or approvers.
• Autonomy-level reduction where the evidence base is now in doubt, restored only through the performance review.
• Communication to affected parties and to governance.

Each action has an owner, a date, and a verification step. Completion is tracked; open actions are reviewed at the next governance meeting.

How does the postmortem feed evaluation?

The incident case, and variants of it, join the golden dataset with the correct behavior defined. Thresholds may change. The next release of the agent runs against the updated set, which is what makes the postmortem's most important promise, that this cannot recur silently, true. The loop is described in the evaluation-driven development whitepaper.

What does a short example look like?

Summary: a support agent issued a replacement order outside policy after reading a customer message that contained instructions. Impact: eleven orders over two days, low value, fully remediated. Trace: the message was ingested without a trust label; the plan changed after reading it; the replacement tool was classified reversible with sampling, not gated. Root cause: data (injected content) with contributing factors in controls (missing trust label; no confirmation on plan change). Actions: label customer messages as untrusted; require confirmation when the plan changes after reading content; add eleven cases and variants to the golden set; reduce autonomy on replacements to act-with-approval pending review. Controls question: sampling at five percent did not surface the pattern for two days; alert added on replacement rate by intent.

What are the common mistakes?

1. Blaming the model and stopping there.
2. No trace, so the reconstruction is guesswork.
3. Fix in the prompt when the cause was a permission.
4. Cases not added to the evaluation set.
5. Actions unowned and untracked.

How does FISTA Solutions help?

FISTA Solutions builds every AI agent with the trace, permissions, and evaluation loop this template depends on, runs postmortems with clients through forward deployed engineers, and installs the incident practice across teams through AI enablement. FISTA has delivered 150+ projects for 50+ companies across 12+ countries with 99.9% uptime.

To review an incident with us, message FISTA on WhatsApp, or read the AI incident response checklist for the response steps that precede the postmortem.