When an AI coding agent accidentally deletes a production database, what do you write in the postmortem? This isn't a theoretical question—it's happening right now as teams adopt AI tools for production code.
The pattern we're seeing
Recent incidents show a clear pattern:
- Fewshell's agent that refuses to run commands without human approval (12 pts, 5 comments)
- HyperProbe's read-only debugging in production (69 pts, 51 comments)
- Nightwatch's open-source AI SRE for incident grouping (33 pts, 10 comments)
These aren't just isolated incidents. They're symptoms of a larger shift: AI agents are now touching production systems, and when things go wrong, engineering teams need a structured way to analyze what happened.
What goes into an AI agent incident postmortem
A proper AI agent incident postmortem needs to answer these questions:
1. What was the agent trying to do?
- Original prompt/goal
- Tools and permissions available
- Context provided to the agent
2. What actually happened?
- Sequence of actions taken
- Decision points where the agent went off-track
- Data accessed or modified
3. Why did it happen?
- Prompt ambiguities or misinterpretations
- Missing guardrails or constraints
- Insufficient context or understanding
4. What are we changing?
- Prompt engineering improvements
- Permission restrictions
- Additional validation steps
- Monitoring requirements
The anatomy of a blameless postmortem
A blameless approach focuses on systemic improvements rather than individual mistakes:
- Timeline: Clear sequence of events
- Technical details: What the agent actually did vs what it was supposed to do
- Root cause analysis: Why the system allowed this to happen
- Action items: Concrete changes to prevent recurrence
Example structure
## Incident Summary
- Date: [Date]
- Agent: [Name/version]
- Impact: [What broke]
- Duration: [How long it took to fix]
## Timeline
- 10:30 AM: Agent given task to [original goal]
- 10:32 AM: Agent interpreted task as [misinterpretation]
- 10:33 AM: Agent executed [problematic action]
- 10:35 AM: [Detection mechanism] identified issue
- 10:40 AM: Manual intervention stopped agent
## Root Cause Analysis
- [Specific prompt ambiguity]
- [Missing constraint in agent's instructions]
- [Insufficient validation in the agent's decision process]
## Action Items
1. [Specific change to prompts]
2. [Additional guardrail implementation]
3. [Improved monitoring for similar scenarios]
Prevention strategies
The best postmortems lead to prevention:
- Clear boundaries: Define exactly what the agent can and cannot do
- Step-by-step approval: Require human confirmation for critical actions
- Dry-run mode: Test agent behavior in a safe environment first
- Audit logging: Record all agent decisions and reasoning
- Regular reviews: Periodically assess agent performance and safety
Resources
For teams dealing with AI agent incidents, having a structured template can save hours of documentation time and ensure consistent analysis across different types of failures.
What's your experience with AI agent incidents in production? Have you found other effective approaches to postmortem documentation?
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