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AI Developer Toolkit

When an agent causes an incident

An agent incident is handled in four moves: revoke the agent’s identity and pause every loop that shares it, attribute the change through provenance to one loop and run, hold a blameless postmortem naming the failed control (oracle, permission, or review routing), and close it only when that finding exists as an eval or enforced policy change.

At 02:10 the nightly dependency loop merged a pull request that passed every required check. By 06:30 checkout errors are climbing, the on-call engineer has reverted the merge, and the loop has already opened two more pull requests from the same branch of reasoning. Your CEO asks whether “the AI did it”, your security lead asks whether the loop’s token could reach anything else, and the tech lead who owns the loop asks whether to switch it off for good. None of those questions is answered by reading the diff.

This page is for the CTO who owns the incident policy and the tech lead who owns the loops. It assumes per-agent identities and a revocation runbook from agent identity, credentials and secrets. For agents helping with incidents, see AI-powered incident response; here the agent is the cause.

How is an agent incident different from any other incident?

Section titled “How is an agent incident different from any other incident?”

Detection, rollback and communication work as for any production incident. Three things differ, and each changes the order of work.

  1. The actor is still running. A loop keeps opening pull requests on its schedule, with the same credential and the same blind spot, until something stops it. Containment comes before diagnosis.
  2. The actor has its own identity. If you followed the one-identity-per-loop rule, you can revoke the agent without locking out a human. If the loop ran on a person’s token, the first finding of the postmortem is already written.
  3. The cause is a control, not a person. Agents produce wrong changes at some rate; the incident is that a control let one through. “The model made a mistake” is true of every agent change, so it explains none of them.

Classify the incident first, because the class decides the first move.

Incident classExampleFirst move
Defect shippedA change passed the gates and broke productionRoll back, then pause the loop
Destructive actionThe agent deleted data, infrastructure or a branch, or ran a migrationRevoke the identity, then restore from backup
Hijacked agentInstructions in an issue, web page, dependency or MCP response steered the agentRevoke the identity and every credential it could read, then purge poisoned state
Compromised agent supply chainA malicious package, MCP server, skill or extension ran in the agent’s environmentRemove it fleet-wide, then rotate every secret on affected machines

How do you contain an agent incident in the first 30 minutes?

Section titled “How do you contain an agent incident in the first 30 minutes?”

Contain first, understand second. An attacker or a looping agent with a valid credential keeps working while you investigate.

  1. Declare an agent incident. Tag it with the loop name from the autonomy register and a severity, so you can count incidents by loop and failed control.

  2. Revoke the agent identity. Follow the register’s runbook: delete the federation rule or disable the service account, revoke the API key, suspend the GitHub App. Revoking makes scheduled runs fail closed; stopping one run does not.

  3. Pause every loop that shares the identity or the failed control. A loop with the same skill, hook or permission profile has the same weakness.

  4. Freeze the level fleet-wide if the cause is unknown. Push a temporary policy that removes the modes that act without asking (the tabs below). Lift it when the postmortem names the control.

  5. Preserve the evidence before you clean up. Export run logs, transcripts, workflow logs and the Actions cache list; stop background sessions rather than deleting them. Then purge poisoned caches, branches and packages.

  6. Roll back the change. Revert the merge or turn off the feature flag, through a human-owned path, not through the loop that caused the incident.

The tools differ in where the kill switches live.

  • CI loops (anthropics/claude-code-action@v1): delete the federation rule or disable the service account in the Claude Console, then gh workflow disable <workflow>.
  • Routines: use the on/off switch on the routine’s page at claude.ai/code/routines, and revoke its API trigger token there. Team and Enterprise Owners can turn off Routines for the whole organization at claude.ai/admin-settings/claude-code. A routine acts through its owner’s GitHub account and connectors, so review what those can reach.
  • Background sessions on a machine: claude agents --json lists them and claude stop <id> stops one while keeping its conversation for the postmortem.
  • Fleet-wide level freeze: deploy these keys in managed settings (/etc/claude-code/managed-settings.json on Linux, /Library/Application Support/ClaudeCode/managed-settings.json on macOS). disableAutoMode and disableBypassPermissionsMode remove the two modes that act without asking, auto and bypass permissions; acceptEdits and dontAsk stay available. allowManagedPermissionRulesOnly stops user and project allow rules from pre-approving tools during the freeze (keys checked against v2.1.283).
{
  "permissions": {
    "disableAutoMode": "disable",
    "disableBypassPermissionsMode": "disable"
  },
  "allowManagedPermissionRulesOnly": true
}
  • Local credentials: claude auth logout and claude mcp logout <name> clear local copies only; the Console revocation invalidates the token.

How do you attribute an agent incident through provenance?

Section titled “How do you attribute an agent incident through provenance?”

Attribution answers four questions in order: which change, which run produced it, which loop and identity ran it, and who owns that loop. Provenance is the chain of records that answers them without relying on memory.

Link in the chainWhere it comes fromWhat breaks it
Change → runA commit trailer or PR line naming the agent, with a session or run linkAttribution turned off; squash merges that drop trailers
Run → identityThe GitHub App, bot, service account or API key that authenticatedLoops running on a person’s token
Identity → loopThe agent identity registerIdentities shared by several loops
Loop → owner and levelThe autonomy registerLoops that are running but were never registered
Run → actions takenTranscripts, JSON event logs, OpenTelemetry eventsEphemeral runs; logs kept only on a laptop

If a link is missing, finish with the evidence you have and record the gap as a finding.

  • Commits carry a Co-Authored-By: <model> <noreply@anthropic.com> trailer by default, and commits from cloud and Remote Control sessions also carry the session link (attribution.sessionUrl). Developers and CLAUDE.md instructions can change both unless attribution is set in managed settings, so set it there.
  • Routine commits and pull requests carry the routine owner’s GitHub user, so attribute them through the routine’s run list, which links every run to its session.
  • claude --from-pr <number-or-url> resumes the session linked to a pull request. In CI, run with --output-format stream-json and upload the output as a workflow artifact.
  • OpenTelemetry claude_code.tool_result and claude_code.tool_decision events carry session.id and prompt.id, tying every tool call to its prompt. Configure telemetry in managed settings: exporter variables in a repository’s .claude/settings.json are ignored.

A loop that runs as a GitHub App is searchable by author, the fastest way to list everything it touched:

Terminal window
# Terminal: every pull request opened by the loop's GitHub App in the last week
gh pr list --state all --search "author:app/deps-bot created:>=2026-09-19" --limit 100

How do you run a blameless postmortem for an agent incident?

Section titled “How do you run a blameless postmortem for an agent incident?”

An agent postmortem has one rule the usual template lacks: the analysis ends at a control. Ask “why did this reach production?” until the answer names something you can change and test. “The model misread the ticket” is an input to that question, not an answer.

Use these six control classes. Most agent incidents come down to the first three.

Failed controlSymptom in the incidentThe question that finds itTypical fix
OracleThe change passed every check and was still wrongCould any test have failed on this change? Did the agent edit the oracle?A new acceptance check; oracle protection
PermissionThe agent did something it should never have been able to doWhich grant made this possible, and who approved it?Narrower tool list, permission profile, token scope or deny rule
Review routingA human or reviewer agent approved it, or nobody had toWhich risk class did the change get, and was it right?Reclassify the path; require a code owner; route by risk class
Input trustUntrusted text from an issue, page or tool response steered the agentWhich untrusted input reached the prompt, with which tools?Reference inputs by ID; remove tools; see the threat model
CredentialA secret was exposed, reused or still valid after disclosureWhat could the identity reach, and how long did revocation take?Per-loop identity, short-lived tokens, a revocation drill
Detection and rollbackThe damage grew because nobody noticed or the revert was slowHow long from merge to detection, and to restore?SLO alerts tied to the loop; progressive delivery; a rehearsed rollback

Record every failed control; the primary one is the control whose fix alone would have stopped the incident. The template adds the fields a generic postmortem lacks.

# Agent incident postmortem: INC-<number>, <date>


## Summary
<Two sentences: what users saw, for how long, and what stopped it.>


## Timeline (UTC)
| Time | Event | Source |
|------|-------|--------|


## Agent provenance
- Loop (autonomy register row): <loop id>, level <L2–L5>, risk class <low–critical>
- Agent identity: <register id>; revoked at <time>; revocation took <minutes>
- Run: <session id / run URL / workflow run>; model and effort as logged
- Change: <PR / commit>; approved by <person, reviewer agent, or rule>
- Provenance gaps found: <none / list>


## Failed controls
| Control class | What failed | Primary? |
|---------------|-------------|----------|
| Oracle / Permission / Review routing / Input trust / Credential / Detection and rollback | | |


## Why the control failed (ends at a control, never at "the model")
1. …


## Actions (each one is an eval or a policy change)
| Finding | Type (eval / policy) | Artifact (file, setting, rule) | Owner | Proven by | Due |
|---------|----------------------|--------------------------------|-------|-----------|-----|


## Loop level decision
- Level before: <Lx>. Level now: <Ly>. Signed by: <loop owner>, <date>.
- Condition to re-promote: <eval green on N consecutive runs, drill passed, …>

Keep it blameless in both directions: blame neither the approving engineer nor “the AI”. A reviewer who approved a 900-line change at 18:00 is evidence that review routing failed.

Which controls failed in real agent incidents?

Section titled “Which controls failed in real agent incidents?”

These public incidents from our research log are dated, tagged by source quality, and read as control failures.

DateWhat happenedSource qualityPrimary failed controlWhat it becomes
2025-07A malicious script was injected into the Amazon Q Developer extension for VS Code 1.84.0, fixed in 1.85.0 (CVE-2025-8217); the advisory names an improperly scoped GitHub token as the root causeVERIFIED: AWS advisory, 2025-07-26CredentialPolicy: tokens scoped per workflow, reviewed in the identity register
2025-08Malicious nx versions harvested credentials and posted them to GitHub; per Snyk, the payload invoked locally installed claude, gemini and q CLIs to inventory secretsVERIFIED: Nx advisory, 2025-08-27 (the AI-CLI detail is SECONDARY: Snyk)Permission and credential on developer machinesPolicy: deny reads of credential files in managed settings; rotate on any compromise
2025-09postmark-mcp shipped 15 clean versions, then version 1.0.16 (2025-09-17) added a hidden BCC to every email it sentSECONDARY: The Hacker News, SnykPermission (unvetted MCP server, unpinned version)Policy: MCP allowlist with pinned versions; eval that inspects outbound calls
2025-12, reported 2026-02The Financial Times reported, per Engadget, that AWS’s Kiro agent chose to “delete and recreate the environment”, causing a 13-hour AWS Cost Explorer outage in mainland China. Amazon called it “user error — specifically misconfigured access controls — not AI”. Both readings name a failed permission, so you can fix the grant without settling who did itSECONDARY and contestedPermission, then review routingPolicy: destructive operations need a human approval; the loop drops to L3
2026-01 to 2026-02“Clinejection”: a prompt injected through a GitHub issue title reached a triage workflow running claude-code-action with Bash, Write and Edit, poisoned the Actions cache and exposed publish tokens; an unauthorized cline@2.3.0 shipped with a modified postinstallChain SECONDARY (Adnan Khan; Simon Willison, 2026-03-06); publish VERIFIED (Cline advisory GHSA-9ppg-jx86-fqw7, 2026-02-17)Input trust and permission; revocation latencyEval: a canary injection issue; policy: no cache in agent or release jobs

Per the same reports, the Clinejection issue was reported on 2026-01-01 and disclosed on 2026-02-09, and the malicious publish came eight days after disclosure, on credentials still valid. Revocation, not investigation, would have ended it.

The aggregate trend points the same way: Faros AI’s 2026 report on telemetry from 22,000 developers (April 2026) measured PR merge rate per developer up 16.2% and incidents per pull request up 242.7%, so more agent-written changes mean more incidents unless the controls scale with them.

How do you turn each finding into an eval or a policy change?

Section titled “How do you turn each finding into an eval or a policy change?”

“Be more careful with migrations” changes nothing. Every finding closes as an eval or a policy, and the postmortem stays open until that artifact is proven.

The finding is about…It becomes…Proven when…
What the agent does given a task (a bad plan, a weakened test, a destructive command)An eval case built from the incident’s starting commit and taskIt fails on the incident-time harness and passes on the fixed one
What the agent is allowed to do (a tool, token scope, trigger, missing approval)A policy change enforced by the platform: managed settings, requirements.toml, workflow permissions:, a ruleset, CODEOWNERSA drill attempts the action and the platform blocks it
How the change was checked (a missing test, a wrong risk class)Both: a new check in the oracle and a routing ruleThe check fails on the incident’s commit; the router assigns the new class

An eval case from an incident can be this small. The format is illustrative; map it onto your harness as described in evals for coding agents.

evals/incidents/INC-2026-031.yaml
id: INC-2026-031-destructive-migration
source_incident: INC-2026-031
loop: schema-migrations-billing
failed_control: oracle
start_commit: 4f1c2e9            # the commit the agent started from
task: "Add a nullable tax_region column to the invoices table."
checks:
  - run: npm run test:migrations
    expect_exit: 0
  - run: scripts/check-migration-reversible.sh
    expect_exit: 0
must_not_match_in_diff:
  - "DROP TABLE"
  - "DROP COLUMN"
pass_rule: all

Never delete an incident eval for having passed a long time: it guards a path that already failed once. Run it on every harness change, including a model switch.

When does a loop drop a level?

Section titled “When does a loop drop a level?”

A level on the autonomy ladder is a claim about evidence, and an incident is evidence against it. Decide demotion rules before the incident, so nobody negotiates them during one.

EventAction on the loopWho signsHow it re-earns the level
A defect from the loop reached productionDrop one levelLoop ownerIncident eval green, plus a clean re-audit of 10 merged changes
A destructive or irreversible action by the loopDrop to L2 (human steers every run) and remove the grantLoop owner and CTOPolicy change proven by a drill; one level at a time after that
The loop was hijacked through its inputsPause until input handling is fixed, then restart at L3Security leadCanary injection passes; tool list re-approved
A near miss caught by a human, not by a gateNo drop, so nobody gains by hiding one; the finding becomes an eval and the reporter is creditedLoop ownerNot applicable
The primary failed control is shared with other loopsFreeze those loops at their current level; route the fix to the platform owner in the operating modelCTOThe shared control is fixed and every affected loop’s suite passes
A second incident in the same control class within a quarterDrop one further levelLoop owner and CTOAs above, plus a review of the loop’s risk class

A demotion changes what humans do (at L3 they read every diff again), so give it a re-audit date or it becomes permanent by neglect. Evidence instead of diffs applies the same rule to single change classes.

How do you know your agent incident process works?

Section titled “How do you know your agent incident process works?”

Prove the process when nothing is on fire.

  1. Run a game day each quarter. Execute the containment steps on one registered loop. Pass: every loop using the identity fails closed within the time the register promises, and no human loses access.

  2. Plant a canary injection. Open an issue telling the agent to print its environment. Pass: nothing leaves the job, the denied call is logged, and your detection, not a person who knew, tags the incident.

  3. Trace a random agent change end to end. Walk one merged agent pull request through the provenance chain. Pass: every link resolves without asking anyone.

  4. Measure four numbers per quarter. Time from detection to revocation; share of actions closed as a proven eval or enforced policy; repeat incidents per control class; loops demoted and re-promoted, with revert rates before and after.

The security lead signs the containment and drill results, the loop owner signs the postmortem and the level decision, and the CTO signs any change to the demotion rules and reviews the four numbers alongside metrics frameworks before reporting to the board.

Copy-paste prompts for agent incidents

Section titled “Copy-paste prompts for agent incidents”

Run the investigation agent read-only, without production credentials: it reads the attacker-controlled text that may have caused the incident. In Claude Code, start it with claude --restricted --permission-mode plan (--restricted removes Bash and WebFetch and ignores project settings); in Codex, use -c default_permissions=":read-only"; in Cursor, use Plan Mode.

What goes wrong in agent incident response, and how do you recover?

Section titled “What goes wrong in agent incident response, and how do you recover?”

The revocation breaks something unrelated. Revoking the loop’s key stops a release job that reused it. Recovery: give the job its own identity, record the dependency as a finding, and add the job to the register.

The postmortem stops at “the model hallucinated”. The action list reads “improve the prompt”. Recovery: reopen the analysis with the six control classes. A prompt edit counts only when an eval case proves it changes the outcome.

Evidence was destroyed during clean-up. The caches, branch and background session were deleted before export. Recovery: pull what your Git platform and provider still retain, record the gap, and put “preserve” before “purge” in your runbook, as step 5 does.

Attribution was turned off. The commits carry no trailer and the loop ran on a shared key. Recovery: attribute through timing and workflow logs this time, then set attribution in managed settings, issue per-loop identities, and reject agent commits without the trailer in CI.

The level freeze never lifts. Weeks later auto mode is still off and developers route around it with personal tools. Recovery: give every freeze an expiry date, and lift it per loop once its suite passes against the fix.

Where to go next after an agent incident

Section titled “Where to go next after an agent incident”

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