Agent Incident Response: Stop, Investigate, Reconcile and Recover

Evidence check: 25 October 2026. Product details reflect the referenced documentation observed at this cutoff; exact plans, prices, regional eligibility and unverified release dates are not claimed. This is an editorial guide, not a hands-on test.

Classify the incident by side effect

First ask whether the system only generated text or actually called a tool. Next determine whether the tool read data, attempted a write, or committed an external action. A hallucinated answer is an information-quality problem; a tool denial may indicate attempted prompt injection; a confirmed export or payment demands containment and ordinary security incident procedures.

Contain without destroying evidence

Disable the affected workflow and revoke only the credentials or tool permissions necessary to stop further harm. Preserve immutable run identifiers, sanitized traces, approval decisions and remote transaction references. A user-visible kill switch should stop future actions and mark in-flight work as cancelled, but cancellation does not guarantee a remote request already sent can be reversed.

Reconstruct the causal chain

Trace from user request to retrieved content, model turn, proposed tool call, permission decision, approval and external response. Check for duplicated retries or misleading status messages. Inspect malicious document instructions as untrusted artifacts. Compare the intended approved payload with what the external service actually received; do not rely only on the model’s reconstructed story.

Reconcile and repair

For every indeterminate write, read the authoritative system of record. If a transaction completed, document the effect and decide whether a new compensating action requires user approval. If no write completed, carefully unblock the task with a new operation identifier. Correct false final messages and notify affected users through an authorized channel.

Prevent recurrence

Reproduce the scenario in a test environment and add it to a regression corpus. Reduce unnecessary tool permissions, improve schema validation or isolate untrusted retrieval. Update the workflow runbook, not just its system prompt. Add alerts for new policy denials, unusual destinations, repeated retries and unexpectedly high data volume.

What observability tools contribute

Agent traces such as those documented by OpenAI and Phoenix help show model and tool execution sequences. They are investigative data, not a guarantee of completeness or compliance. Audit events should be written independently where possible so a failed model trace export cannot erase a critical permission decision.

Runbook for a suspicious action

If an agent unexpectedly attempted to change a customer account, disable its write capability and pause the affected workflow before examining logs. Preserve a minimal, access-controlled evidence set: event ID, caller identity, tool request, gateway decision, timestamp, workflow release and external state. Determine whether the attempt was denied or whether a mutation occurred. Notify the appropriate service owner before assuming the model’s apology indicates remediation.

Recovery decisions

For an accidental duplicate notification, identify recipients and message IDs; do not blindly send another correction. For a financial write, use an authorized reconciliation path rather than a model-generated reversal. Rotate any exposed tokens, invalidate poisoned retrieval material and patch tool permissions. After resolution, convert the trigger into a regression case with redacted fixtures. Review why existing approval and monitoring controls did not prevent or surface the issue. Regulatory reporting requirements vary by jurisdiction and incident type and require qualified advice.