Incident Response for AI Agent Failures

AI Agent Incidents Are Different

Traditional incident response assumes a human actor: someone clicked a phishing link, someone misconfigured a firewall, someone pushed vulnerable code. The response playbook follows a familiar pattern: detect the incident, contain the blast radius, investigate root cause, remediate, and conduct a post-mortem.

AI agent incidents follow the same general pattern, but with critical differences. First, agents can cause damage much faster than humans. A human might misconfigure one firewall rule. An agent can misconfigure every firewall rule in the account in seconds. Second, agents do not have intent in the human sense. An agent did not "decide" to do something malicious. It followed its programming, which might have been incorrect, or it operated outside its governance boundaries. Third, the investigation is different because you are debugging a system, not interviewing a person.

Here is the incident response playbook for AI agent failures.

Phase 1: Detection

Detection is the time between when an AI agent incident occurs and when someone knows about it. For traditional security incidents, detection is often measured in days or weeks. For AI agent incidents, detection must be measured in seconds or minutes, because the damage compounds rapidly.

Intended provides three detection mechanisms.

**Real-time policy violations.** When the Authority Engine denies an intent, that denial is a detection signal. If denials spike for a particular agent, something is wrong. The agent is attempting actions it is not authorized to perform, which indicates either a malfunction, a compromise, or a policy misconfiguration.

**Anomaly detection.** Intended monitors agent behavior against established baselines. When an agent's behavior deviates significantly -- action volume spikes, new action types appear, timing patterns change -- an anomaly alert is triggered. Anomaly detection catches incidents that policy evaluation misses: the agent is performing authorized actions, but the pattern of actions is abnormal.

**Execution failures.** When a token verification fails at the execution perimeter, something is wrong. Either the agent is presenting an invalid token (compromised or malfunctioning), or the execution environment has changed since the token was issued. Execution failures are logged and alerted in real time.

The goal is to detect incidents before they cause significant damage. With governance instrumentation, detection time drops from days to minutes.

Phase 2: Containment

Once an incident is detected, the first priority is containment: stopping the agent from causing further damage.

**Immediate containment.** Revoke the agent's API credentials. In Intended, this is a single API call or console action. Once the agent's credentials are revoked, it can no longer submit intents to the Authority Engine, and any outstanding authority tokens become unverifiable (because the agent identity check fails).

**Scope assessment.** Before pursuing root cause, assess the blast radius. Use Intended's audit ledger to answer: what actions did this agent perform since the incident began? What resources were affected? What data was accessed or modified? The hash-chained audit provides a complete, tamper-evident record.

**Network containment.** If the agent has direct network access to production systems (outside of Intended's governance), additional containment may be required: revoking the agent's network credentials, updating firewall rules, or disabling the agent's service account in the target systems.

**Communication.** Notify the incident response team, the agent's owning team, and any stakeholders affected by the compromised resources. Use your organization's standard incident communication channels.

Containment should be fast and decisive. It is better to over-contain (temporarily disabling a healthy agent) than to under-contain (allowing a compromised agent to continue operating). You can re-enable the agent after investigation confirms it is safe.

Phase 3: Investigation

With the agent contained, the investigation begins. The goal is to determine what happened, why it happened, and what the total impact is.

**Timeline reconstruction.** Use Intended's audit ledger to reconstruct a complete timeline of the agent's actions. The hash-chained structure ensures the timeline is tamper-evident. Export the relevant audit records and arrange them chronologically.

Look for the transition point: the moment when the agent's behavior changed from normal to abnormal. Was it a sudden change (suggesting a compromise or deployment) or a gradual drift (suggesting a misconfiguration or model degradation)?

**Root cause categories.** AI agent incidents typically fall into one of five root cause categories.

Misconfiguration: the agent was configured incorrectly, either in its own parameters or in its Intended policies. The agent did exactly what it was told to do; it was just told to do the wrong thing.

Model degradation: the agent's underlying AI model produced unexpected outputs. This is common when models are updated or when the agent encounters inputs outside its training distribution.

Compromise: the agent's credentials were stolen or the agent's code was modified by a malicious actor. This is the scenario that most closely resembles a traditional security incident.

Integration failure: the connector or target system behaved unexpectedly, and the agent responded incorrectly. API changes, schema changes, or service outages can trigger unexpected agent behavior.

Policy gap: the agent's actions were not covered by existing governance policies. The agent was operating in an ungoverned space, and its behavior happened to be harmful.

**Impact assessment.** For each action the agent performed during the incident window, assess the impact. Was data exposed? Was infrastructure modified? Were financial transactions processed? Were customer-facing systems affected?

Intended's evidence bundles provide the detail needed for impact assessment. Each bundle includes the full intent, the resource identifier, the action type, and the execution outcome. Cross-reference with target system logs for additional detail.

Phase 4: Remediation

Remediation addresses both the immediate incident and the underlying conditions that allowed it to happen.

**Immediate remediation.** Undo the damage, where possible. Roll back infrastructure changes. Restore modified data from backups. Revoke exposed credentials. Notify affected customers if data was exposed.

For irreversible actions (data already sent externally, financial transactions already settled), document the impact and initiate appropriate disclosure and recovery processes.

**Policy remediation.** Update Intended policies to prevent the same class of incident from recurring. If the incident was caused by a policy gap, write new policies. If it was caused by a policy misconfiguration, correct the configuration. If it was caused by overly permissive policies, tighten them.

Test the policy changes against the incident's audit trail. Replay the agent's actions against the updated policies and verify that the policies would have caught the problematic actions.

**Agent remediation.** If the incident was caused by agent misconfiguration, fix the configuration. If it was caused by model degradation, retrain or rollback the model. If it was caused by compromise, rebuild the agent from trusted sources and issue new credentials.

Before re-enabling the agent, run it through a governance dry-run: submit its typical actions to the Authority Engine in evaluation-only mode (no token issuance) and verify that the policies produce the expected outcomes.

Phase 5: Post-Mortem

Every AI agent incident deserves a post-mortem. The post-mortem should be blameless (focused on systems, not people), thorough (covering detection, containment, investigation, and remediation), and actionable (producing specific, assigned follow-up items).

Key questions for the post-mortem: How long was the detection time? Could it be shorter? Was containment effective? Did the agent cause additional damage after detection? Was the audit trail sufficient for investigation? Were there gaps? Were the governance policies adequate? What policy changes are needed? Is the remediation durable? Will the same class of incident be prevented going forward?

The post-mortem output should include a timeline of events, root cause analysis, impact assessment, remediation actions taken, and follow-up items with owners and deadlines.

Building the Muscle

Incident response is a skill that improves with practice. Run tabletop exercises for AI agent failure scenarios. Simulate compromised agents, policy gaps, and model degradation events. Test your detection, containment, and investigation procedures before you need them in a real incident.

Organizations that treat AI agent incident response as a first-class operational capability will recover faster, contain damage more effectively, and learn more from every incident.