AI Agent Incident Response and Recovery Architecture

Agentic AI systems can plan, invoke tools, access enterprise data, delegate tasks, and execute transactions at machine speed. These capabilities create incident patterns that differ from conventional application or cybersecurity events because a compromised, misconfigured, or unsafe agent may continue to reason and act while propagating effects across systems. This paper proposes an AI Agent Incident Response and Recovery Architecture that integrates detection, triage, containment, identity revocation, tool isolation, evidence preservation, rollback, human escalation, recovery validation, and post-incident learning. The architecture defines incident severity, telemetry requirements, forensic evidence, kill-switch patterns, bounded recovery, safe fallback, and business continuity controls. It also addresses multi-agent coordination, prompt injection, unauthorized actions, data leakage, policy bypass, and model or tool failures. The proposed framework enables enterprises to respond to agentic AI incidents with clear accountability, rapid containment, auditable evidence, and resilient restoration of business services.