AI Agent Forensics

AI agent forensics is the structured reconstruction of what an AI agent did before, during, and after a security-relevant incident. Traditional log analysis focuses on server events, user actions, and network traces. Agent forensics has to preserve additional layers: the system prompt, user instruction, retrieved context, model version, tool calls, permissions, intermediate outputs, memory state, and external data sources. The goal is not only to identify which system was affected. The key question is why the agent treated a specific action as allowed, useful, or necessary. Strong agent forensics therefore starts before the incident. Execution traces should be tamper-resistant, sensitive content must be protected, timestamps need to be consistent, and tool results have to be linked to the decisions they influenced. After an incident, these records help teams separate prompt injection, misconfiguration, excessive permissions, model error, and human process failure. Without that evidence, the response becomes guesswork: the team can shut the agent down, but it cannot confidently explain what happened. AI agent forensics makes autonomous systems inspectable, auditable, and improvable.