Incident Analysis for AI Agents

AI agents need specialized incident reporting systems: Current incident reporting processes rely on publicly available information, but understanding AI agent incidents requires access to sensitive data like the agent's internal reasoning (chain of thought) and activity logs—information that existing systems don't capture or preserve.

Three distinct categories of problems can cause AI agent incidents: The framework identifies system-related factors (such as problematic training data), contextual factors (like prompt injection attacks designed to manipulate agents), and cognitive factors (such as agents misunderstanding user requests), suggesting that preventing incidents requires addressing multiple different types of vulnerabilities.

Developers and deployers must retain specific information for investigation: The paper recommends that organizations keep activity logs, system documentation, and information about the tools agents use available to incident investigators, enabling better understanding of what went wrong when harmful events occur.

Transparency and access are critical for safety: While some incident information is sensitive, the framework argues that developers and deployers should make relevant data available to investigators upon request—balancing privacy concerns with the need to learn from incidents and prevent future harms.

Proactive incident analysis can prevent future AI agent harms: By establishing structured processes to analyze incidents involving prompt injection attacks, unauthorized actions, or data exfiltration, organizations can identify patterns and implement safeguards before similar problems occur at scale.