Trace-based evaluation is the review of an AI agent’s full action trail: prompts, tool calls, intermediate decisions, outputs, and follow-up steps. Instead of judging only the final answer, defenders inspect how the agent got there. This is especially useful for agentic systems that can browse the web, call APIs, move files, or trigger workflows.
In cybersecurity, trace-based evaluation helps detect unsafe behavior that a normal benchmark can miss. A model may look safe in a static test but still follow malicious instructions, overreach permissions, leak data, or take unauthorized actions when interacting with real tools. Security teams use traces to spot prompt injection, privilege misuse, policy violations, and poor escalation handling. The same traces also support incident analysis, because they show exactly where guardrails failed and which control should be tightened: input filtering, tool permissions, monitoring, or human approval.