Free Lesson
Your AI Agent Failed. Can you Prove What Happened?
60 min
Oct 17, 2026 10:00 AM
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What you'll learn
Reconstruct the Agent’s Full Decision Path
Trace prompts, plans, data access, tool calls, approvals, handoffs, and actions across the agent’s full workflow.
Find the Evidence Missing From Your Logs
Find gaps in timestamps, identities, permissions, model versions, retrieved data, tool results, and human approvals.
Separate Root Cause From the Final Error
Determine whether failure began with the model, memory, data, tool, permissions, human approval, or another agent.
Build an Audit Trail You Can Defend
Define the minimum evidence needed to investigate incidents and explain what happened to auditors and leaders.
Why this topic matters
When an AI agent fails, its final output rarely tells you why. The cause may sit several steps earlier in a prompt, retrieved file, memory update, permission change, tool response, human approval, or subagent handoff. If those events were not captured, you cannot reconstruct the incident, assign accountability, or prove your controls worked. Learn the minimum evidence needed to explain an agent failure with confidence.
