Click any item in the activity feed and the execution inspector shows the full story. What was the original request. What the employee planned. Which tools it called, in what order, with what inputs. What each tool returned. What the employee decided based on those results. And what it produced as final output. Every step is visible.

The inspector shows the actual prompts sent to the AI model and the exact responses received. If you want to understand why your employee chose to send an email to the VP of Sales instead of the Marketing Director, the inspector shows the reasoning chain. If a tool call failed and the employee retried with different parameters, you see both attempts.

This level of transparency is what separates a trustworthy AI employee from a black box. When results are good, the inspector confirms the employee followed the right process. When results are bad, the inspector pinpoints exactly where things went wrong. You debug AI work the same way you would debug a human employee's decision: by understanding their thought process.

See Every Step Your AI Agent Took, and Why

The Execution Inspector gives you a complete, step-by-step trace of every agent run. For each step, you see the tool that was called, the exact inputs passed to it, the output returned, the reasoning the agent used to decide on that step, and the time it took. Nothing is hidden.

This level of transparency is what separates trustworthy autonomous agents from black boxes. When an agent produces a result you did not expect, the Inspector shows you the exact decision path that led there. When an agent performs brilliantly, you can understand the strategy it used and replicate it across other employees.

Debug Complex Agentic Workflows Without Guesswork

Debugging AI agents is fundamentally different from debugging traditional software. The failure point is rarely a code error. It is usually a reasoning choice: the agent misinterpreted context, selected the wrong tool, or made a plausible-sounding decision that led it off course. The Inspector gives you the data to diagnose these issues precisely.

You can step through the execution chronologically, jump to specific tool calls, expand any step to see the full input and output payload, and compare the agent's stated reasoning against what it actually did. This makes it possible to identify prompt issues, tool configuration problems, or unexpected edge cases in real-world data.

For multi-agent systems, the Inspector traces the full execution graph: which agent delegated to which, what inputs were passed at each delegation boundary, and what the receiving agent did with them. Debugging a 10-agent pipeline is as tractable as debugging a single agent.

Execution Traces as a Compliance and QA Record

Beyond debugging, execution traces serve as a permanent record of what your AI employees did and how they did it. For regulated industries, this is not optional. Being able to show that an agent followed a defined process, did not access data it should not have, and produced output that can be traced back to specific inputs is a compliance requirement.

Traces are retained in full and are exportable in JSON format for integration with existing audit and QA systems. Each trace is linked to the activity feed entry and the original conversation, creating a complete chain of evidence from user input to agent output.

The execution inspector showed us our AI agent was calling the same API three times per task. We fixed it in ten minutes and cut our costs by 40 percent.