Trace-driven intelligence for teams building and operating AI agents. Understand why your agent did what it did, and what to change to make it better.
A trace captures the complete execution of an agent session — every reasoning step, tool call, memory operation, LLM interaction, and final response.
The platform treats each trace as a structured narrative — the equivalent of watching over the agent's shoulder as it works through a problem. This is the foundation for every insight the platform delivers.
Six behavioral dimensions analyzed across every trace — each scored, explained, and tracked over time.
Whether the agent understood what the user actually needed — including implicit goals, unstated context, and the difference between what was said and what was meant.
How sound the agent's logic was — whether it reasoned through the problem correctly, or skipped steps, made faulty inferences, or reached conclusions inconsistently.
Whether the agent stayed focused on the goal throughout the session — completing what was asked without getting distracted or giving up too early.
Whether the agent selected the right tools at the right time, used them effectively, handled their outputs correctly, and avoided unnecessary calls.
How the agent responded to errors, dead ends, ambiguous inputs, and unexpected situations — whether it self-corrected or compounded mistakes.
Whether the agent behaves in predictable, consistent ways across similar situations — a key signal of reliability and stability.