Agent Observability: trace every AI agent run in production

Trodo captures every AI agent execution: the planner steps, tool calls, LLM prompts and completions, retrieved context, latency, cost, and final output. A whole multi-step run end to end, across sub-agents and tool hand-offs, which is where most real failures happen rather than in a single model call.

Silent failure detection: the runs that never throw an error

The dangerous failure is the one that completes successfully and returns the wrong thing: a number that traces back to no tool call, a walkthrough of a flow that changed last week, a confident answer with nothing behind it. No span fails and no alert fires, so detection ends up depending on a user complaining. Trodo evaluates every production run rather than a sample, so those runs get flagged and grouped by root cause as they happen.

Observability that takes action, not just records

A trace tells you what happened. Trodo goes further: every run carries the prompt version that produced it and the eval score that flagged it, failures cluster by cause so one fix closes the whole set, and a detected failure can open the pull request itself. Not just what broke, but what to do about it.

AI agent monitoring and evals for production AI

Trodo monitors AI agent runs in real time and surfaces issues automatically: latency spikes, tool call failure surges, cost anomalies, and quality regressions. Score any run with a human review, a Python assertion, or an LLM judge, and get a direct path from the issue to a fix in your IDE.