Introduction

A detector is an LLM-as-a-judge — an evaluator model that reads each incoming trace and decides whether a specific problem is present. When the judge flags one, the TraceRoot AI agent investigates — pulling in the trace, your production source code, and recent GitHub activity — and the combined alert lands once both steps complete.

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Two-step pipeline

Detection runs in two stages so you pay deep-analysis costs only on real problems: Step 1 — LLM-as-a-judge. Every selected trace is read by an evaluator model. The judge returns a boolean: is the problem you defined present in this trace? Step 2 — AI agent run. When the judge flags a trace, the TraceRoot AI agent investigates: it downloads the trace, reads your production source code at the failure point, checks recent commits and PRs touching that file, and produces a root cause analysis with a code location and recommendation. Each step’s model is configurable. For the judge we can choose a cheaper model and for the agent we can pick a stronger model since it only runs on real findings. You can swap either to any system model or a BYOK model — independently per step.

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Anatomy of a detector

A detector has three parts:

• A prompt — what to look for, in natural language (“does this trace contain a hallucination?”).
• A target — which traces it runs on (every trace in the project, by default).
• An outcome — a boolean identified — was the problem present in this trace?

When identified is true, the detector emits a finding, the AI agent investigates, and the combined alert — finding plus analysis — lands in your inbox.

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Where to start