Production agent trace and evaluation console

Reduce time to diagnose agent failures and quality drift while keeping production traces under the team's control. For engineering and operations teams running AI agents in production, convert captured agent executions, evaluation rubrics and production metrics into reviewed agent health reports and debug findings. The benefit is a testable hypothesis, measured through time to diagnose a failed run and share of runs with a completed review; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect agent execution traces, evaluation rubrics, production metrics and data source connections, then follow this sequence: 1. Capture full agent executions as sessions, traces and spans across LLMs, tools and sub-agents. 2. Group related traces and calls into one session timeline. 3. Score traces and sessions with agent-specific metrics and rubrics. 4. Schedule recurring evaluations and track agent health over time. 5. Track token usage, cost and latency per request and workflow step. Resolve uncertain cases with qualified reviewers, approve reviewed agent health reports and debug findings, and measure time to diagnose a failed run and share of runs with a completed review against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved capture method and a fixed set of agent versions; final quality judgments and production changes remain with the responsible engineering owner. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve trace integrity, source attribution, evaluation accuracy and usage permissions. Engineering owners approve substantive changes and production scope. One approved capture method and a fixed set of agent versions; final quality judgments and production changes remain with the responsible engineering owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One approved capture method and a fixed set of agent versions; final quality judgments and production changes remain with the responsible engineering owner. Implement one approved input format, a bounded representative case set and the first five task modules: capture full agent executions as sessions, traces and spans; group related traces and calls into one session timeline; score traces and sessions with agent-specific metrics and rubrics; schedule recurring evaluations and track agent health over time; track token usage, cost and latency per request and workflow step. Support the remaining modules with operator review: label user intents, corrections and agent resolutions automatically; collect reasoning blocks and tool decision metadata; segment metrics by agent version; monitor agent runs in real time; track workflow state; support parallel instances and sub-agent reporting; debug failures; manage agent configuration; visualize latency in a waterfall timeline; capture TLS-encrypted LLM calls; map agent turns to traces and spans; convert production traffic into fine-tuning training data; detect unsupported claims; generate test questions; connect to data sources. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Agent-owned traces, authorized evaluation rubrics and permitted production metrics. Cloud trace storage, OpenTelemetry collectors, data sources such as PostgreSQL, internal APIs and Pinecone, and training-data destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Agent and project setup, Live run board, Trace and session explorer, Evaluation and rubric workbench, Health and cost dashboard, Export and training-data review. Use a project list with agent versions, a central session timeline with a waterfall view, and a right-hand panel for spans, reasoning blocks, annotations and comments. Let users compare agent versions side by side. Display running, needs review, reviewed and failed states. Provide a shareable trace link with comments anchored to the relevant span. Make the task-specific outcome reviewed agent health reports and debug findings visible beside its evidence, review state and value baseline.