How to find and debug agent failures your evals are missing

Arize Signal is presented as an always-on investigation tool within Arize AX that reviews production AI-agent traces to identify recurring or emerging failures that predefined evaluations and conventional monitoring may miss. Rather than judging only final responses, it examines full agent trajectories, including retrieval, tool calls, retries, errors, state changes, and completion claims, helping reveal cases where an agent produces a plausible answer without completing the requested task. Signal groups related evidence into prioritized issues with likely causes and recommended next steps, allowing engineers to convert confirmed findings into representative regression datasets and targeted code evaluators, LLM judges, or agent-based evaluations. The proposed workflow keeps humans responsible for validating failures, defining expected behavior, reviewing candidate changes, and approving releases, while experiments compare complete system effects such as correctness, tool use, latency, cost, and reliability. By continuously connecting production observations to maintained test suites, the approach aims to make agent evaluation more responsive to unforeseen real-world failures across models, prompts, tools, retrieval, routing, and retry logic.

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