Evaluate and observe LLM agents in production
Shipping LLM agents safely now requires an evaluation pipeline and production observability to catch regressions, enforce safety, and debug multi-step behavior. Start by formalizing evaluation with LLM judges, human feedback, and code-based metrics across correctness, relevance, safety, and task completion; see the practical overview in [MLflow’s guide](https://mlflow.org/llm-evaluation). Treat evaluation as continuous: run on benchmark datasets before deploys and monitor for drift and regressions over time. For runtime visibility, trace agent loops end-to-end with OpenTelemetry and inspect spans, tool calls, and latencies in SigNoz; this walkthrough shows multi-agent observability patterns and SLOs for task success and latency ([HackerNoon](https://hackernoon.com/production-observability-for-multi-agent-ai-with-kaos-otel-signoz?source=rss)). If you prefer a managed route, Innodata’s platform adds trace-level analysis, custom rubrics, LLM-as-a-judge, and CI integration for evaluation-driven rollouts ([Innodata](https://innodata.com/agentic-platform/)). For AI-generated code risk, a roundup highlights options like Hud, LangSmith, Langfuse, Arize Phoenix, and WhyLabs for tracing, evaluations, and anomaly detection in production ([WebProNews](https://www.webpronews.com/monitoring-ai-generated-code/)). Meanwhile, research updates explore whether coding agents can take on broader engineering work, underscoring the need for robust evaluation and observability from day one ([Scale AI](https://scale.com/blog/swe-atlas)).