AI Agents in Production: Evaluation via Langfuse | QATestLab
Summary
QATestLab runs 32 AI agents across four platforms and built a unified evaluation and observability pipeline using Langfuse to trace full execution paths, measure performance, and speed root-cause analysis. They evaluate agents at task, step, and behavioral levels, use AI-as-Judge for automated checks (covering 22 agents), and retain human review for complex or novel cases. The team enforces one-evaluator-per-failure-mode, validates evaluators against human labels, and tracks metrics like latency, token usage, and error rates to drive targeted improvements. They recommend deploying observability before production and emphasize that no-code, low-code, and code-based platforms share similar QA challenges that require a unified approach and continuous improvement cycles.