Moving from ad-hoc vibe-checking to structured AI evaluation requires a systematic approach. MLflow's eval-driven development cycle covers three phases: instrumenting agents with tracing from day one, incorporating human and LLM-judge feedback while building evaluation datasets, and deploying with stakeholder dashboards and continuous production monitoring. Key capabilities include one-line autologging for LLM calls, custom domain-specific judges via the make_judge API, prompt versioning with the Prompt Registry, and automated prompt optimization using algorithms like GEPA. The same evaluation framework used offline runs continuously on live traffic, eliminating the need for a separate production monitoring system.
Table of contents
Why Agents Break Differently: The Case for AI Observability Eval-Driven Development: Three Phases That Shape Your MLflow Evaluation Strategy From Built-in Judges to Custom Evaluations: Layering Your Agent Scoring Strategy Systematically Improving and Optimizing Prompts in LLMOps Key Takeaways for a Structured Approach to AI Observability What's Next? References and Resources Sort: