LLM Observability for Multi-Agent Systems with MLflow

This video explores how to move beyond request-level monitoring to full LLM observability for multi-agent systems using MLflow. It demonstrates how tracing captures inputs, outputs, and metadata from every step, and how LLM-as-a-judge evaluation can assess agent quality. The video also covers production deployment considerations.

■ Core Concepts
- Trace and span structure for multi-agent requests
- Silent tool failures, cascading latency, context overflow, and nondeterminism

■ Evaluation with MLflow
- Deterministic scores and LLM-as-a-judge criteria
- Prompt registry for version control

■ Production Deployment Tips
- Database and artifact storage configuration
- Async logging, sampling, and judge model selection
- Integrating evaluation into CI/CD

This video is for developers and engineers building or operating multi-agent LLM applications who need deeper visibility and quality assurance. You will learn how to implement tracing, evaluate agent performance, and apply production-ready configuration choices.