AI-Driven Observability for Trustworthy Agentic AI
Agents return a clean 200 OK and still be wrong, unsafe, or expensive. Why agentic AI needs a new observability layer — LLM-as-judge, safety metrics, and the four lifecycle stages.
#LLM-as-Judge
LLM-as-judge evaluation uses one language model to assess the output quality of another. Posts cover LLM-as-judge implementation in Microsoft Agent Framework evaluation pipelines, scoring rubric design, and the calibration practices that make automated evaluation reliable enough for production quality gates.
2 posts tagged with llm-as-judge. ← All posts
Multi-turn evals from first principles
Single-turn evals check one decision. Multi-turn evals check the whole trajectory. A Python harness with three evaluators, an offline test suite, and the judge prompt that actually works.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.