Navya Yadav

Navya Yadav

Agent Evaluation for Multi-Turn Consistency: What Works and What Doesn’t

Agent Evaluation for Multi-Turn Consistency: What Works and What Doesn’t

TL;DR: Multi-turn AI agents need layered evaluation metrics to maintain consistency and prevent failures. Successful evaluation combines session-level outcomes (task success, trajectory quality, efficiency) with node-level precision (tool accuracy, retry behavior, retrieval quality). By integrating LLM-as-a-Judge for qualitative assessment, running realistic simulations, and closing the feedback loop between testing
Navya Yadav
How to Test AI Reliability: Detect Hallucinations and Build End-to-End Trustworthy AI Systems

How to Test AI Reliability: Detect Hallucinations and Build End-to-End Trustworthy AI Systems

TL;DR AI reliability requires systematic hallucination detection and continuous monitoring across the entire lifecycle. Test core failure modes early: non-factual assertions, context misses, reasoning drift, retrieval errors, and domain-specific gaps. Build an end-to-end pipeline with prompt engineering, multi-turn simulations, hybrid evaluations (programmatic checks, statistical metrics, LLM-as-a-Judge, human review), and
Navya Yadav