Navya Yadav

Navya Yadav

Evaluating Agentic AI Systems: Frameworks, Metrics, and Best Practices

Evaluating Agentic AI Systems: Frameworks, Metrics, and Best Practices

TL;DR Agentic AI systems require evaluation beyond single-shot benchmarks. Use a three-layer framework: System Efficiency (latency, tokens, tool calls), Session-Level Outcomes (task success, trajectory quality), and Node-Level Precision (tool selection, step utility). Combine automated evaluators like LLM-as-a-Judge with human review. Operationalize evaluation from offline simulation to online production monitoring

Utilizing Human-in-the-Loop (HITL) Feedback for Robust AI Evaluation

Utilizing Human-in-the-Loop (HITL) Feedback for Robust AI Evaluation

TL;DR Human-in-the-loop evaluation fills critical gaps that automated evaluators miss in agentic AI systems. This guide explains how to integrate Human-in-the-loop with machine evaluators, distributed tracing, and production observability. You'll learn when to route interactions to humans, how to structure effective rubrics, and how to convert feedback

Managing AI Agent Drift: How to Maintain Consistent Performance Over Time

Managing AI Agent Drift: How to Maintain Consistent Performance Over Time

TL;DR: Agent drift is the gradual decline in AI agent performance caused by changing data, evolving models, prompt modifications, and shifting user patterns. This guide provides a practical framework to detect and prevent drift through session-level observability, scenario-based simulation, unified evaluations, and controlled rollouts. A disciplined loop of simulation,

Iterative Development of AI Agents: Tools and Techniques for Rapid Prototyping and Testing

Iterative Development of AI Agents: Tools and Techniques for Rapid Prototyping and Testing

TL;DR Building reliable AI agents requires disciplined iteration through simulation, evaluation, and observability. This guide outlines a practical workflow: simulate multi-turn scenarios with personas and realistic environments, evaluate both session-level outcomes and node-level operations, instrument distributed tracing for debugging, and curate production cases into test datasets. By closing the

Managing Prompt Versions: Effective Strategies for Large Teams Using AI Agents

Managing Prompt Versions: Effective Strategies for Large Teams Using AI Agents

TL;DR Large teams building AI agents need structured prompt versioning to ship changes confidently and roll back safely. Treat prompts like code: maintain version history, link versions to evaluators, and deploy with control using canary cohorts and A/B testing. Combine side-by-side comparisons, comprehensive evaluation (deterministic checks + LLM-as-a-judge + human

Designing Reliable Prompt Flows: From Version Control to Output Monitoring

Designing Reliable Prompt Flows: From Version Control to Output Monitoring

Discover a proven workflow for prompt versioning, evaluation, and observability. Treat prompts as engineering assets to improve AI reliability and performance.

Measuring LLM Hallucinations: The Metrics That Actually Matter for Reliable AI Apps

Measuring LLM Hallucinations: The Metrics That Actually Matter for Reliable AI Apps

LLM hallucinations aren’t random; they’re measurable. This guide breaks down six core metrics and explains how to wire them into tracing and rubric-driven evaluation so teams can diagnose failures fast and ship reliable AI agents with confidence.