Navya Yadav

Navya Yadav

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.

10 Reasons Observability Is the Backbone of Reliable AI Systems

10 Reasons Observability Is the Backbone of Reliable AI Systems

Discover why observability is the backbone of reliable AI systems: trace, measure, and improve agents with evidence, not guesswork.

Reliability at Scale: How Simulation-Based Evaluation Accelerates AI Agent Deployment

Reliability at Scale: How Simulation-Based Evaluation Accelerates AI Agent Deployment

TL;DR Reliable AI agents require continuous evaluation across multi-turn conversations, not just single-response testing. Teams should run simulation-based evaluations with realistic scenarios and personas, measure session-level metrics like task success and latency, and bridge lab testing with production observability. This approach catches failures early, validates improvements, and maintains quality

Closing the Feedback Loop: How Evaluation Metrics Prevent AI Agent Failures

Closing the Feedback Loop: How Evaluation Metrics Prevent AI Agent Failures

TL;DR AI agents often fail in production due to tool misuse, context drift, and safety lapses. Static benchmarks miss real-world failures. Build a continuous feedback loop with four stages: detect (automated evaluators on production logs), diagnose (replay traces to isolate failures), decide (use metrics and thresholds for promotion gates)