2026-06-09 –, Frannz Salon
Many so-called “agent failures” are actually context failures in disguise. In this session, we’ll explore how to tell whether your agent truly saw and used the right context, using techniques like tracing and attribution, golden datasets for context-aware evaluation, and targeted probes to test retrieval quality.
Your agent answered confidently, did it use the right evidence? We’ll walk through a repeatable debugging workflow for RAG + tool-using agents: instrument traces, inspect retrieved chunks, run attribution and citation checks, and isolate failure modes (missing recall, bad ranking, distractors, stale docs). You’ll learn how to create a lightweight golden set, write probe questions, and track retrieval + answer metrics so improvements are measurable, not vibes.
Apurva Misra is a machine learning engineer, speaker, and founder of Sentick, where she helps growing teams unlock practical, ROI-driven AI solutions across automations, predictive analytics, and copilots.
As a consultant, she builds end-to-end AI systems from discovery to production. In one client engagement, she delivered an AI customer support system that reduced support emails by more than 30%.
Her academic work includes a University of Waterloo Master’s focused on driver cognitive distraction detection, publications in IEEE Access with 100+ citations so far.
She especially enjoys the education side of AI getting founders and teams up to speed on what’s genuinely useful and how to apply it through over 40 publicly listed talks, workshops, webinars, panels, and podcast appearances.