Free Lesson
The Hillclimb: How to Measure and Improve Your AI Agents
60 min
Sep 16, 2026 3:00 PM
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What you'll learn
Demystify evals
What evals, benchmarks, and training datasets are, and how they fit together for your product.
Design structured evals
Taxonomize workflows that matter, write verifiable rubric criteria, and build simulations that check what your agent did
Complete the learning loop
Turn failure modes into training data your agent hillclimbs on, so evals become compounding IP instead of a report card.
Why this topic matters
Most teams check their agents on vibes: a few test prompts and sparse top-line metrics. Structured Evals systematically identify where your agent fails, with simulations that mimic real-world scenarios, and turn those failures into the training data that teaches the agent to do the work better. That's how you build AI you can trust. In this session, we'll walk through a real case study: an agent that automates pay disputes.
