AI agents are easy to spin up and dangerously hard to trust in production. This ebook gives you a concrete evaluation playbook to turn messy, non-deterministic agents into controlled systems you can rely on in high-stakes environments.
What you'll learn:
Understand agent failure modes: Learn how planning, memory, and tool use make agents fail differently from traditional ML (and how to catch it).
Set up a complete eval stack: Instrument agents with span/trace signals and metrics to make behavior transparent and debuggable.
Run meaningful experiments: Design controlled tests, compare variants, and optimize for quality, cost, latency, and safety.
Monitor agents in production: Detect anomalies, hallucinations, and safety issues early and enforce compliance as things change.
Perfect for AI teams deploying multimodal agents (voice, image, RAG, etc.) into customer support, finance, healthcare, legal, and other domains where reliability is non-negotiable.
