Modern AI agents are easy to prototype and surprisingly hard to trust in production. This guide focuses on the evaluation side of the problem: how to turn messy, non-deterministic agent behavior into something you can reason about, debug, and safely ship.
What you’ll explore 
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Agent failure modes in the real world
How planning, memory, and tool use create new failure patterns compared to classic ML models – and how to surface them instead of treating agents as black boxes. -
Designing a full evaluation stack
Instrumenting agents with span- and trace-level signals, logging the right metadata, and defining behavioral metrics so you can actually debug decisions instead of guessing. -
Running experiments that matter
Setting up controlled evaluations, comparing variants, and balancing multiple objectives (quality, cost, latency, safety) rather than chasing a single score. -
Monitoring live agents
Building feedback loops for anomaly detection, hallucination tracking, and safety checks so you notice degradation and edge cases before users do.
This is especially relevant if you’re deploying multimodal agents (voice, image, RAG, workflow-style systems) into domains like customer support, finance, healthcare, or legal - places where reliability is non-negotiable. Free download- Future AGI | LLM Observability & Evaluation Platform