TL;DR: Evaluating agents ≠ evaluating classic ML. This guide breaks down architectures, failure modes, an end-to-end evaluation pipeline, and how to move from ad-hoc checks to continuous monitoring.
Download for Free -> https://shorturl.at/xwhYJ
Why agent eval is different:
LLM agents are non-deterministic, tool-using, and context-heavy. Failures creep in at:
Grounding: Retrieval gaps, stale knowledge, weak citations
Reasoning: Flaky plans, tool-call errors, brittle control flow
Safety: Toxic outputs, prompt injections, policy violations
Latency/Cost: Timeouts, cascading retries, budget blowups
Classic accuracy/precision isn’t enough. You need multi-dimensional, evidence-linked evaluation.
The system-level view (what the ebook covers)
Architecture & failure modes: where things break (planning, memory, tools, retrieval).
Evaluation pipeline: reliability, grounding, safety, UX, and business metrics—scored with traceable evidence.
Continuous monitoring: drift, degradation, and hallucinations caught before users do.
Grab the ebook (free): Mastering AI Agent Evaluation, system-level architecture, pipeline templates, and ready-to-adapt playbooks.
👉 Download:https://shorturl.at/xwhYJ
Tags: #ai #machinelearning #llmop
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