Evaluating AI Agents and Autonomous Systems: Systematic Frameworks for Testing Autonomy, Tool-Calling Reliability, and Multi-Step ReasoningAI agents are moving from impressive demos into real systems that call tools, retrieve data, make decisions, and execute workflows. But how do you know an autonomous agent is safe, reliable, and ready for production before it reaches users?
Evaluating AI Agents and Autonomous Systems gives engineers, architects, and technical leaders a practical framework for testing the systems that traditional software tests cannot fully capture. Built around autonomy, tool-calling reliability, multi-step reasoning, RAG evaluation, safety boundaries, observability, and multi-agent coordination, this book shows how to move from prompt testing to systematic agent validation. The book's structure covers evaluation harnesses, planning metrics, schema validation, LLM-as-a-judge workflows, RAG faithfulness, red teaming, trace analysis, human-in-the-loop review, scalable benchmarking, and MCP-based tool integration.
Inside, readers will learn how to:
- Measure whether an agent follows the right reasoning path, not just produces a polished answer.
- Test tool selection, JSON/schema correctness, hallucinated tool calls, and recovery behavior.
- Build evaluation pipelines for RAG, memory retrieval, multi-hop reasoning, and grounded tool arguments.
- Apply red teaming, guardrails, PII audits, and boundary testing to autonomous workflows.
- Use observability, tracing, regression tests, and human review to catch failures before deployment.
For AI engineers, ML engineers, platform teams, and enterprise AI leaders, this book provides the testing discipline needed to ship agentic systems with confidence.
