AI agents are moving quickly from experiments into production systems.
They answer customer questions, retrieve private information, call tools, update records, coordinate workflows, and increasingly make decisions that affect real people.
But how do you know whether an agent is actually ready for that responsibility?
That is the question behind my new book:
The AI Agent Test Manual
A Practical Guide to Evaluating, Testing, Monitoring, and Trusting AI Agents
The book is now available as an ebook and paperback.
Ebook: https://a.co/d/03VR2FWE
Paperback: https://a.co/d/01s8KbBJ
Why I wrote this book
Testing traditional software is already difficult. Testing AI agents introduces an additional challenge: the same input does not always produce the same path, tool call, or response.
A unit test can confirm that an API returns the expected value. It cannot fully determine whether an agent:
- Selected the correct tool
- Used the right customer or entity
- Gathered sufficient evidence
- Invented a plausible explanation
- Stopped at the right time
- Repeated a state-changing action
- Escalated an uncertain case
- Behaved safely across several runs
Teams often begin by testing prompts and reading a few example conversations. That may be enough for a prototype, but it is not enough for a production system with access to data, tools, memory, and external services.
I wrote The AI Agent Test Manual to provide a practical engineering approach to this problem.
What the book covers
The book follows the fictional BlueTorrent Insurance team as it develops a customer-service agent and encounters increasingly realistic production risks.
Along the way, it explains how to:
- Define measurable quality for an AI agent
- Build evaluation strategies based on risk
- Create realistic and reusable test cases
- Measure correctness, grounding, latency, cost, and task completion
- Design structured human evaluations
- Validate and calibrate LLM judges
- Build automated evaluation pipelines
- Test tools, retrieval, memory, permissions, APIs, and state
- Evaluate multi-agent systems as distributed systems
- Add production observability and conversation replay
- Prepare incident-response procedures and kill switches
- Create the governance and evidence needed for trustworthy agents
The focus is not a particular framework, model provider, or evaluation platform.
The methods are intended to remain useful even as the underlying technology changes.
Beyond prompt testing
A central argument of the book is that an agent is more than its prompt.
Its behaviour depends on an entire system:
- Models
- Instructions
- Knowledge sources
- Retrieval
- Memory
- Identity
- Permissions
- Tools
- APIs
- External state
- Evaluation systems
- Release processes
- Human oversight
A perfect prompt cannot compensate for stale customer data, an ambiguous tool result, an overly privileged service account, or a write action executed twice.
Reliable agent testing therefore needs to examine the complete chain from the user’s request to the final response or external action.
Practical rather than theoretical
This is not a collection of abstract principles.
The book includes practical artefacts that teams can adapt, including:
- Evaluation-strategy templates
- Test-case structures
- Metric definitions
- Human-review forms
- Judge cards
- Release-gate examples
- Pipeline definitions
- System-test matrices
- Observability schemas
- Incident records
- Trust-review checklists
Each chapter ends with a focused exercise that a team can begin on Monday morning.
The goal is to help engineering, quality, product, security, and operations teams turn evaluation into normal production engineering work.
Who the book is for
The AI Agent Test Manual is written for people building or operating real AI systems, including:
- Software engineers
- QA and test engineers
- AI and machine-learning engineers
- Engineering leads
- Product managers
- Platform and DevOps teams
- Security and governance specialists
- Technical decision-makers
You do not need to use a particular agent framework. The book focuses on the underlying engineering problems and the evidence required to solve them.
The central question
The most important question about an AI agent is not:
How intelligent is it?
It is:
What evidence do we have that this system can be trusted with the responsibility we are giving it?
That question becomes more important as agents gain access to sensitive information, external systems, financial processes, and long-running workflows.
The AI Agent Test Manual provides a structured way to answer it.
Get the book
The book is available now:
Ebook: https://a.co/d/0htPAHi8
Paperback: https://a.co/d/0acYRKZT
I hope it helps teams move beyond impressive demonstrations and build AI agents that can be tested, observed, operated, and improved with confidence.
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