AI Agents: From Design to Deployment bundle 
AI agents are transforming how intelligent applications are built, moving beyond simple chatbots to autonomous systems that can reason, plan, use tools, collaborate, and accomplish complex tasks. Whether you're just getting started with agentic AI or looking to build production-ready systems, this bundle gives you the practical knowledge to design, develop, and deploy modern AI agents with confidence.
Start by learning the core principles behind effective agent architecture and decision-making. Then dive into hands-on implementation with real-world examples covering LLM-powered agents, MCP, memory, reasoning, planning, multi-agent systems, evaluation, and deployment. Finally, explore how agentic workflows are reshaping cybersecurity through AI-powered reconnaissance, vulnerability discovery, reporting, and offensive security automation.
.jpg){kind=spacer}
AI Agents for Offensive Security 
AI is changing how offensive security workflows are designed, executed, and analyzed. AI Agents for Offensive Security: Understanding AI-powered attacks and how to stop them shows you how to build and use AI agents and multi-agent pipelines to support reconnaissance, triage, vulnerability discovery, reporting, agentic penetration testing, and red team/blue team workflows.In this practical guide for offensive security professionals, you’ll learn how to create intelligent agents that automate parts of security testing while preserving safety, auditability, and human oversight. You’ll build your first security agent, explore how artifacts and pipelines structure agentic workflows, and see how AI can help interpret results, prioritize findings, and reduce manual triage. Along the way, AI security expert Mark Foudy emphasizes governance, authorization controls, resilience, and transparency. You’ll understand where AI adds value, and also where it introduces operational and security risk. The result is a clear framework for integrating AI into day-to-day security work.
AI Agents and Applications
Along the way you’ll build concrete applications—summarization and Q&A engines, context-aware chatbots with memory, and tool-using AI agents that orchestrate multi-step workflows with branching logic. For the examples, the book uses Python, LangChain, LangGraph, and LangSmith, but you’ll be able to generalize to other frameworks. You’ll understand with clarity and confidence how to keep integrations maintainable, manage context limits and cost/latency tradeoffs, and evaluate, debug, and monitor behavior so your systems work in production.
Designing AI Agents
AI agents promise to automate work on an unprecedented scale—even for tasks requiring reasoning and complex multi-step processes. But where do you start? How do you budget your dev time and token spend? What do you do with an agent that “almost works?” How do you scale or improve a multi-agent system? Designing AI Agents answers all these questions and more. In this enlightening book, author Jia Huang, a senior AI researcher at the Singapore-based Agency for Science, Technology and Research (A*STAR), presents an innovative two-axis framework blending seven cognitive functions—perception, memory, reasoning, action, reflection, collaboration, and governance—with six topologies—chain, route, parallel, orchestrate, hierarchy, and loop.In Designing AI Agents, you’ll learn how to establish agent architectures that manage costs and take governance seriously from day one. This innovative book explores 27 reusable patterns that you can apply to your own agentic systems confidently. Each pattern has been stress-tested at scale, with over 10,000 engineers applying them to ship production agents in banks, manufacturers, and AI startups. You’ll appreciate how this book guides you toward system and harness design that imposes certainty and reliability on the non-deterministic behavior of LLM-driven agents. Once you catch author Jia Huang’s vision, you’ll stop asking “which tools?” and start asking "which patterns?".
Unlike other “agentic patterns” books that dwell on abstract theory, every chapter in this practical guide grows a single running example. You’ll incrementally build Argus, a code-review agent that evolves from a 50-line reasoning-and-action loop all the way to a production-grade system. As you steadily upgrade Argus, you’ll learn both which patterns to use and when and why to apply them. Plus, full case studies exploring agents working as a DevOps incident responder, compliance reviewer, and research synthesis agent show the methodology in action across diverse domains. And, as with all Manning books, you’ll find a clearly defined learning path, thoughtfully edited and readable text, and our promise that everything is accurate and reliable.
AI Agents in Action, Second Edition
"Great contents, broad coverage, fun exercises. This book has it all.”—Saurabh Sawant, Microsoft
AI Agents in Action, Second Edition is a substantial revision and update of the first edition. It is a practical and comprehensive guide to building AI agents—not just understanding what they are, but designing, implementing, evaluating, and deploying them. Its strength is in the way it combines conceptual clarity with working code examples, so readers build progressively rather than absorb isolated ideas. The examples form a continuous learning path, moving from a minimal agent to more capable, tool-using, multi-agent, and deployable systems. Each step adds a new skill while reinforcing what came before.
The book begins by giving readers a usable mental model for agent design. Its central organizing idea is the five functional layers: persona, actions and tools, reasoning and planning, knowledge and memory, and evaluation and feedback. This framework helps readers understand where an agent’s behavior comes from and how to diagnose weaknesses. Rather than randomly adding prompts, tools, or memory, readers learn to ask which layer needs improvement. This is especially valuable because the model is not tied to one vendor or framework; it remains useful even as APIs and tools continue to change.
From there, the book moves into the practical building blocks of agents: LLMs, prompting, typed outputs, tracing, tool use, and the OpenAI Agents SDK. Typed outputs reduce brittle text parsing. Tracing exposes what the agent is doing. Tool integration gives agents the ability to act rather than merely respond. The cumulative benefit is that readers learn to build agents that are more predictable, inspectable, and maintainable.
A highlight is the treatment of Model Context Protocol. Readers liked the book’s “USB-C” analogy, because it explains MCP as a standard connector between agents and external capabilities. The book shows how MCP can flatten “a mess of bespoke integrations” into cleaner, swappable components, helping developers build agents that are modular instead of tangled.
The book also covers multi-agent architectures, reasoning patterns, planning strategies, RAG, memory, evaluation, feedback, observability, and deployment. Each topic is tied to a practical benefit: multi-agent patterns help divide complex work; reasoning and planning help agents handle multi-step tasks; RAG and memory let agents use external and retained knowledge; evaluation and feedback help make them safer and more reliable.
Physically, this is a substantial but focused book covering 392 pages across 11 chapters. Its tables and figures are a valuable part of the learning experience. While building, readers will want to return to the easy-to-use tables summarizing complex trade-offs.
AI Agents in Action shows developers how to build agents they can ship, trust, and maintain.
Some bundled books and liveVideos are part of the Manning Early Access Program. You'll get all the available content now, new content as it's created, and the final product when it's ready.
