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.

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.