30 Agents Every AI Engineer Must Build

From the author of 50 Algorithms Every Programmer Should Know. Learn to design and implement 30 intelligent agents that combine core architecture patterns with domain-specific solutions.

Key Features

Get to grips with foundational agent principles including perception, memory, reasoning, and planning
Integrate advanced frameworks like LangChain and AutoGPT in your AI agent development
Design agents using advanced prompting, knowledge retrieval, and multi-agent orchestration
Purchase of the print or Kindle book includes a free PDF eBook

Book DescriptionAs AI evolves from passive tools into proactive collaborators, intelligent agents lead this transformative shift. This guide equips you with critical knowledge on agent architectures, practical tools, and industry insights to develop robust, autonomous AI systems. You'll start by mastering foundational agent capabilities such as perception, memory, reasoning, planning, and learning. Gain insight into the cognitive loops essential for autonomous systems and build agent architectures using state-of-the-art frameworks like LangChain and LangGraph. Practical industry applications are explored across healthcare, finance, manufacturing, and education-illustrating how agents can optimize workflows, enhance advisory systems, automate quality control, and enable adaptive learning environments. Through numerous real-world examples, this book guides you in creating intelligent agents capable of contextual reasoning, effective tool utilization, real-time responsiveness, and seamless collaboration with humans. Additionally, you'll learn crucial strategies for the deployment, management, and ethical development of responsible AI systems. Whether you're developing your first intelligent agent or enhancing critical business operations, this book provides clear, actionable guidance for creating scalable and ethically robust AI solutions.What you will learn

Use LangChain and LangGraph to construct autonomous agents with modular, scalable architectures
Establish robust evaluation frameworks to measure agent performance, reliability, and alignment
Deploy production-ready agent systems that scale securely in enterprise environments
Implement ethical guardrails and explainability features to ensure responsible AI deployment
Navigate ethical concerns around explainability, bias, and safe deployment
Implement ethical guardrails and explainability features to ensure responsible AI deployment

Who this book is forThis book is for AI engineers, software developers, ML researchers, and technical leads building intelligent systems. Ideal for those deploying LLM-powered applications or transitioning from traditional ML to agentic frameworks. Python experience and basic ML knowledge are recommended.