Content from the AI Agent Handbook, covering autonomous AI agents, models, memory systems, tools, reasoning, evaluation, and operations.
Learn how to scale AI agents from single users to thousands while maintaining performance and controlling costs. Covers horizontal scaling, load balancing, monitoring, cost controls, and prompt optimization strategies.
Learn how to dramatically reduce AI agent API costs without sacrificing capability. Covers model selection, caching, batching, prompt optimization, and budget controls with practical Python examples.
Learn practical techniques to make AI agents respond faster, including model selection strategies, response caching, streaming, parallel execution, and prompt optimization for reduced latency.
Learn how to maintain and update AI agents safely, manage costs, respond to user feedback, and keep your system healthy over months and years of operation.
Learn how to monitor your deployed AI agent's health, handle errors gracefully, and build reliability through health checks, metrics tracking, error handling, and scaling strategies.
Learn how to deploy your AI agent from a local script to a production service. Covers packaging, cloud deployment, APIs, and making your agent accessible to users.
Learn how to establish ethical guidelines and implement human oversight for AI agents. Covers defining core principles, encoding ethics in system prompts, preventing bias, and implementing human-in-the-loop, human-on-the-loop, and human-out-of-the-loop oversight strategies.
Learn how to implement action restrictions and permissions for AI agents using the principle of least privilege, confirmation steps, and sandboxing to keep your agent powerful but safe.
Learn how to implement content safety and moderation in AI agents, including system-level instructions, output filtering, pattern blocking, graceful refusals, and privacy boundaries to keep agent outputs safe and responsible.
Learn how to use observability for continuous agent improvement. Discover patterns in logs, turn observations into targeted improvements, track quantitative metrics, and build a feedback loop that makes your AI agent smarter over time.
Learn how to read agent logs, trace reasoning chains, identify common problems, and systematically debug AI agents. Master the art of understanding what your agent is thinking and why.
Learn how to add logging to AI agents to debug behavior, track decisions, and monitor tool usage. Includes practical Python examples with structured logging patterns and best practices.
Learn how to create feedback loops that continuously improve your AI agent through real-world usage data, pattern analysis, and targeted improvements.
Learn how to create and use test cases to evaluate AI agent performance. Build comprehensive test suites, track results over time, and use testing frameworks like pytest, LangSmith, LangFuse, and Promptfoo to measure your agent's capabilities systematically.
Learn how to define clear, measurable success criteria for AI agents including correctness, reliability, efficiency, safety, and user experience metrics to guide evaluation and improvement.
Explore the trade-offs of multi-agent AI systems, from specialization and parallel processing to coordination challenges and complexity management. Learn when to use multiple agents versus a single agent.
Learn how AI agents exchange information and coordinate actions through structured messages, communication patterns like pub-sub and request-response, and protocols for task delegation and consensus building.
Learn how multiple AI agents collaborate through specialization, parallel processing, and coordination. Explore cooperation patterns including sequential handoff, iterative refinement, and consensus building, plus real frameworks like Google's A2A Protocol.
See how AI agents use planning to handle complex, multi-step tasks. Learn task decomposition, sequential execution, and error handling through a complete example of booking meetings and sending summaries.
Learn how AI agents execute multi-step plans sequentially, handle failures gracefully, and adapt when things go wrong. Includes practical Python examples with Claude Sonnet 4.5.
Learn how AI agents break down complex goals into manageable subtasks. Understand task decomposition strategies, sequential vs parallel tasks, and practical implementation with Claude Sonnet 4.5.
Learn how to define what your AI agent can and cannot do through access controls, action policies, rate limits, and scope boundaries. Master the art of balancing agent capability with security and trust.
Learn how AI agents perceive their environment through inputs, tool outputs, and memory, and how they take actions that change the world around them through the perception-action cycle.
Learn what an environment means for AI agents, from digital assistants to physical robots. Understand how environment shapes perception, actions, and agent design.
Learn how AI agents maintain continuity across sessions with ephemeral, session, and persistent state management. Includes practical implementation patterns for state lifecycle, conflict resolution, and debugging.
Learn how to structure AI agents with clear architecture patterns. Build organized agent loops, decision logic, and state management for scalable, maintainable agent systems.
Learn what agent state means and why it's essential for building AI agents that can handle complex, multi-step tasks. Explore the components of state including goals, memory, intermediate results, and task progress.
Learn how to build a complete AI agent memory system combining conversation history and persistent knowledge storage. Includes semantic search, tool integration, and practical implementation patterns.
Learn how AI agents store and retrieve information across sessions using vector databases, embeddings, and semantic search. Build a personal assistant that remembers facts, preferences, and knowledge long-term.
Learn how to give AI agents the ability to remember recent conversations, handle follow-up questions, and manage conversation history across multiple interactions.
Build a working calculator tool for your AI agent from scratch. Learn the complete workflow from Python function to tool integration, with error handling and testing examples.
Learn how to call language models from Python code, including GPT-5, Claude Sonnet 4.5, and Gemini 2.5. Master API integration, error handling, and building reusable functions for AI agents.
Learn how to design effective tool interfaces for AI agents, from basic function definitions to multi-tool orchestration. Covers tool descriptions, parameter extraction, workflow implementation, and best practices for agent-friendly APIs.
Discover why AI agents need external tools to overcome limitations like outdated knowledge, imprecise calculations, and inability to take real-world actions. Learn how tools transform agents from conversationalists into capable assistants.
Learn how to use chain-of-thought prompting to get AI agents to reason through problems step by step, improving accuracy and transparency for complex questions, math problems, and decision-making tasks.
Learn how to guide AI agents to verify and refine their reasoning through self-checking techniques. Discover practical methods for catching errors, improving accuracy, and building more reliable AI systems.
Learn how to teach AI agents to think through problems step by step using chain-of-thought reasoning. Discover practical techniques for improving accuracy and transparency in complex tasks.
Master the art of communicating with AI agents through effective prompting. Learn how to craft clear instructions, use roles and examples, and iterate on prompts to get better results from your language models.
Master advanced prompting strategies for AI agents including role assignment, few-shot prompting with examples, and iterative refinement. Learn practical techniques to improve AI responses through context, demonstration, and systematic testing.
Learn the fundamentals of writing effective prompts for AI agents. Discover how to be specific, provide context, and structure instructions to get exactly what you need from language models.
Learn how language models work as the foundation of AI agents. Discover what powers ChatGPT, Claude, and other AI systems through intuitive explanations and practical Python examples.
Discover what you'll build throughout this book: a capable AI agent that remembers conversations, uses tools, plans tasks, and grows smarter with each chapter. Learn about the journey from simple chatbot to intelligent personal assistant.
Learn how language models predict text, process tokens, and power AI agents through simple analogies and clear explanations. Understand training, parameters, and why context matters for building intelligent agents.
Learn what distinguishes AI agents from chatbots, exploring perception, reasoning, action, and autonomy. Discover how agents work through practical examples and understand the spectrum from reactive chatbots to autonomous agents.
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