44 items
Create a free account to keep your reading organized, join thoughtful discussions, and get more from every chapter.
Free to join · Takes less than a minute
Why
I created this space so readers can learn together, ask questions, and make sense of difficult ideas.
Scale AI agents from single users to thousands while maintaining performance and controlling costs. Topics include horizontal scaling, load balancing.
Reduce AI agent API costs without sacrificing capability. Topics include model selection, caching, batching, prompt optimization.
Covers practical techniques to make AI agents respond faster, including model selection strategies, response caching, streaming, parallel execution.
Maintain and update AI agents safely, manage costs, respond to user feedback, and keep your system healthy over months and years of operation.
Monitor your deployed AI agent's health, handle errors gracefully, and build reliability through health checks, metrics tracking, error handling.
Deploy your AI agent from a local script to a production service. Topics include packaging, cloud deployment, APIs, and making your agent accessible to users.
Establish ethical guidelines and implement human oversight for AI agents. Topics include defining core principles, encoding ethics in system prompts.
Control AI agent actions with least-privilege permissions, confirmation steps, and sandboxing. Covers tool allowlists, sensitive operations, and safe defaults.
Implement content safety and moderation in AI agents, including system-level instructions, output filtering, pattern blocking, graceful refusals.
Use observability for continuous agent improvement. Topics include patterns in logs, turn observations into targeted improvements, track quantitative metrics.
Read agent logs, trace reasoning chains, identify common problems, and systematically debug AI agents.
Add logging to AI agents to debug behavior, track decisions, and monitor tool usage. Includes practical Python examples.
Create feedback loops that continuously improve your AI agent through real-world usage data, pattern analysis, and targeted improvements.
Create and use test cases to evaluate AI agent performance. Build broad test suites, track results over time.
Define clear, measurable success criteria for AI agents including correctness, reliability, efficiency, safety.
Examines the trade-offs of multi-agent AI systems, from specialization and parallel processing to coordination challenges and complexity management.
Explains how AI agents exchange information and coordinate actions through structured messages, communication patterns like pub-sub and request-response.
Explains how multiple AI agents collaborate through specialization, parallel processing, and coordination.
See how AI agents use planning to handle complex, multi-step tasks. Topics include task decomposition, sequential execution.
Explains how AI agents execute multi-step plans sequentially, handle failures gracefully, and adapt when things go wrong.
Explains how AI agents break down complex goals into manageable subtasks. Topics include task decomposition strategies, sequential vs parallel tasks.
Define what your AI agent can and cannot do through access controls, action policies, rate limits, and scope boundaries.
Explains how AI agents perceive their environment through inputs, tool outputs, and memory.
Explains what an environment means for AI agents, from digital assistants to physical robots. Explains how environment shapes perception, actions.
Explains how AI agents maintain continuity across sessions with ephemeral, session, and persistent state management.
Structure AI agents with clear architecture patterns. Build organized agent loops, decision logic.
Explains what agent state means and why it's essential for building AI agents that can handle complex, multi-step tasks.
Build a complete AI agent memory system combining conversation history and persistent knowledge storage.
Explains how AI agents store and retrieve information across sessions using vector databases, embeddings, and semantic search.
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. Topics include the complete workflow from Python function to tool integration.
Call language models from Python code, including GPT-5, Claude Sonnet 4.5, and Gemini 2.5. Covers API integration, error handling.
Design effective tool interfaces for AI agents, from basic function definitions to multi-tool orchestration.
Explains why AI agents need external tools to overcome limitations like outdated knowledge, imprecise calculations, and inability to take real-world actions.
Use chain-of-thought prompting to get AI agents to reason through problems step by step, improving accuracy and transparency for complex questions.
Guide AI agents to verify and refine their reasoning through self-checking techniques. Topics include practical methods for catching errors, improving accuracy.
Teach AI agents to think through problems step by step using chain-of-thought reasoning.
Covers art of communicating with AI agents through effective prompting. Craft clear instructions, use roles and examples.
Covers advanced prompting strategies for AI agents including role assignment, few-shot prompting with examples, and iterative refinement.
Covers the fundamentals of writing effective prompts for AI agents. Explains how to be specific, provide context.
Language models are the reasoning core of many AI agents. Covers token prediction, instruction following, tool selection, and limits of model-only systems.
Covers what you'll build throughout this book: a capable AI agent that remembers conversations, uses tools, plans tasks, and grows smarter with each chapter.
Explains how language models predict text, process tokens, and power AI agents through simple analogies and clear explanations.
Explains what distinguishes AI agents from chatbots, exploring perception, reasoning, action, and autonomy.
Create a free account to keep your reading organized, join thoughtful discussions, and get more from every chapter.
Free to join · Takes less than a minute
Why
I created this space so readers can learn together, ask questions, and make sense of difficult ideas.