Language AI Handbook, Volume 16: Agents and AI Coding Systems cover

Volume 16 of 20 · PDF edition

Agents and AI Coding Systems

For builders of tool-using, coding, computer-use, and deep-research agents that must work safely over long horizons.

Part of Language AI Handbook. The online chapters are free to read; the PDF edition is yours to keep, with future updates included.

Become a more confident language AI practitioner.

Build the understanding to explain how language models work and make more informed choices about using them. The handbook connects the foundations to the methods behind modern language AI, with explanations you can return to as you learn.

See what you’ll learn

What you will learn

  • Understand Code Generation.
  • Understand Tool Use and Agents.
  • Understand Long-Horizon Agents and Interoperability.

Who this volume is for

  • Engineers, researchers, students, AI enthusiasts, linguists, product managers, and anyone interested in understanding or building modern language AI systems, from foundational NLP to advanced large language models.

Prerequisites: Assumes decoder models from Volume 9; Volume 15 is recommended.

Volume 16 PDF edition

Own this 510-page edition

Keep the PDF for offline reading, with future updates included. The corresponding chapters stay free online.

  • Carefully typeset PDF
  • Future updates and errata included
  • Online chapters remain free to read

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Reader verdict

5.0

out of 5

12 five-star reader reviews

Genuine feedback, with names abbreviated for privacy.

Meet the community
“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
Muhammad H.AI/ML Engineer
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
Will G.Data Scientist
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
Krishnan S.Researcher
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
Griffin P.Quantitative Researcher
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
San Z.Researcher
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
Cyrille L.AI/ML Engineer
“Thank you Michael. It's top!”
David X.Data Scientist
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
Karim Z.Reader
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
Uon L.Reader
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
Vikas C.AI/ML Engineer
“Thanks for putting cool stuff out into the world.”
Charlie B.Applied AI
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
Kanishk K.Reader
Read all 12 reader reviews
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
Will G.Data Scientist
“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
Muhammad H.AI/ML Engineer
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
Cyrille L.AI/ML Engineer
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
Vikas C.AI/ML Engineer
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
Krishnan S.Researcher
“Thank you Michael. It's top!”
David X.Data Scientist
“Thanks for putting cool stuff out into the world.”
Charlie B.Applied AI
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
San Z.Researcher
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
Griffin P.Quantitative Researcher
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
Karim Z.Reader
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
Kanishk K.Reader
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
Uon L.Reader

Exact contents

16 chapters across 3 parts

Every linked chapter is free to read online. 8 further chapters are planned; unlinked titles are still in progress.

Part XLVII: Code Generation

  1. 01
    Code LLM Training

    Covers code training data, code tokenization, fill-in-the-middle training, code pre-training objectives.

  2. 02
    Code Understanding

    Covers code explanation, bug detection, code review, code search.

  3. 03
    Code Completion

    Covers completion context, completion ranking, completion latency, completion UX.

  4. 04
    Code Generation

    Covers docstring-to-code, test-to-code, code generation strategies, generation quality.

  5. 05
    Code Execution

    Covers sandboxed execution, execution feedback, iterative refinement, execution safety.

  6. 06
    Code Evaluation

    Covers functional correctness, pass@k metric, code benchmarks, beyond correctness.

Part XLVIII: Tool Use and Agents

  1. 01
    Tool Use Motivation: Why LLMs Need External Tools for Accuracy

    Discover why LLMs require external tools to overcome knowledge cutoffs, computational limits, and hallucinations. Learn about tool-augmented AI systems.

  2. 02
    Function Calling: Structured Tool Use for Large Language Models

    Learn how function calling enables LLMs to invoke external tools and APIs through structured JSON schemas, bridging natural language and executable code.

  3. 03
    ReAct Pattern: Interleaving Reasoning and Action for LLM Agents

    Learn how the ReAct pattern enables LLM agents to interleave reasoning with tool execution. Master thought-action-observation loops for autonomous AI systems.

  4. 04
    Tool Selection for LLM Agents: Routing Strategies and Implementation

    Master LLM tool selection through embedding-based routing, hybrid strategies, and semantic interfaces. Learn to build scalable multi-tool agent systems.

  5. 05
    Agent Architectures: Control Loops, State & Planning

    Master LLM agent architectures including control loops, state management strategies, planning mechanisms, and termination conditions for autonomous AI systems.

  6. 06
    Agent Memory Systems: From Context to Persistent Storage

    Learn how AI agents manage short-term context and long-term memory. Explore vector databases, retrieval algorithms, and memory hierarchies that enable persistent, learning systems.

  7. 07
    Agent Evaluation: Metrics, Benchmarks and Safety Standards

    Learn to evaluate AI agents with task completion metrics, trajectory analysis, and safety testing. Covers WebArena, SWE-bench, GAIA, and OSWorld benchmarks.

  8. 08
    Planning

    Covers task decomposition, goal-directed planning, plan execution, plan revision and recovery.

  9. 09
    Multi-Agent Systems

    Covers agent coordination, communication protocols, role assignment, multi-agent benchmarks.

  10. 10
    Agent Safety

    Covers unsafe action prevention, agent alignment, sandboxing, monitoring and intervention.

Part XLIX: Long-Horizon Agents and Interoperability

  1. 01
    Computer-Use and Browser Agents · In progress

    Screenshots, accessibility trees, action spaces, grounding, recovery, and evaluation on real interfaces.

  2. 02
    Coding and Software-Engineering Agents · In progress

    Repository navigation, test-driven changes, execution feedback, code review, and long-running tasks.

  3. 03
    Deep-Research Agents · In progress

    Search, source selection, evidence synthesis, citation integrity, uncertainty, and reproducible research traces.

  4. 04
    Durable and Asynchronous Agent Work · In progress

    Checkpoints, resumability, queues, idempotency, retries, deadlines, and state across long-running workflows.

  5. 05
    Model Context Protocol · In progress

    MCP clients, servers, tools, resources, prompts, capability negotiation, transport, and trust boundaries.

  6. 06
    Agent-to-Agent Protocols · In progress

    Discovery, task delegation, status, artifacts, interoperability, and failure semantics across agents.

  7. 07
    Human Approval and Control Boundaries · In progress

    Approval gates, reversibility, escalation, authority, interruption, and clear ownership of consequential actions.

  8. 08
    Long-Horizon Agent Evaluation · In progress

    Functional success, partial credit, recovery, cost, time, safety, and contamination-resistant task suites.

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Volume 16: Agents and AI Coding Systems

510 pages focused on this subject, with future updates included.

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Language AI Handbook, Volume 1: Language Data and Classical NLP cover

Volume 1 · 570 pages

Language Data and Classical NLP

Text as Data · Classical Text Representations · Distributional Semantics

Explore Volume 1
Language AI Handbook, Volume 2: Embeddings, Tokenization, and Sequence Labeling cover

Volume 2 · 790 pages

Embeddings, Tokenization, and Sequence Labeling

Word Embeddings · Subword Tokenization · Sequence Labeling

Explore Volume 2
Language AI Handbook, Volume 3: Linguistic Structure and Meaning cover

Volume 3 · Coming soon

Linguistic Structure and Meaning

Morphology · Syntax · Semantics · Discourse · Dialogue

Explore Volume 3
Language AI Handbook, Volume 4: Neural NLP and Sequence Models cover

Volume 4 · 920 pages

Neural NLP and Sequence Models

Neural Network Foundations · Recurrent Neural Networks · Sequence-to-Sequence

Explore Volume 4
Language AI Handbook, Volume 5: Transformers from First Principles cover

Volume 5 · 860 pages

Transformers from First Principles

Self-Attention · Positional Encoding · Transformer Blocks · Transformer Architectures

Explore Volume 5
Language AI Handbook, Volume 6: Efficient, Long-Context, and Alternative Architectures cover

Volume 6 · 510 pages

Efficient, Long-Context, and Alternative Architectures

Efficient Attention · Long Context

Explore Volume 6
Language AI Handbook, Volume 7: Pre-training Data and Scaling cover

Volume 7 · 760 pages

Pre-training Data and Scaling

Data Curation · Pre-training Objectives · Scaling Laws

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Language AI Handbook, Volume 8: Encoder, Multilingual, and Translation Models cover

Volume 8 · 440 pages

Encoder, Multilingual, and Translation Models

BERT and Variants · Encoder-Decoder Models

Explore Volume 8
Language AI Handbook, Volume 9: Decoder Language Models and Capabilities cover

Volume 9 · 730 pages

Decoder Language Models and Capabilities

GPT Architecture · Modern Decoder Models · Emergent Capabilities

Explore Volume 9
Language AI Handbook, Volume 10: Large-Scale Training and Sparse Models cover

Volume 10 · 920 pages

Large-Scale Training and Sparse Models

Training Infrastructure · Training Optimization · Mixture of Experts

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Language AI Handbook, Volume 11: Fine-tuning and Efficient Adaptation cover

Volume 11 · 730 pages

Fine-tuning and Efficient Adaptation

Fine-tuning Fundamentals · Parameter-Efficient Fine-tuning · Instruction Tuning

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Language AI Handbook, Volume 12: Alignment, Preferences, and Oversight cover

Volume 12 · 510 pages

Alignment, Preferences, and Oversight

Alignment and RLHF

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Language AI Handbook, Volume 13: Reasoning and Verifiable Learning cover

Volume 13 · 220 pages

Reasoning and Verifiable Learning

Reasoning

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Language AI Handbook, Volume 14: Efficient Inference and On-Device Models cover

Volume 14 · 640 pages

Efficient Inference and On-Device Models

Model Compression · Inference Optimization

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Language AI Handbook, Volume 15: Retrieval, Knowledge, and Memory cover

Volume 15 · 600 pages

Retrieval, Knowledge, and Memory

Retrieval-Augmented Generation · Continual Learning

Explore Volume 15
Language AI Handbook, Volume 17: Speech, Multimodal, and Embodied Language cover

Volume 17 · 540 pages

Speech, Multimodal, and Embodied Language

Multimodal Models · Speech and Audio

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Language AI Handbook, Volume 18: Evaluation, Factuality, and Reliability cover

Volume 18 · 950 pages

Evaluation, Factuality, and Reliability

Evaluation Fundamentals · Benchmark Evaluation · Human and Model Evaluation · Hallucination and Factuality

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Language AI Handbook, Volume 19: Responsible, Secure, and Interpretable Language AI cover

Volume 19 · 760 pages

Responsible, Secure, and Interpretable Language AI

Bias and Fairness · Interpretability · Safety and Security

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Language AI Handbook, Volume 20: Applications, Production, and Compound AI Systems cover

Volume 20 · 510 pages

Applications, Production, and Compound AI Systems

LLM Applications · Production Systems

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Author and edition details

About the author and this edition

Michael Brenndoerfer, author of Language AI Handbook

Michael Brenndoerfer

Michael has spent more than a decade working across software engineering, data, AI, and business. He writes to understand difficult ideas more deeply and to share what he learns in a clear, practical way.

Edition
First edition
Published
Last reviewed

Version history

Kept current, not frozen in time

Library access includes every future volume and update. When a volume changes, its updated PDF appears in My books at no extra cost.

Current release

Edition 2026.08.0

Initial volume-library release with 19 written PDFs, a Volume 3 placeholder, and no combined edition.