Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents
by Victor Dibia
Build a complete multi-agent framework from scratch, then use it for workflows, orchestration, computer use, evals and MCP/A2A.
Overview
Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents is a roughly 395-page, 15-chapter book first published on 14 November 2025 by Victor Dibia, PhD, a Principal Research Software Engineer at Microsoft Research, creator of AutoGen Studio and a core contributor to the AutoGen framework, previously at Cloudera and IBM Research. It is organised in four parts: Foundations (the first three chapters cover theory, design patterns and UX principles for agents such as capability discovery, cost-aware delegation, observability and interruptibility), Building (your first agent, tools, memory, middleware, deterministic workflows, autonomous multi-agent orchestration and modern web UX for agent apps), Evaluating and Optimizing (evaluating agent trajectories and optimisation strategies), and Real-World Applications (answering business questions from unstructured data with a Y Combinator company analysis, computer-use agents that drive UIs, and a software-engineering agent). Throughout, the reader builds PicoAgents, an Apache-2.0 Python library in the companion GitHub repo (1.3k stars) with agents, workflows, orchestration, an eval module, a web UI and optional MCP and computer-use extras, runnable against OpenAI, Azure OpenAI, Anthropic, GitHub Models or any OpenAI-compatible local endpoint. The book is kept current: chapters 4 and 15 were updated in March 2026 for middleware signatures and execution traces, and in August 2026 the protocols chapter was revised for the wire-incompatible MCP 2026-07-28 specification, with slide decks added for every chapter. Digital buyers get lifetime updates in PDF and EPUB; print is on Amazon.
At a Glance
- Topic
- Agentic
- Level
- Intermediate
- Format
- Book
- Cost
- Paid
- Duration
- ~395 pages, 15 chapters, plus companion code
- Provider
- Victor Dibia
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Build a working agent execution loop with tools, memory and middleware from scratch
- ✓Design deterministic multi-agent workflows and know when to prefer them over autonomy
- ✓Implement autonomous multi-agent orchestration patterns and reason about their failure modes
- ✓Apply agent UX principles like interruptibility, observability, provenance and cost-aware delegation
- ✓Evaluate multi-agent systems by scoring trajectories rather than only final answers
- ✓Connect distributed agents using MCP (updated for the 2026-07-28 spec) and A2A
- ✓Build a computer-use agent that automates graphical user interfaces end to end
- ✓Assemble complete applications: a data-analysis workflow and a software-engineering agent
Highlights
- •Written by the creator of AutoGen Studio and an AutoGen core contributor at Microsoft Research
- •Framework-agnostic: you build PicoAgents yourself, so the concepts transfer to LangGraph, CrewAI or the OpenAI Agents SDK
- •Actively revised: the MCP chapter was rewritten in August 2026 for the 2026-07-28 spec; digital copies get lifetime updates
- •Companion repo (Apache-2.0, 1.3k stars, 275 forks) maps chapters to runnable code across OpenAI, Anthropic and local models
- •186 code snippets, 50 figures and per-chapter slide decks
Who It’s For
Best For
- ✓AI engineers who use agent frameworks but want to understand what happens inside them
- ✓Tech leads choosing between workflows and autonomous multi-agent designs
- ✓Developers building computer-use, research or coding agents for production
- ✓Engineers who need an evaluation methodology for multi-step agent behaviour
Prerequisites
- •Comfortable intermediate Python (classes, async code, virtual environments)
- •Basic familiarity with calling an LLM API and tool/function calling
- •An API key for OpenAI, Azure OpenAI, Anthropic or a local OpenAI-compatible model server
FAQ
What is Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents?
Designing Multi-Agent Systems is a 15-chapter book by Victor Dibia, creator of AutoGen Studio, for engineers who want to understand AI agents from first principles instead of through a framework. You build PicoAgents, a minimal but feature-complete multi-agent library, and finish able to design, evaluate and ship agent workflows, orchestrated teams and computer-use agents.
Is Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents free?
Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents is a paid resource.
What level is Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents for?
Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents is aimed at a intermediate audience. Recommended background: Comfortable intermediate Python (classes, async code, virtual environments), Basic familiarity with calling an LLM API and tool/function calling, An API key for OpenAI, Azure OpenAI, Anthropic or a local OpenAI-compatible model server.
How long does Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents take?
Expect roughly ~395 pages, 15 chapters, plus companion code. Most learners work through it at their own pace.
What will I learn from Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents?
You'll learn: Build a working agent execution loop with tools, memory and middleware from scratch; Design deterministic multi-agent workflows and know when to prefer them over autonomy; Implement autonomous multi-agent orchestration patterns and reason about their failure modes; Apply agent UX principles like interruptibility, observability, provenance and cost-aware delegation; Evaluate multi-agent systems by scoring trajectories rather than only final answers; Connect distributed agents using MCP (updated for the 2026-07-28 spec) and A2A; Build a computer-use agent that automates graphical user interfaces end to end; Assemble complete applications: a data-analysis workflow and a software-engineering agent.
Topics
Sources
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