Multi-Agent Systems in Production: Designing and Operating Collaborative AI Agent Architectures (AI Systems Architecture Series Book 16)
English | October 12, 2026 | ASIN: B0HMRN92P5 | 244 pages | Epub | 1.09 MB
One AI agent is a tool. A team of AI agents, coordinated well, is anorganization - and organizations are what get enterprise work done.Every team that has pushed AI agents into production has discoveredthe same truth: the moment the task gets big enough - a productlaunch, a fraud investigation, a client onboarding that touches sixsystems - the lone agent strains. It juggles too much context, makesdecisions outside its expertise, and becomes the bottleneck in its ownworkflow. The answer is the one human organizations discoveredcenturies ago: divide the labor. Give the research to a researcher,the code to a coder, the checking to a checker, and put coordinationin charge of it all.Multi-Agent Systems in Production is the definitive production manualfor building teams of AI agents that actually work. Across twelvecomprehensive chapters, you will learn:- Why agent teams win - and the honest accounting of when a singleagent is the better choice- Agent communication: message protocols, Agent-to-Agent (A2A)patterns, blackboard systems, and event buses- Hierarchical architectures: supervisors, planner-executor splits,escalation paths, and org-chart thinking- Decentralized swarms: stigmergy, market-based allocation, consensus,and emergent coordination- Task decomposition and distributed planning: DAGs, dynamicreplanning, and dependency management- Shared memory and state: blackboards, vector-backed knowledge,consistency, and conflict resolution- Coordination failures: deadlocks, delegation loops, actionconflicts, cascading errors - and recovery patterns for each- Observability: distributed tracing for agent teams, conversationreplay, eval harnesses, and red-teaming- Cost control: model routing, caching, batching, budget governors,and unit economics per task- Security and governance: prompt-injection propagation, privilegeseparation, audit trails, and compliance- Production deployment: infrastructure, scaling, canary agentversions, SLOs, and incident response- The road ahead: from agent teams to autonomous organizations, with athree-year adoption roadmapEvery chapter includes learning objectives, architecture diagrams,comparison tables, failure taxonomies, cost models, and chaptersummaries with key takeaways. It completes the ai systems architectureseries - agents (Book 1), knowledge (Book 2), tools (Book 3),automation discipline (Book 4) - with the force multiplier: manyagents, coordinated well, running in production. If you are ready tomove from impressive demos to agent teams that ship real workreliably, safely, and economically - this is your manual.
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