Formal Methods for Multi-Agent Communication: Protocols, Types and Verified Systems

Modern AI agents can coordinate brilliantly-or fail spectacularly-depending on how they communicate. This book shows you how to turn vague, error-prone messaging into precise, verifiable conversations that scale from prototypes to production. Drawing on formal methods, distributed systems, and practical MLOps, you'll learn to specify what agents may say, when they should speak, and how to guarantee safety, privacy, and performance. Each chapter blends clear intuition with actionable patterns, lightweight DSLs, and deployment checklists-plus case studies from robotics, enterprise tooling, IoT/edge, and human-in-the-loop operations. What you'll learn Design typed messages and ontologies that make meaning unambiguous and evolution-safe. Capture conversations with protocol FSMs and (multi)party session types to prevent out-of-order or missing steps at compile time. Specify and verify ordering, liveness, timing, and knowledge properties using LTL/CTL, model checking, and runtime monitors. Apply Value-of-Information (VoI) policies to speak only when it helps-cutting bandwidth while improving outcomes. Build typed plan messages and safety checkers that turn messages into executable, auditable intent. Ship production systems with schema governance, idempotency, sagas, observability, and fault-tolerance. Protect users and orgs with information-flow control, typed credentials, attestation, differential privacy, and robust aggregation. Govern multi-agent ecosystems via contracts/SLAs, auctions/markets, reputation, and capability delegation. What's inside Clear primers on automata, temporal/epistemic logic, process calculi, session/MPST types, and compositional design. A practical contract DSL (obligations, permissions, prohibitions, deadlines, remedies) with runtime enforcement. VoI-gated communication and causal metrics (ACE) to prioritize messages that actually change decisions. End-to-end systems playbooks: middleware choices, schema evolution, supervisors/orchestration, traces & causal graphs. Case studies: warehouse swarms, enterprise agents for tickets/docs/payments, energy-aware IoT swarms, reproducible scientific workflows, and human-agent teams. Who should read this Software/ML engineers, distributed-systems practitioners, and architects building agent platforms. Researchers and graduate students seeking applied, safety-aware methods that move from proofs to production. Product and reliability leaders who need auditable, governable AI systems. About the author Dr. Karthik Kambhampati designs AI-driven cloud and multi-agent systems with a focus on safety, efficiency, and verifiability. He writes and mentors on bringing research-grade rigor to production systems. Build agents that communicate less but accomplish more-with conversations that are typed, verified, and safe by construction.