About this book:

*Scaling Agent Teams* is a technical treatise on the orchestration and resource management required to coordinate large-scale, heterogeneous AI agent fleets. The book argues that as AI deployments shift from single-agent prototypes to massive, cooperative "swarms," the primary bottleneck moves from individual model capability to system-level effectiveness. It frames orchestration (decision-making and coordination) and resource management (the allocation of CPU, GPU, memory, and bandwidth) as inseparable disciplines necessary to maintain reliability, efficiency, and cost-effectiveness at "planet scale."

The text details a variety of architectural patterns—centralized, hierarchical, and market-based—to organize agent labor. It covers essential distributed systems primitives such as RPC, Pub/Sub, and Blackboards for communication, alongside strategies for task decomposition and dependency-aware scheduling. A significant portion of the book is dedicated to operational resilience, exploring fault-tolerance techniques like idempotent retries, checkpointing, and replication. It also introduces "Chaos Engineering" and simulated stress testing as vital methods for uncovering emergent failure modes in complex, autonomous systems before they reach production.

Beyond technical infrastructure, the book emphasizes the economic and governance dimensions of scaling. It introduces internal market models where agents "bid" for resources, alongside robust cost attribution and autoscaling policies to manage cloud expenditures. The final sections address the necessity of human-in-the-loop oversight, ensuring that as agent fleets gain autonomy in sensitive sectors like finance or infrastructure, they remain aligned with ethical standards and legal frameworks. Through various case studies, the book provides a roadmap for evolving AI from isolated tools into a coordinated, global digital nervous system.