Patterns for Building AI Agents by Sam Bhagwat

Patterns for Building AI Agents by Sam Bhagwat

The core thesis of Patterns for Building AI Agents is that the gap between a flashy proof-of-concept prototype and a reliable, production-ready system is where most enterprise AI initiatives collapse. While Principles outlined what blocks are required to construct an agentic system, Patterns delivers the pragmatic, battle-tested playbook on how to wire them together so they survive real-world corporate traffic. Bhagwat presents a core engineering reality: monolithic, unguided "mega-agents" naturally degrade in reliability, overspend token budgets, and scale poorly.

Drawing heavily from lessons learned designing production agent frameworks at Mastra, Bhagwat guides readers through a comprehensive, code-driven matrix. The text explores structural patterns for mapping whiteboard capabilities into modular, specialized agent hierarchies, managing complex context engineering pipelines, and treating evaluation (evals) as strict continuous integration criteria. Rather than relying on fragile prompt tweaks, the book implements structured development patterns to stop infinite model loops, isolate external tool calling within sandboxed execution environments, and orchestrate deterministic routing logic across decentralized networks.