AI Agent Engineering in Production

Building a basic AI wrapper is simple. Building an autonomous agent system that runs reliably, securely, and cost-effectively in a production environment at scale is a monumental engineering challenge. AI Agent Engineering in Production is the definitive, practical guide written specifically for software engineers, system architects, and tech leads who need to bridge the gap between fragile prototypes and resilient enterprise systems.

This book bypasses the high-level hype and basic API tutorials. Instead, it dives straight into the concrete architectural patterns, protocols, and best practices required to build and scale production-ready AI agent architectures.

Inside this comprehensive engineering handbook, you will discover:

• Model Context Protocol (MCP): Master Anthropic's open standard. Implement custom MCP servers, define tool schemas, and connect LLMs securely to proprietary databases.
• Agent Loop & Memory Architecture: Design robust ReAct loops and integrate sophisticated in-context, episodic, semantic, and vector-based memory systems.
• Multi-Agent Orchestration: Learn when and how to deploy supervisor, sequential pipeline, and peer-to-peer agent coordination topologies.
• Reliability & Observability: Mitigate non-deterministic failures using circuit breakers, advanced retries, Langfuse tracing, and OpenTelemetry tracking.
• Enterprise-Grade Security & Cost Control: Protect agents against prompt injection attacks and optimize running costs using token budgeting, semantic caching, and model routing.

Don't let your AI projects remain stuck in the demo phase. Equip yourself with the production engineering frameworks needed to build, test, and scale deterministic, secure, and highly efficient multi-agent systems today.