Building Agents That Run in Production

The playbook for shipping agents that don't break at 3amThis is not a tutorial. It's the architecture and operations playbook for building AI agents that run in production — the patterns, failure modes, and deployment strategies that separate demo agents from real ones.What's inside Architecture patterns — agent loops, tool orchestration, state management, multi-agent coordination Error handling — retry strategies, graceful degradation, circuit breakers for LLM calls Observability — logging, tracing, and monitoring for agents (not just the LLM calls) Testing — how to test agents deterministically, including tool mocking and scenario replay Deployment — CI/CD for agents, blue-green deployments, rollback strategies Security — sandboxing, permission models, secrets management, audit trails Cost management — token budgeting, caching strategies, model routing Platform coverageEvery pattern works with Claude Code and Hermes Agent. Includes platform-specific setup guides and configuration templates.Written by someone who ships agents in production, not someone who writes about them.

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