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12 FACTOR AGENTS
Principles for building production-grade LLM applications
ABOUT
Building AI agents that work reliably in production requires more than connecting an LLM to a tool — teams must handle observability, prompt versioning, error recovery, cost management, security boundaries, and gradual rollout. Most agent frameworks provide building blocks but no guidance on operational patterns. The 12 Factor Agents methodology codifies best practices for production LLM applications: treating prompts as code, implementing observability by default, designing for graceful degradation, managing context windows, and structuring agent communication for auditability and debugging.
INTEGRATION GUIDE
1. Adopt production-ready patterns for observability, prompt versioning, and error recovery in agent applications
2. Design agent systems that gracefully degrade when LLM APIs fail or return low-confidence responses
3. Structure multi-agent communication patterns for auditability, debugging, and cost tracking
4. Apply gradual rollout and A/B testing strategies to agent behaviors without disrupting production users
TAGS
methodologybest-practicesproductionarchitecturellmagents