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What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
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12-Factor Agents is a set of principles for building reliable LLM-powered applications, inspired by the 12 Factor App methodology. It provides guidelines for creating production-grade AI agents that are scalable, maintainable, and robust.
Key Features
- Defines 12 core factors for building reliable LLM applications, from owning prompts to stateless reducers. - Provides a visual navigation and detailed content for each factor. - Includes community contributions and discussions for practical implementation.
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
Developer Tool
Tools that improve coding, testing, build, and local workflow
Automation
Workflow automation, integration glue, and orchestration
UI Framework
Frontend frameworks, design systems, and interface libraries
No installation required; the project is a guide. For the CLI tool, run `npx create-12-factor-agent` or `uvx create-12-factor-agent`.70
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Mar 30, 2025
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Sep 21, 2025
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humanlayer
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Problem Solved
It addresses the lack of reliability and production-readiness in current agent frameworks by advocating for a software-first approach where LLMs are used as components within deterministic systems. This contrasts with the 'prompt + tools + loop' pattern that often fails in production.
Capabilities
Developers can build customer-facing AI applications that are reliable, scalable, and maintainable, such as customer support bots, code assistants, or data processing pipelines. The ceiling is high: any application where LLM capabilities are needed but must be tightly controlled and integrated with existing software engineering practices.
Bottom Line
This guide is for developers who want to build production-grade LLM applications without the fragility of current agent frameworks. It's not for those seeking quick prototypes or fully autonomous agents. The key trade-off is more upfront engineering effort for significantly higher reliability and maintainability.