12-factor Agents: Patterns of reliable LLM applications (github.com)

Article summary

The article discusses the principles for building reliable LLM applications, introducing the concept of 12-factor agents. It highlights the importance of owning prompts, context windows, and control flow, as well as unifying execution state and business state. The author shares their experience with various agent frameworks and emphasizes the need for a more modular approach to building LLM-powered software. The 12 factors are outlined as a guide for developers to create more reliable and scalable LLM applications.

Main themes

• LLM applications
• Agent frameworks
• Modular software development
• Reliability and scalability
• Control flow and state management

What commenters say

• Most AI agents in production are not truly agentic, but rather well-engineered software with LLMs sprinkled in at key points.
• Using LLMs as libraries rather than frameworks can provide more control and flexibility for developers.
• The current state of LLMs is not yet suitable for fully autonomous agents, and more work is needed to achieve reliable and trustworthy AI systems.
• Some developers argue that frameworks can be useful for building LLM applications, but others believe that they can be limiting and inflexible.
• The importance of owning the lowest level planning loop and having heuristics for determining convergence on a solution is emphasized.
• There is a need for a new approach to building LLM applications that balances the need for customization with the need for abstraction and ease of use.
• Some commenters disagree on the usefulness of certain libraries and frameworks, such as Langchain and DSPY, for building LLM applications.
• The idea of prompting and evals may need to be rethought in order to achieve more impressive experiences in AI.