Great work summarizing the core principles — it’s a solid bridge between software deployment discipline and the emerging agent paradigm.
One suggestion: we could explore evolving this into a “12-Factor Agent 2.0”, shifting focus from deployment consistency to learning and data iteration.
Key ideas to consider:
-
Treat data pipelines and experience management as first-class citizens — ensuring clean, versioned, and traceable datasets.
-
Add a principle on feedback loops and reinforcement, connecting runtime metrics to model or policy updates.
-
Introduce a notion of “token economy”, where interaction data itself becomes the foundation for continuous improvement.
These ideas are partly inspired by my LLM thinking — detail in From Tokens to Intelligence: How LLMs Learn, Think, and Act
.
Would love to discuss how we can evolve this framework toward a learning-centric agent architecture.
Great work summarizing the core principles — it’s a solid bridge between software deployment discipline and the emerging agent paradigm.
One suggestion: we could explore evolving this into a “12-Factor Agent 2.0”, shifting focus from deployment consistency to learning and data iteration.
Key ideas to consider:
Treat data pipelines and experience management as first-class citizens — ensuring clean, versioned, and traceable datasets.
Add a principle on feedback loops and reinforcement, connecting runtime metrics to model or policy updates.
Introduce a notion of “token economy”, where interaction data itself becomes the foundation for continuous improvement.
These ideas are partly inspired by my LLM thinking — detail in From Tokens to Intelligence: How LLMs Learn, Think, and Act
.
Would love to discuss how we can evolve this framework toward a learning-centric agent architecture.