Markdown industrial complex.

Basic issue is this:

1. The models are probabilistic in their output. This is objectively verifiable fact. Give same prompt, repeat and you'll get different output.

2. With these markdown heavy approaches, the system has to keep a track of the system state not just in code but in tons of markdown files as well.

3. As the system evolves due to features, bug fixes, the corresponding markdown files are to be updated as well to keep in sync.

4. This update consumes tokens for sure so now token spend goes through roof.

5. But because the models are probabilistic, the real problem is that it is guaranteed that some of the places would not be updated or updated with misinformation in those markdown files.

6. Next agent run when loading the context from these markdown files would have misinformation in its context that it would discover mid task, would have to rectify the situation. token spend would be quadrupled.

7. Only those edge cases of markdown files would hit that has been visited so there would be many inconsistencies still and they would pile up and compound with the passage of time.

8. Anyone who has significant experience with such approaches would testify to that.

9. There's no solution to that other than just using agents as power tools and holding the context and desired outcome in your own head.