A lot of AI coding workflows degrade the exact same way.

At first, everything feels incredible.

Your coding agent:

understands the project

moves insanely fast

eliminates boilerplate

compounds your momentum

Then a few weeks later:

AGENTS.md turns into a novel.

Prompts get bloated.

The model starts missing obvious things.

Responses become inconsistent.

Token usage quietly becomes absurd.

I kept running into this while building Empirical .

Eventually I realized the problem wasn’t:

“The model needs more context.”

The problem was:

“The model is carrying too much irrelevant context at once.”

That distinction changed everything.

The Hidden Failure Mode of Coding Agents

Most teams solve AI memory like this:

“Just add it to the prompt.”

And over time the context fills up with:

Permanent Context Soup

architecture decisions

coding standards

deployment notes

UI preferences

old implementation details

temporary fixes

abandoned experiments

half-finished thoughts

Eventually every request…