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 drags all of it around forever.…