The context degradation problem gets much worse when you have multiple agents or models touching the same project. One agent compacts, loses what it knew, and now the human is the only source of truth for what actually happened vs what was reported done. If that human isn't a coder, they can't verify by reading the source either.

I've been working on this and landed on a pattern I call a "mechanical ledger", basically a structured state file that sits outside any context window and gets updated as a side effect of work, not as a step anyone remembers to do. Every commit writes to it, every failed patch writes to it, every test run writes to it. When a session starts (or an agent compacts), it reads the ledger and rebuilds context from ground truth instead of from memory.

Its not a novel idea really, its basically what ops teams do with runbooks and state files, but applied to the AI agent handoff problem. The interesting bit is making the updates mechanical so no agent can forget to do it.

> A smarter model would be great but there are bigger productivity gains to be had with a good set up, a faster model, and abstracting away the need to think about agents or context usage. I’m still figuring out a good set up. Something with the speed of Haiku with the reasoning of Opus without the overhead of having to think about the management of agents or context would be sweet.

I was thinking about this recently. This kind of setup is a Holy Grail everyone is searching for. Make the damn tool produce the right output more of the time. And yet, despite testing the methods provided by the people who claim they get excellent results, I still come to the point where the it gets off rails. Nevertheless, since practically everybody works on resolving this particular issue, and huge amounts of money have been poured into getting it right, I hope in the next year or so we will finally have something we can reliably use.