Context is the bottleneck for coding agents now

There's a misunderstanding here broadly. Context could be infinite, but the real bottleneck is understanding intent late in a multi-step operation. A human can effectively discard or disregard prior information as the narrow window of focus moves to a new task, LLMs seem incredibly bad at this.

Having more context, but leaving open an inability to effectively focus on the latest task is the real problem.

I think that's the real issue. If the LLM spends a lot of context investigating a bad solution and you redirect it, I notice it has trouble ignoring maybe 10K tokens of bad exploration context against my 10 line of 'No, don't do X, explore Y' instead.

It seems possible for openAI/Anthropic to rework their tools so they discard/add relevant context on the fly, but it might have some unintended behaviors.

The main thing is people have already integrated AI into their workflows so the "right" way for the LLM to work is the way people expect it to. For now I expect to start multiple fresh contexts while solving a single problem until I can setup a context that gets the result I want. Changing this behavior might mess me up.

A number of agentic coding tools do this. Upon an initial request for a larger set of actions, it will write a markdown file with its "thoughts" on its plan to do something, and keep notes as it goes. They'll then automatically compact their contexts and re-read their notes to keep "focused" while still having a bit of insight on what it did previously and what the original ask was.

Interesting. I know people do this manually. But are there agentic coding tools that actually automate this approach?