Ask HN: Agents Craving Context

Hey, I was an early adopter of the Cursor IDE and pretty much installed it day 1 of it's public release. Like many I've been using LLMs to help accelerate the writing of code in my own company and side projects and have been quite optimistic and satisfied with the quality and experience of using the Cursor IDE chat -> composer -> agents to enhance the developer experience.

However, recently it seems like there has been a shift in how agents work (I am mainly referring to using Cursor agents here but the seems to also apply for Claude Code). When I introduce a problem to the agent a while ago it used to just start writing code. I remember having to be explicit about selecting the files or even lines I wanted it to edit or generate code in, and be quite precise in supplying the LLM with the relevant context. These days I will write a similar prompt to what I would do before: "Refactor X.ts into separate files /utils/[module].ts using the /shared/utils directory as an example". (just a toy example)

Now, instead of just reading the examples I feed it and the context I provide, the agent will read the files and notice the imports, it reads into which imports may be important and then goes into the node_modules and reads the library code and then realizes there are docs linked and then it asks to fetch the docs linked there then in the docs there is a reference to another library so it needs to fetch that library which links to a Github page which it needs to read before realiing.. wait what was the task again? Oh yeah refactoring this. But wait now I need to read all the files that rely on this to make sure that I can refactor those correcly once I make these changes... and so on.

This was not the case even just 1 year ago. Before I was able to do a clear job defining the task and the LLM would do the task with the provided information. Sometimes I would provide the wrong information and it would do the task wrong and I could undo, fix the information and have to do the task again, or I could manually go in a fix the issues. Now, the agents are consuming huge amounts of context and taking incredibly large amounts of time to produce "correct" code where in my experience the 1-shot accuracy seems to only have marginally improved due to the fact that when they finally get to the step of generating code there is so much extraneous context in their prompting that can cause artifacts or issues in the actual generated solution, not to mention the fact that a simple request will take maybe a full minute to change some code in 1 file...

Anyways apologies for the rant but I was wondering if anyone else is experiencing similar changes to the AI coding space and if they have come up with solutions for it? (Caveman mode maybe ? :))