AI Agent should be treated like a human developer. If you bring a new human developer to your codebase and give them a task it will take a lot of time to read and understand the codebase before making proper solution. If you want to use AI agent regularly it makes sense to have some sort of memory of the codebase.
And it seems like community realizes it and invents different solutions. RooCode has task orchestration built in already, there is a claude task-manager that allows splitting and remembering tasks so AI agent can pick it up quicker, there are different solutions with files like memory bank. Windsurf cursor upgraded their .widsurf/rules functionality to allow more solutions like that for instructing AI agents about the codebase/tasks. Some people even write their own scripts that feed every file to LLM and store the summary description in the separate file that AI agent tool can use instead of searching codebase.
I'm eager to see how some of these solutions will become embedded into every AI agent solution. It's one of the missing stones to make AI agents order of magnitude more efficient and productive.
And it seems like community realizes it and invents different solutions. RooCode has task orchestration built in already, there is a claude task-manager that allows splitting and remembering tasks so AI agent can pick it up quicker, there are different solutions with files like memory bank. Windsurf cursor upgraded their .widsurf/rules functionality to allow more solutions like that for instructing AI agents about the codebase/tasks. Some people even write their own scripts that feed every file to LLM and store the summary description in the separate file that AI agent tool can use instead of searching codebase.
I'm eager to see how some of these solutions will become embedded into every AI agent solution. It's one of the missing stones to make AI agents order of magnitude more efficient and productive.