same experience. Editor agents with basic level tasks and boilerplate. Problem solving and decomposition of it in the chat apps seems to be better. What I've noticed is the agent modes of these editors use the embeddings API of ChatGPT and how the LLM model maps the context of the codebase. Often what has happened is the LLM's in Agent mode ignore the default setup of the codebase example package managers and use the package managers which would be popular throughout their training data.

To summarise the Agent Mode editors don't try to fill in their context gaps and be aware of the environment they operate it unless explicitly specified by the prompter to first review the codebase, understand it's structure and then proceed with implementing features.