I think he really think what then he using this tool he work better
Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down
Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers...
arxiv.org
I believe it but depending on your app and its complexity both in size and in component complexity there is a wide window where if an AI slowed you down, it’s a skill issue.
This is not about stateless prompts. In fact, the point of MCP is to provide context (=state) to the LLM. It is about the APIs that the LLM is supposed to use for gathering data and triggering actions.
It is a very good idea to make your APIs stateless if you can. This means that the server that serves your next request can be completely different from the one that served your last request. This makes life easier for everyone involved. Now having to wrap a stateful protocol around your stateless APIs does not seem to be good design.
You definitely want your APIs stateless but do you want your agent stateless?
My only experience with this other than day dreaming various software is that I’m doing a lot of refactoring of my company’s legacy web app using Augment Code, which I think uses MCP as how they run their Claude agent with your code base and its indexing of your code base as the objects it retrieves (as well as web search etc).
There’s two modes “agent mode” and “chat”. I think neither are stateless although chat mode acts like you’re just sending the LLM everything like a 2024 ChatGPT convo, but with 2025 coding agent skills (creating working files and showing you git style changes).
Agent mode is the same but keeps going and going, weirdly making it cheaper I guess because you are charged per your sent message which maybe has to resend the whole context whereas it running does not? Or maybe it’s just priced that way for other reasons. Using agent mode like it’s chat costs the same as chat but is way more effective.
You can give it much more complex instructions, and it will just run. So skill issue related to above, you have to demand it doesn’t do any coding, but writes long reports and the references these reports to make a plan and then writes a plan and then executes, and you gotta interrupt it frequently, which is expensive, but you know… keep it writing reports that it references and it can code with guidance.
I guess my point is that it’s leagues above using chat or Claude chat for coding because it’s keeping the conversation and its searches in state while you curse at and tell it it should be better. And I think it’s keeping the cost down keeping state and spinning off smaller chunks for xyz in the agent rules pipeline.
But this is all just what it feels like as a user not someone building it. And the article truthy linked obviously slaps and makes mcp sound bloated and useless so I am curious what kind of agents and control he
Like I could imagine an actually efficient agent that wasn’t just a 25k token prompt (cough Claude cough) with code listeners below but instead code listeners above sending json to and from stateless agents doing defined tasks. That should be more computer efficient and “safer” but harder to code and make “alive” like my augment agent refactoring 60 pages of legacy code to help me switch our 120 nested navigation pages from drop downs to simple sidebar.
