Why Are Coding Agents So Dumb?(mtlynch.io) |
Why Are Coding Agents So Dumb?(mtlynch.io) |
I let most agents work asynchronously and don't pay attention so I don't care that much about the sequential nature. But if it's a problem for you then fix your harness. This is a bit like saying "Why are shoes so shit? There's a stone in one and it just gets stuck there and your foot steps on it and it hurts". Take off the shoe, and shake out the rock. Put the shoe back on. You have the power.
A lot can be improved, but this is already so much speed.
I still occasionally have issues with open-weight models, but the frontier labs have solved the above for most use cases.
I run an LLM server with Qwen 3.6 in the office, and OpenCode, which the OP mentioned, usually defaults to sequential TODO lists, and it works fine with our little LLM server with 3-4 parallel users. But I noticed that once in a while the LLM got overloaded with requests in the queue, and you couldn't do anything for 20-30 minutes. My investigation led me to an employee who used QwenCode. I tried it myself then, and indeed, it immediately launched something like 6 parallel subagents, where OpenCode would have sequential TODOs with the same model by default.
So in the end, I had to detect QwenCode on the server side and serialize all its parallel requests into a single request queue, because it made life miserable for other OpenCode users :)
This is a pretty big failing, which is compounded by the fact that most humans don't know which model to pick, or make assumptions based on Anthropic's hierarchy or "effort" involved.
Like Fable: your toughest challenges. You mean, like Fields Medal toughest challenges? Or analyzing and updating three monster spreadsheet toughest challenges? Or writing a new novel in the style of William Gibson toughest challenges?
The descriptions are near-useless and tend to flip around, as model families are not released in sync anymore, that's true, but fortunately, thanks in a big way to subscription pricing, the choice is simple: start with the best model on offer, and when you run out of quota, downgrade to the next best (or briefly switch providers).
Have you tried telling models about your dream agent environment?
They can build it.
One thing that seems to help for me is to do the docs before plans (collaboratively edit with agent). Then I understand what this change is going to look like from the user's perspective before we start implementation. This seems to help keep things on track.
While I don't use this plugin a lot anymore, I think doc-driven development is one of the most effective ways to do development in any paradigm, I should probably refresh this plugin and use it more:
https://github.com/tmpdir-org/tmpdir-claude-code-marketplace...
I asked DeepSeek to translate a page to five languages and it opened five subagents each one working independently on the translation, once they all finished the main agent informed me of the job completion with a bell. Fantastic!
Sooo, which agent?
As expected. A model's knowledge is what was it ingested a creation.t
Unfortunately what we get is worse - for the same reason. Model version thinks it is its previous version.