Components of a Coding Agent
magazine.sebastianraschka.com
Components of a Coding Agent
1–10 of 120 posts
Re: Components of a Coding Agent
#2[dead]
Re: Components of a Coding Agent
#3I still find it incredible at the power that was unleashed by surrounding an LLM with a simple state machine, and giving it access to bash
Re: Components of a Coding Agent
#4I still find it incredible at the power that was unleashed by surrounding an LLM with a simple state machine, and giving it access to bash
Tools gave humans the edge over other animals.
Re: Components of a Coding Agent
#5I still find it incredible at the power that was unleashed by surrounding an LLM with a simple state machine, and giving it access to bash
unfortunately all the agent cli makers have decided that simply giving it access to bash is not enough. instead we need to jam every possible functionality we can imagine into a javascript “TUI”.
Re: Components of a Coding Agent
#6> This is speculative, but I suspect that if we dropped one of the latest, most capable open-weight LLMs, such as GLM-5, into a similar harness, it could likely perform on par with GPT-5.4 in Codex or Claude Opus 4.6 in Claude Code.
Unless I'm misunderstanding what's being described here, running Claude Code with different backend models is pretty common.
https://docs.z.ai/scenario-example/develop-tools/claude
It doesn't perform on par with Anthropic's models in my experience.
Re: Components of a Coding Agent
#7> This is speculative, but I suspect that if we dropped one of the latest, most capable open-weight LLMs, such as GLM-5, into a similar harness, it could likely perform on par with GPT-5.4 in Codex or Claude Opus 4.6 in Claude Code. Unless I'm misunderstanding what's being described here, running Claude Code with different backend models is pretty common. https://docs.z.ai/scenario-example/develop-tools/claude It doe…
> It doesn't perform on par with Anthropic's models in my experience.
Why do you think that is the case? Is Anthropic's models just better or do they train the models to somehow work better with the harness?
Re: Components of a Coding Agent
#8I still find it incredible at the power that was unleashed by surrounding an LLM with a simple state machine, and giving it access to bash
At it's heart it's prompt/context engineering. The model has a lot of knowledge baked into it, but how do you get it out (and make it actionable for a semi-autonomous agent)? ... you craft the context to guide generation and maintain state (still interacting with a stateless LLM), and provide (as part of context) skills/tools to "narrow" model output into tool calls to inspect and modify the code base.
I suspect that more could be done in terms of translating semi-naive user requests into the steps that a senior developer would take to enact them, maybe including the tools needed to do so.
It's interesting that the author believes that the best open source models may already be good enough to complete with the best closed source ones with an optimized agent and maybe a bit of fine tuning. I guess the bar isn't really being able to match the SOTA model, but being close to competent human level - it's a fixed bar, not a moving one. Adding more developer expertise by having the agent translate/augment the users request/intent into execution steps would certainly seem to have potential to lower the bar of what the model needs to be capable of one-shotting from the raw prompt.
Re: Components of a Coding Agent
#9> This is speculative, but I suspect that if we dropped one of the latest, most capable open-weight LLMs, such as GLM-5, into a similar harness, it could likely perform on par with GPT-5.4 in Codex or Claude Opus 4.6 in Claude Code. Unless I'm misunderstanding what's being described here, running Claude Code with different backend models is pretty common. https://docs.z.ai/scenario-example/develop-tools/claude It doe…
> It doesn't perform on par with Anthropic's models in my experience. Why do you think that is the case? Is Anthropic's models just better or do they train the models to somehow work better with the harness?
It's a good question, I've wondered that myself. I haven't used GLM-5 with CC but I've used GLM-4.7 a fair amount, often swapping back and forth with Sonnet/Opus. The difference is fairly obvious - on occasions I've mistakenly left GLM enabled running when I thought I was using Sonnet, and could tell pretty quickly just based on the gap in problem solving ability.
Re: Components of a Coding Agent
#10A timely link - I've just spent the last week failing to get a ChatGPT Skill to produce a reproducible management reporting workflow. I've figured out why and this article pretty much confirms my conclusions about the strengths & weaknesses of "pure" LLMS, and how to work around them. This article is for a slightly different problem domain, but the general problems and architecture needed to address them seem very similar.