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I write very little code these days, so I've been following the AI development mostly from the backseat. One aspect I fail to grasp perfectly is what the practical differences are between CLI (so terminal-based) agents and ones fully integrated into an IDE. Could someone chime in and give their opinion on what are the pros and cons of either approach?
Genuine question: what's the evidence that the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? The author uses different models for each role, which I get. But I run production agents on Opus daily and in my experience, if you give it good context and clear direction in a single conversation, the output is already solid. The ceremony…
> what's the evidence What’s the evidence for anything software engineers use? Tests, type checkers, syntax highlighting, IDEs, code review, pair programming, and so on. In my experience, evidence for the efficacy of software engineering practices falls into two categories: - the intuitions of developers, based in their experiences. - scientific studies, which are unconvincing. Some are unconvincing because they atte…
See: OOP
Genuine question: what's the evidence that the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? The author uses different models for each role, which I get. But I run production agents on Opus daily and in my experience, if you give it good context and clear direction in a single conversation, the output is already solid. The ceremony…
> the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? There's a 63 pages paper with mathematical proof if you really into this. https://arxiv.org/html/2601.03220v1 My takeaway: AI learns from real-world texts, and real-world corpus are used to have a role split of architect/developer/reviewer
Genuine question: what's the evidence that the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? The author uses different models for each role, which I get. But I run production agents on Opus daily and in my experience, if you give it good context and clear direction in a single conversation, the output is already solid. The ceremony…
Well I was until the session limit for a week kicked in.
Genuine question: what's the evidence that the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? The author uses different models for each role, which I get. But I run production agents on Opus daily and in my experience, if you give it good context and clear direction in a single conversation, the output is already solid. The ceremony…
Context & how LLMs work requires this.
From my experience no frontier model produces bug free & error free code with the first pass, no matter how much planning you do beforehand.
With 3 tiers, you spend your token & context budget in full in 3 phases. Plan, implement, review.
If the feature is complex, multiple round of reviews, from scratch.
It works.
I write very little code these days, so I've been following the AI development mostly from the backseat. One aspect I fail to grasp perfectly is what the practical differences are between CLI (so terminal-based) agents and ones fully integrated into an IDE. Could someone chime in and give their opinion on what are the pros and cons of either approach?
Earlier quoted context omitted.
> what's the evidence What’s the evidence for anything software engineers use? Tests, type checkers, syntax highlighting, IDEs, code review, pair programming, and so on. In my experience, evidence for the efficacy of software engineering practices falls into two categories: - the intuitions of developers, based in their experiences. - scientific studies, which are unconvincing. Some are unconvincing because they atte…
You can measure customer facing defects. Also, lines of code is not completely meaningless metric. What one should measure is lines of code that is not verified by compiler. E.g., in C++ you cannot have unbalanced brackets or use incorrectly typed value, but you still may have off-by-one error. Given all that, you can measure customer facing defect density and compare different tools, whether they are programming lan…
Comparing lines of code can be meaningful, mostly if you can keep a lot of other things constant, like coding style, developer experience, domain, tech stack. There are many style differences between LLM and human generated code, so that I expect 1000 lines of LLM code do a lot less than 1000 lines of human code, even in the exact same codebase.
Genuine question: what's the evidence that the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? The author uses different models for each role, which I get. But I run production agents on Opus daily and in my experience, if you give it good context and clear direction in a single conversation, the output is already solid. The ceremony…
We used a hierarchy of agents to analyze a requirement, letting agents with different personas (architect, business analyst, security expert, developer, infra etc) discuss a request and distill a solution. They all had access to the source code of the project to work on.
Then we provided the very same input, including the personas' definition, straight to Claude Code, and we compared the result.
They council of agents got to a very good result, consuming about 12$, mostly using Opus 4.6.
To our surprise, going straight with a single prompt in Claude Code got to a similar good result, faster and consuming 0.3$ and mostly using Haiku.
This surely deserves more investigation, but our assumption / hypothesis so far is that coordination and communication between agents has a remarkable cost.
Should this be the case, I personally would not be surprised:
- the reason why we humans do job separation is because we have an inherent limited capacity. We cannot reach the point to be experts in all the needed fields : we just can't acquire the needed knowledge to be good architects, good business analysts, good security experts. Apparently, that's not a problem for a LLM. So, probably, job separation is not a needed pattern as it is for humans.
- Job separation has an inherent high cost and just does not scale. Notably, most of the problems in human organizations are about coordination, and the larger the organization the higher the cost for processes, to the point processed turn in bureaucracy. In IT companies, many problems are at the interface between groups, because the low-bandwidth communication and inherent ambiguity of language. I'm not surprised that a single LLM can communicate with itself way better and cheaper that a council of agents, which inevitably faces the same communication challenges of a society of people.
Earlier quoted context omitted.
> the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? There's a 63 pages paper with mathematical proof if you really into this. https://arxiv.org/html/2601.03220v1 My takeaway: AI learns from real-world texts, and real-world corpus are used to have a role split of architect/developer/reviewer
>> the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? > There's a 63 page paper with mathematical proof if you really into this. > https://arxiv.org/html/2601.03220v1 I'm confused. The linked paper is not primarily a mathematics paper, and to the extent that it is, proves nothing remotely like the question that was asked.
Genuine question: what's the evidence that the architect → developer → reviewer pipeline actually produces better results than just... talking to one strong model in one session? The author uses different models for each role, which I get. But I run production agents on Opus daily and in my experience, if you give it good context and clear direction in a single conversation, the output is already solid. The ceremony…
I think the splitting make sense to give more specific prompts and isolated context to different agents. The "architect" does not need to have the code style guide in its context, that actually could be misleading and contains information that drives it away from the architecture