Comment by CharlieDigital
7 hours ago
I use the frontier models only in plan mode; coding is held out to cheaper models.
I run Paseo as an orchestrator with agent profiles. "Distinguished Member of Technical Staff" -> Fable, plan mode. "End-User Customer Advocate" -> Astra, plan mode. A few others. Basically, these agents (with profile prompts) can only be "consulted" for input on design elements; it's OK if they produce a bit of verbosity here. This has worked fantastically for me, really awesome to see a self-constructed team own the work end-to-end.
A "Director of Engineering" (6.1 Sol or Opus 5.5) also in plan mode organizes the team for the task and then owns coordination and instruction. You can think of this in essence like a "model router".
Coding is handled by Haiku 5.5/GPT 6 Luna/DeepSeek v4.1 Flash. One "Principal Engineer" Sonnet 5.5 or 6.1 Sol for some tasks.
You are deep in it.
You may be larping for no additional benefit vs just using Opus 5.5 and just asking it to implement a feature.
You are correct, at least based on my experimentation. This subagent workflow really only delivers meaningful results when working across model families (ex: Opus asking Astra for a review) or when working across conceptual hardware boundaries (cloud offloading to local inference, etc). The planner / overseer model ends up reading all the output of the coder model anyway, at least if you want quality output, so there's not much benefit otherwise.
I mean, even that's fine because output tokens cost ~5x input tokens. So that's still a win if the agent that does the heavy code reading and writing produces output that may be read again by the lead (not my observation of how agents in Paseo interact and of course, correctable to degrees with shared system prompt).
Here's a snippet of a sample prompt downwards from the lead for a piece of code I'm working on:
The agent is given explicit "comms" instructions upwards and I find that both agents in this exchange stick to the contract.
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Nah; I can use my token budget across both providers this way, use tokens more sparingly with smaller models for heavy text in/out (why pay Opus prices to babysit a PR? run tests?), and you'd be surprised when the agents talk to each other and "role play" how much this improves things like the UX as well as technical design which are more subjective.
Distinguished Member of Technical Staff, for example, is asked to "expand the solution space" which I find has a strong effect on approaches considered (it doesn't know what was already considered on the main thread; only the domain and problem space).
This also helps with context growth since the sub-agents do the heavy lifting (e.g. search, read docs) and just provide the key bits back to the lead.
Like you, I was a single-model, single-TUI user up until three weeks ago when I picked up Paseo and it is the only tool that I've found that has significantly positively improved my throughput without sacrificing quality of product.
Is it "larping"? I ran an eval today on a contract ingest and extract agent I'm working on. Changing the <role> fragment of the prompt lifted the results by +7 points by simply changing the text to the effect of "You are a senior partner at a prestigious law firm that focuses on reading licensing contracts..." and some bits about the content the agent is reviewing + objective. No one would believe it without an eval.
Claude docs explicitly say to LARP[0]:
My conclusion: the agents like to LARP so I'm happy to LARP with them.
[0] https://platform.claude.com/docs/en/build-with-claude/prompt...
Why stop at "You are a senior partner at a prestigious law firm that focuses on reading licensing contracts..." why not "You are super programmer/developer that has been programming with Ada using punchcard, having Linus on quick dial, you can smell BS code from miles away and know exactly which question to ask".
I hate to be pedantic, but LARP stands for live action role playing.
Only mentioning it because LLMs are already taking over software development and everything online. Let's keep them out of our meet space peculiarities (1) while we can. ;)
1: https://epicarmoury.com/blogs/news/what-is-larp
He has to be deep in it because it’s the product he sells.
Paseo is open source (https://github.com/getpaseo/paseo, Apache 2.0) and not my project; not affiliated.
I have other products and I work for private equity (no affiliation with Paseo at all).
You've entirely missed the lede.
No "may be" about it. There is no benefit to having bad models write your code, and there's no benefit to "plan" mode. Plan mode is an outdated 2025 concept that should be removed from standard harnesses.
There are 2 scenarios for dumb builder agents:
1. Your "planner" actually implements the project almost completely, finds all the gotchas, solves all of the emergent problems, and fully plans the solution.
2. Your "planner" takes a half-assed first attempt, solving none of the major problems that will arise during implementation
In scenario 1 -- just pay cache output on a few more tokens to finish the project. No need to spend twenty minutes pumping a few million tokens through deepseek regardless of how cheap it is to do so.
In scenario 2 -- good luck getting quality output
I do think this "software team" LARPing is an engineering version of AI psychosis. You can get all of the little parts humming along, sending messages, and looking really cool. You can even convince yourself that all of these busy little bees actually make better product.
But a frontier model working alone can produce better output in a tiny fraction of the time. Just build the thing.
Try it. 3 weeks ago I was in the same boat. Single model, TUI. I only tried Paseo primarily to test multi-host orchestration (after giving up on Herdr) and not even multi-agent, multi-plat behavior.
Output quality, especially subjective bits, has improved relative to my attention needed. Autonomous decision making is much, much better for me adding one profile that specifically is instructed to focus on UI/UX, one for end user proxy, and one for MTS.
It costs you nothing to try except maybe 15 minutes of setup. Give it a solid, skeptical week. It's good even without the profiles, but way better with.
Try my profiles as a starting point: https://gist.github.com/CharlieDigital/1c496a2799862795f0c31...
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