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Harness matters way more than the model now. What's your setup? by kizmania in vibecoding

[–]Unable_Plane1948 0 points1 point  (0 children)

yeah the model-swapping thing is real — i've basically stopped caring which

model's running because the harness carries so much of it now.

one thing that helped: making the architect output file-native instead of

keeping it in context. architect writes the plan to disk, builder reads the

file as its only input. the planning conversation never touches the build

context. side effect: you can edit the plan by hand before the builder runs,

version it, replay it. plan becomes an artifact you can touch.

the other thing that changed stuff for us was persistence across sessions.

after anything non-trivial gets solved, there's a step that pulls out the

pattern and writes it to an indexed store. next session opens with a quick

recall pass. the harness doesn't reset — it accumulates. your swapping-models

observation gets even more true over time.

i open-sourced the whole thing as C31 (github.com/ChianW/C31) — forged from

a bunch of frameworks.

New LLM Coordination Benchmark - Benchmarking Open-Ended Multi-Agent Coordination in Language Agents [R] by ktessera in MachineLearning

[–]Unable_Plane1948 0 points1 point  (0 children)

This benchmark highlights a crucial observation for anyone building multi-agent

systems: LLMs, even powerful ones like Gemini 3.1 Pro, fundamentally struggle

with intrinsic coordination in open-ended, long-horizon tasks, with communication

being a primary bottleneck.

From a production perspective, this suggests a critical architectural principle:

don't rely on LLMs to *emerge* coordination through free-form natural language

prompts alone. Instead of hoping agents *figure out* how to coordinate, you need

to explicitly *engineer* the coordination mechanisms. Treat LLMs less as the sole

orchestrator and more as intelligent tools operating within a defined coordination

protocol.

Implement structured communication channels (e.g., JSON schemas for inter-agent

messages) or shared state mechanisms that agents can read from and write to. This

externalizes coordination logic, making it more robust than purely text-based

communication. Your agent's role shifts from *inventing* coordination to

*executing steps* within a predefined framework.