GoKawiilShow HN: Raven debuts as a multi-agent orchestration harness for self-improving AI
A developer released Raven, a pre-alpha 'harness of harnesses' that coordinates specialized built-in agents (Research, Code, Design, Oncall) and third-party agents to complete complex tasks via generated task graphs. The project's showcase includes a fully autonomous Godot 4 shooter build and an RSI experiment where Raven ran 172 training runs across 7 rounds without crashing, cutting val_bpb by 5.8% under a fixed compute budget.
GoKawiil's interpretation of the reporting above, not reported fact.
The project suggests a path toward agents that not only execute tasks but iteratively revise their own orchestration logic, which could accelerate automation of multi-step engineering workflows if the approach generalizes beyond benchmarks. Because it's pre-alpha and self-reported, the performance claims and stability results have not been independently verified, so their real-world reliability remains uncertain.
- Raven orchestrates multiple specialized agents (research, code, design, on-call) through auto-generated task graphs.
- It supports recursive self-improvement, proposing and validating changes to its own agent harness over time.
- Demonstrations include a fully autonomous game-development project and an RSI experiment improving training efficiency by 5.8%.
Source: github.com, 2026-09-29
Published there as: “Show HN: Raven – The harness of harnesses, built for RSI”
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