6-agent system negotiates satellite collision avoidance in 4-day hackathon
u/Pristine_Quality1764 built PARLEY, a six-agent AI system that autonomously negotiates satellite conjunction collision avoidance maneuvers, shipping a working demo in four days at a hackathon.
Score breakdown
The build illustrates that inter-agent state management and context isolation — not model capability — are the primary engineering bottlenecks in real multi-agent systems.
- 01PARLEY is a six-agent AI system that autonomously negotiates satellite conjunction collision avoidance maneuvers.
- 02The six agents are: Sentinel, Oracle, Operator Alpha, Operator Bravo, Arbiter, and Archivist.
- 03The Arbiter uses a different, smaller model than the operator agents to ensure genuine neutrality.
u/Pristine_Quality1764 built PARLEY, a six-agent multi-agent system designed to autonomously negotiate satellite conjunction collision avoidance maneuvers, shipping a working demo in four days at a hackathon with no prior experience in the SDK used. The six agents each hold a distinct role: Sentinel monitors for conjunction risk, Oracle runs orbital mechanics and risk assessment, Operator Alpha and Operator Bravo each represent a satellite operator's interests, Arbiter acts as a neutral mediating party, and Archivist maintains a sealed audit trail of every decision made.
Day 1 was consumed almost entirely by environment setup and SDK debugging: wrong import names, doubled API base URLs, and constructor mismatches.
One of the more deliberate architectural choices was assigning a different, smaller model to the Arbiter specifically so it wouldn't share "instincts" with the operator agents — the goal being genuine neutrality rather than the same model effectively talking to itself. Day 1 was consumed almost entirely by environment setup and SDK debugging: wrong import names, doubled API base URLs, and constructor mismatches. By days 3–4, a full negotiation chain was running end-to-end, logging over 100 successful API calls and 5 sealed audit blocks. The landing page, voiceover script, and submission deck were all built on the final day — something the author noted they would front-load in future projects.
The central lesson was that the hardest part of building a multi-agent system wasn't the AI logic but rather state management between agents and ensuring each agent only received the context it was meant to have. The author frames this as a general challenge for any real multi-agent product, not specific to hackathon conditions.
Key facts
- 01PARLEY is a six-agent AI system that autonomously negotiates satellite conjunction collision avoidance maneuvers.
- 02The six agents are: Sentinel, Oracle, Operator Alpha, Operator Bravo, Arbiter, and Archivist.
- 03The Arbiter uses a different, smaller model than the operator agents to ensure genuine neutrality.
- 04The author had zero prior experience with the SDK used and spent Day 1 almost entirely on environment setup and debugging.
- 05By days 3–4, the system had completed over 100 successful API calls and produced 5 sealed audit blocks.
- 06The landing page, voiceover script, and submission deck were all built on the final day.
- 07The author's biggest lesson: the hardest problem was state management and context control between agents, not the AI logic.
Topics
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