The Agentic Observer Hackathon: Autonomous Decision-making in Simulated Astronomical Surveys

Abstract

Astronomical surveys must allocate observing time while responding to changing conditions and competing scientific priorities. We present the Agentic Observer Hackathon (AOH), a community challenge for studying autonomous survey operations. Within a synthetic multi-object survey with prescribed exposure responses and assignment constraints, decision systems select pointings, assign targets and choose exposure configurations while responding to observing requests, instrumental faults and data loss. We evaluate systems submitted by 102 teams on campaigns reserved for evaluation and analyse target completion in a reference campaign. The leading systems combine numerical exposure planning with language models for interpreting operational information and supporting longer-term decisions. Exploratory tests show improved fault reporting with model access in several systems, while revised scientific priorities produce substantial changes in ranking. The reference campaign traces target completion and diagnoses errors in the baseline's inference of prescribed quality bands. The released simulator and reference strategies provide common survey conditions for comparing exposure allocation, target selection and recovery from interruptions.