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GOD introduces a critical infrastructure layer for multi-agent systems by enabling real-time inspection, intervention, and reproducible experimentation within agent societies. This tool addresses the growing complexity of generative-agent environments where operators need to debug specific agent behaviors or test hypothetical scenarios without relying solely on static logs. By unifying live control commands with replay evidence in a local-first workflow, it significantly lowers the barrier for researchers to validate and share multi-agent experiments.
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GOD: Govern, Observe, and Direct - A Real-Time Control Room for Agent Societies

arXiv2 min read

arXiv:2608.27992v1 Announce Type: new Abstract: Generative-agent systems are easier to start than to inspect. A run can contain many agents, locations, messages, commands, and model calls, yet the operator often gets either a finished replay or raw logs. That makes it hard to ask why an agent moved, test a small intervention, or package a run for another researcher. GOD is a local-first control room for agent societies. From the same browser workflow, an operator can issue targeted questions or interventions and inspect the resulting replay state. The system combines a setup wizard, Agent Studio, Map Studio, a spatial replay interface, Ask and Intervene commands, and portable experiment, map, and agent packs. Its technical contribution is the command and artifact loop: live controls and replay evidence share the same operator command model, while package contracts separate scenario, map, and profile data from local runtime state. The public release includes hosted Smallville-style and PKU replays, the open-source repository, and downloadable packs. We evaluate this path on 15 completed run slots. Across the 14 intervention runs, 78 of 84 target-agent checks recorded the commanded destination, and 169 of 182 state answers matched a saved location or action string.

Abstract:Generative-agent systems are easier to start than to inspect. A run can contain many agents, locations, messages, commands, and model calls, yet the operator often gets either a finished replay or raw logs. That makes it hard to ask why an agent moved, test a small intervention, or package a run for another researcher. GOD is a local-first control room for agent societies. From the same browser workflow, an operator can issue targeted questions or interventions and inspect the resulting replay state. The system combines a setup wizard, Agent Studio, Map Studio, a spatial replay interface, Ask and Intervene commands, and portable experiment, map, and agent packs. Its technical contribution is the command and artifact loop: live controls and replay evidence share the same operator command model, while package contracts separate scenario, map, and profile data from local runtime state. The public release includes hosted Smallville-style and PKU replays, the open-source repository, and downloadable packs. We evaluate this path on 15 completed run slots. Across the 14 intervention runs, 78 of 84 target-agent checks recorded the commanded destination, and 169 of 182 state answers matched a saved location or action string.

9 pages, 5 figures. Accepted to the EMNLP 2026 System Demonstrations Track

Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2608.27992 [cs.AI]

(or arXiv:2608.27992v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.27992 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 300 words

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