Abstract
Contemporary multi-agent systems are predominantly designed around optimization, task decomposition, and explicit coordination protocols. While effective in structured environments, these paradigms struggle with long-horizon reasoning, irreversibility, and systemic constraints that are not directly observable. This paper introduces Deliberative Constraint-Field Multi-Agent Systems (DCF-MAS), a novel framework that reconceptualizes multi-agent coordination as a process of collective deliberation operating within latent constraint fields and irreversible state transitions. DCF-MAS departs from optimization-centric orchestration by introducing three foundational mechanisms: Shared Deliberative Substrates, Latent Constraint Fields, and Irreversible State Transition Engines. These mechanisms enable agents to reason collectively, adapt to hidden systemic limitations, and account for long-term consequences of decisions. The resulting systems are capable of maintaining coherence over extended temporal horizons while operating under uncertainty and partial observability. This framework establishes a new paradigm for multi-agent workspace management, emphasizing narrative consistency, constraint awareness, and structural evolution. It is positioned as a foundational architecture for enterprise-scale autonomous systems
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This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Manuel-Devadoss, Johny, "Deliberative Constraint-Field Multi-Agent Systems (DCF-MAS): A Framework for Fate-Constrained, Narrative-Consistent Autonomous Workspaces", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9899