Agentic Hives: Equilibrium, Indeterminacy, and Endogenous Cycles in Self-Organizing Multi-Agent Systems

Researchers introduce the Agentic Hive framework for self-organizing multi-agent AI systems where autonomous micro-agents can be dynamically created, specialized, or destroyed based on resource availability and objectives. The framework applies economic theory to prove seven analytical results about equilibrium states, stability, and demographic cycles in variable AI agent populations.

• →The Agentic Hive framework enables dynamic creation and destruction of AI agents at runtime, unlike current fixed-population systems.
• →Each micro-agent operates in a sandboxed environment with language model access and undergoes demographic dynamics similar to biological populations.
• →Seven mathematical proofs establish conditions for equilibrium, stability, and cyclical behavior in multi-agent systems using economic theory.
• →The framework provides operators with predictive tools to steer the evolution of self-organizing AI agent populations.
• →Results partition parameter space into regions of unique equilibrium, indeterminacy, endogenous cycles, and instability.