Self-Evolving Coordination Protocol in Multi-Agent AI Systems: An Exploratory Systems Feasibility Study

TL;DR

This paper explores the feasibility of self-evolving coordination protocols in multi-agent systems, demonstrating that bounded self-modification under formal constraints is possible, auditable, and can improve proposal acceptance rates.

Contribution

It introduces Self-Evolving Coordination Protocols (SECP) and shows they can be designed to permit limited self-modification while maintaining formal invariants in multi-agent systems.

Findings

Self-modification increased proposal coverage from two to three accepted proposals.

All coordination regimes operated under identical formal constraints including Byzantine fault tolerance.

The study confirms the technical feasibility of bounded self-modification in coordination protocols.

Abstract

Contemporary multi-agent systems increasingly rely on internal coordination mechanisms to combine, arbitrate, or constrain the outputs of heterogeneous components. In safety-critical and regulated domains such as finance, these mechanisms must satisfy strict formal requirements, remain auditable, and operate within explicitly bounded limits. Coordination logic therefore functions as a governance layer rather than an optimization heuristic. This paper presents an exploratory systems feasibility study of Self-Evolving Coordination Protocols (SECP): coordination protocols that permit limited, externally validated self-modification while preserving fixed formal invariants. We study a controlled proof-of-concept setting in which six fixed Byzantine consensus protocol proposals are evaluated by six specialized decision modules. All coordination regimes operate under identical hard constraints, including Byzantine fault tolerance (f < n/3), O(n2) message complexity, complete non-statistical safety and liveness arguments, and bounded explainability. Four coordination regimes are compared in a single-shot design: unanimous hard veto, weighted scalar aggregation, SECP v1.0 (an agent-designed non-scalar protocol), and SECP v2.0 (the result of one governed modification). Outcomes are evaluated using a single metric, proposal coverage, defined as the number of proposals accepted. A single recursive modification increased coverage from two to three accepted proposals while preserving all declared invariants. The study makes no claims regarding statistical significance, optimality, convergence, or learning. Its contribution is architectural: it demonstrates that bounded self-modification of coordination protocols is technically implementable, auditable, and analyzable under explicit formal constraints, establishing a foundation for governed multi-agent systems.