DOI:
https://doi.org/10.64539/sjcs.v1i2.2025.327Keywords:
Multi-Agent Systems, Agent Orchestration, Communication Backbone, Scalable AI, System Observability, Architectural PatternAbstract
As multi-agent AI systems evolve from prototypes to production-grade enterprise applications, the need for a robust and scalable communication architecture has become an operational imperative. However, traditional approaches like linear chaining or ad-hoc peer-to-peer messaging result in brittle, unmanageable systems that lack observability and fail to handle the non-deterministic nature of AI agents. To address this architectural deficiencies, this paper introduces the Message, Context, and Protocol (MCP) framework, an architectural pattern designed to serve as a communication backbone and "central nervous system" for complex AI systems by decoupling agent intent from execution. Performance evaluations under simulated enterprise load demonstrate that by decoupling agents through a central message bus and a stateful orchestrator, MCP maintains system resilience and prevents catastrophic failure even under high load (500 req/sec), although the orchestrator itself is identified as a primary bottleneck requiring horizontal scaling. These results underscore that centralized state management is not merely an option but a necessity for enterprise AI, providing the modularity and fault tolerance required to transition agentic workflows from experimental concepts to reliable business solutions.
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