Adaptive Multi-Agent Role Reassignment over Model Context Protocol for Resilient AI Orchestration

Multi-agent systems powered by large language models (LLMs) can automate complex workflows by dividing tasks among specialised roles such as research, critique and summarisation. Existing orchestration frameworks typically assign these roles statically throughout execution, making them brittle when agents fail or workloads fluctuate. This paper introduces Adaptive Role Reassignment (ARR), the first Model Context Protocol (MCP)-native protocol for real-time, context-preserving role switching in multi-agent LLM environments. ARR extends MCP with two primitives: RoleState, a serialised snapshot of an agent’s conversational state, tool usage and pending actions, and RoleSwap, a message type enabling secure hand-off of that state to a new agent. We describe the ARR architecture, present a decision policy for triggering role swaps based on performance and confidence metrics, and evaluate our approach on synthetic stress tests and real-world data-analysis and news-summarisation pipelines. Experiments show that ARR improves task completion rates by up to 28% and reduces recovery latency by over 35% compared to fixed-role baselines, while incurring negligible runtime overhead. A case study of a live news intelligence system illustrates how ARR mitigates bottlenecks and preserves context during agent failures. Our contributions demonstrate that adaptive, MCP-native role reassignment is a critical capability for resilient agentic AI orchestration.