Agentic Traffic Control: Orchestrating AI Agents Across Enterprise Systems

As organizations move from single-agent to multi-agent enterprise deployments, the absence of orchestration methodology produces the agentic equivalent of gridlock: agents blocking each other, corrupting shared state, producing unattributable output. This paper proposes Agentic Traffic Control (ATC) — a methodology for orchestrating multiple AI agents across enterprise systems using five operational layers (signal control, lane separation, junction protocol, priority routing, audit attribution) plus a learning layer (GESA) that optimises pipeline configuration across episodes. The central architectural choice is between the traffic light model (central orchestrator, predictable, human-gated) and the roundabout model (local negotiation, resilient, higher throughput). The pattern is already present in working production systems — Project Phoenix, Strata, EMBER, Wake Intelligence, and Rune Protocol — without yet having a unified name. This document names it.