Safe and Policy-Compliant Multi-Agent Orchestration for Enterprise AI

Abstract

Enterprise AI systems increasingly deploy multiple intelligent agents across mission-critical workflows that must satisfy hard policy constraints, bounded risk exposure, and comprehensive auditability requirements such as SOX, HIPAA, and GDPR. Existing coordination methods—including cooperative multi-agent reinforcement learning, consensus protocols, and centralized planners—optimize expected reward while treating constraints implicitly. This paper introduces CAMCO (Constraint-Aware Multi-Agent Cognitive Orchestration), a runtime coordination layer that models multi-agent decision-making as a constrained optimization problem. CAMCO integrates three mechanisms: a constraint projection engine enforcing policy-feasible actions through convex projection, adaptive risk-weighted Lagrangian utility shaping, and an iterative negotiation protocol with bounded convergence. Unlike training-time constrained reinforcement learning, CAMCO operates as deployment-time middleware compatible with any agent architecture, with policy predicates designed for integration with production engines such as OPA. Evaluation across three enterprise scenarios, including comparison with a constrained Lagrangian baseline, demonstrates zero policy violations, risk exposure below threshold, 92–97% utility retention, and mean convergence in 2.4 iterations.