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

TL;DR

This paper presents CAMCO, a runtime multi-agent orchestration framework ensuring policy compliance, risk bounds, and auditability in enterprise AI systems, outperforming existing methods.

Contribution

CAMCO introduces a novel deployment-time middleware for multi-agent decision-making that enforces hard constraints and adapts risk management without retraining.

Findings

Zero policy violations achieved in all scenarios.

Risk exposure maintained below threshold with mean ratio 0.71.

High utility retention of 92-97% across evaluations.

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 (SOX, HIPAA, GDPR). Existing coordination methods - cooperative MARL, 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: (i) a constraint projection engine enforcing policy-feasible actions via convex projection, (ii) adaptive risk-weighted Lagrangian utility shaping, and (iii) an iterative negotiation protocol with provably bounded convergence. Unlike training-time constrained RL, CAMCO operates as deployment-time middleware compatible with any agent architecture, with policy predicates designed for direct integration with production engines such as OPA. Evaluation across three enterprise scenarios - including comparison against a constrained Lagrangian MARL baseline - demonstrates zero policy violations, risk exposure below threshold (mean ratio 0.71), 92-97% utility retention, and mean convergence in 2.4 iterations.