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

🕒 Published (v1): 2026-04-19 04:02 UTC · Source: Arxiv · link

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Policy-compliant multi-agent orchestration with SOX/HIPAA/GDPR enterprise constraints

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TL;DR

CAMCO (Constraint-Aware Multi-Agent Cognitive Orchestration) is a deployment-time middleware layer that coordinates enterprise AI agents as a constrained optimization problem, enforcing hard policy compliance and bounded risk without retraining. It combines convex constraint projection, adaptive Lagrangian risk penalization, and iterative negotiation to achieve zero policy violations while retaining 92–97% of unconstrained utility.

Problem

Existing multi-agent coordination methods (cooperative MARL, centralized planners, consensus protocols) optimize expected reward while treating constraints implicitly or only at training time. Prompt-based guardrails for LLM agents lack formal guarantees. Enterprise deployments under SOX, HIPAA, and GDPR require runtime enforcement of hard policy feasibility (approval chains, segregation of duties), dynamically bounded aggregate risk, and full auditability—none of which existing frameworks jointly provide.

Method

CAMCO models the joint coordination problem as:

\[\max_{a} \sum_{i=1}^n U_i(s, a_i) \quad \text{s.t.} \quad \Phi(a)=1, \quad \sum_i R_i(a_i) \leq \tau, \quad a_i \in F_i(s)\ \forall i\]

Three components operate in a perceive–reason–coordinate–execute–reflect loop:

  1. Constraint Projection Engine (CPE): Projects each proposed action \(a_i\) to the nearest policy-feasible point \(a'_i = \Pi_C(a_i)\) over \(C_i(s) = F_i(s) \cap \{a : \varphi_k(\cdot)=1,\forall k\}\). Uses Euclidean projection for continuous spaces and minimum-edit-distance constraint satisfaction for discrete action sets. Satisfies idempotency and guaranteed feasibility.

  2. Risk-Weighted Utility Engine (RWUE): Computes risk-adjusted utility \(\tilde{U}_i = U_i(s,a'_i) - \lambda \cdot R_i(a'_i)\), where the Lagrange multiplier \(\lambda\) is updated via dual ascent with diminishing step size \(\eta_t = \eta_0/\sqrt{t}\): \(\lambda^{(k+1)} = \lambda^{(k)} + \eta \cdot \max(0, R_\text{tot}^{(k)} - \tau)\).

  3. Negotiation Loop: Iterative propose–project–evaluate cycle. Agents propose in parallel, CPE projects, aggregate risk \(R_\text{tot}\) is evaluated; if \(\Phi(a')=1\) and \(R_\text{tot} \leq \tau\), the joint action is accepted. Otherwise \(\lambda\) is tightened and agents re-propose. After \(K_\text{max}\) failures, all agents revert to their last compliant configuration (safe fallback), guaranteeing \(\Phi(\mathcal{F}(a))=1\) and \(R(\mathcal{F}(a),s) \leq \tau\) for all \(s\).

Policy predicates are expressed as OPA/Cedar-compatible rules, making CAMCO architecture-agnostic middleware.

Key Contributions

  • Formal enterprise multi-agent constrained decision model capturing policy feasibility \(\Phi\), execution feasibility \(F_i(s)\) (RBAC, availability, temporal windows), and bounded aggregate risk \(\sum_i R_i(a_i) \leq \tau\).
  • CAMCO algorithm with provably bounded convergence (Theorem 1) and monotonic risk reduction (Proposition 1) under mild continuity and boundedness assumptions.
  • Safe fallback operator \(\mathcal{F}\) with constructive proof that the orchestrator never deadlocks in a non-compliant configuration.
  • Deployment-time middleware design compatible with any agent architecture (LLM, RL, rule-based), with policy predicates directly integrable with production engines (OPA, Cedar).

Results

Evaluated over 500 episodes per scenario across three settings (S1: 4-agent financial approval, S2: 3-agent payroll adjustment, S3: 5-agent cloud deployment) against four baselines:

  • Violation rate: CAMCO 0% in all scenarios vs. B1 (MARL) 23.4/18.6/31.2%, B2 (greedy) 8.2/5.4/12.8%, B4 (Lagrangian MARL) 4.6/3.8/9.4%, B3 (rules) 0%.
  • Risk exposure (\(\bar{R}/\tau\)): CAMCO 0.68/0.72/0.74 (all below threshold) vs. B1 1.42/1.28/1.67, B4 0.91/0.88/1.08.
  • Utility retention: CAMCO 96.8/94.2/92.1% vs. B3 71.3/74.6/65.8% (rules-only) and B4 93.4/91.7/89.3%.
  • Deadlock rate: CAMCO 1.2/0.8/2.0% vs. B3 12.8/8.4/18.6%.
  • Mean convergence: 2.1–2.8 iterations; \(K_\text{max}\) reached in ~3% of proposals.
  • Sensitivity analysis shows 100% policy compliance across \(\tau \in [0.4, 1.4]\), with utility degrading from 99% to 78% at extreme tightening.

Limitations

  • Risk-model calibration is external to CAMCO; the linear-additive decomposition \(R_i(a_i) = \sum_{d \in D} w_d \cdot r_{i,d}(a_i)\) may underestimate compounding risk in highly correlated workflows.
  • CPE efficiency relies on convexity (continuous spaces) or moderate action-set size (\(|A_i| \leq 10^3\)) for discrete spaces; larger action sets require external constraint-programming solvers.
  • Evaluation is simulation-only; no empirical validation against production OPA/Cedar deployments is reported.
  • Convergence guarantee (Theorem 1) requires each agent to have at least one zero-risk safe default—this precondition is assumed, not verified for general enterprise settings.
  • Monotonic risk reduction (Proposition 1) requires agents to respond monotonically to \(\lambda\), which holds for rational agents but is not guaranteed for fixed-policy or black-box LLM agents.

Relevance to Harnesses / Meta-Harnesses

CAMCO is directly an orchestration harness: it wraps heterogeneous agents (LLM, RL, rule-based) behind a unified coordination layer without requiring changes to the agents themselves, which is the defining trait of a meta-harness. The perceive–reason–coordinate–execute–reflect cycle it implements is a formalization of the control loop that meta-harnesses impose over sub-agents. For harness designers, the CPE + Lagrangian negotiation loop provides a concrete, formally grounded pattern for injecting policy constraints and risk bounds into any multi-agent pipeline at runtime rather than through agent-level fine-tuning—a key architectural option when sub-agents are third-party or black-box. The safe fallback mechanism (guaranteed reachability of a compliant state) is also directly applicable to harnesses that must satisfy audit or compliance invariants.