Multi-Agent Adaptive Guard (MAAG)

• MAAG is an adaptive, distributed multi-agent system that employs self-organizing agents to protect critical assets in dynamic, adversarial environments.
• It utilizes minimal communication and localized reconfiguration to provide provable safety and resilience against failures and adversarial attacks.
• Its applications span robotic patrolling, network security, and collaborative perception, achieving state-of-the-art performance metrics in security and task utility.

A Multi-Agent Adaptive Guard (MAAG) is an architectural and algorithmic meta-pattern for adaptively protecting, securing, or supervising critical assets via coordinated multi-agent systems. MAAG systems are characterized by distributed, self-organizing ensembles of autonomous agents (robots, software agents, or hybrid LLM-based controllers) that respond to environmental threats, system attrition, adversarial attacks, or dynamic operational objectives with theoretical performance guarantees, minimal communication overhead, and strong adaptability. The MAAG nomenclature now spans physical patrolling and surveillance, LLM security, federated analytics, collaborative perception for robotics and autonomy, and dynamic task execution in open environments (Goeckner et al., 2023, Abbas et al., 2015, Leng et al., 3 Dec 2025, Barua et al., 23 Feb 2025, Xie et al., 13 Aug 2025, Berducci et al., 2023).

1. Core Principles and Defining Features

The defining principles of a MAAG system include:

• Distributed Multi-Agent Control: A MAAG operates as a decentralized (or weakly centralized) ensemble of agents, each capable of local computation, perception, and action, but coordinating at critical events or boundaries.
• Adaptivity to Failure and Attack: MAAG agents adapt at runtime to attrition (agent loss), adversarial interventions (including malicious agents or adversarial prompts), and environmental changes by reconfiguring roles, partitions, or behavioral policies (Goeckner et al., 2023, Leng et al., 3 Dec 2025, Hu et al., 28 Jun 2025).
• Minimal Communication Regime: Effective MAAG algorithms reduce inter-agent communication to essential broadcasts—such as breakdown alerts, consensus verdicts, or security triggers—enabling deployment under bandwidth constraints or adversarial signal environments (Goeckner et al., 2023).
• Provable Safety and Performance Guarantees: Theoretical bounds—on coverage, idleness, mis-classification, or liveness—are provided at both initialization and after reconfiguration events (Abbas et al., 2015, Goeckner et al., 2023, Hu et al., 28 Jun 2025, Berducci et al., 2023).
• Modular and Hierarchical Organization: Many instantiations encapsulate distinct guard roles (e.g., local patroller, policy agent, defense agent, monitor/controller), reflecting the modular, hierarchical, or compositional structure of natural immune systems, cyber-physical controllers, or federated AI (Leng et al., 3 Dec 2025, Barua et al., 23 Feb 2025, Veeraragavan et al., 24 Jun 2025).

2. Mathematical and Algorithmic Foundations

MAAG systems are realized through mathematical models and algorithms that optimize security or utility under resource, safety, or operational constraints.

• Partitioning and Patrolling: Graph decompositions (e.g., Voronoi partitions, power graph cliques) allocate subregions to individual guards, ensuring full coverage with only localized adaptation after adversarial events or agent loss (Goeckner et al., 2023, Abbas et al., 2015). For example, the Voronoi-based allocation in the Adaptive Heuristic‐based Patrolling Algorithm (AHPA) ensures only boundary-neighbor agents reconfigure after attrition, minimizing disturbance (Goeckner et al., 2023).
• Consensus-Based and Probability-Agnostic Verification: In collaborative perception, consensus protocols such as PASAC (Probability-Agnostic Sample Consensus) identify benign collaborators by hierarchical sampling and verification using collaborative consistency losses (CCLoss), bypassing the need for prior probabilities of malicious agents (Hu et al., 28 Jun 2025). Analytical error and complexity bounds are provided:

$$\Pr[\text{error}] \le (\alpha+\beta)m\lceil\log_2 n\rceil$$

$T(n,m) \le 2m\lceil\log_2 n\rceil + (n-m)$

• Immune-Inspired Memory and Guarding: MAAG for LLM jailbreak detection leverages an immune-system-inspired tripartite architecture: a memory bank (for activation-signature recall), a defense agent (response simulation), and an auxiliary/reflective agent (safety vetting). Memory updates are non-parametric, enabling fast adaptation to novel attacks without model fine-tuning (Leng et al., 3 Dec 2025).
• Supervisory Control and Profile-Aware Maneuvering: In dynamic multi-agent tasks, a guard agent employs a “performance fingerprint” for the primary execution agent—a predictive error model derived via offline system identification. The guard issues both feedback and feed-forward interventions, anticipating or correcting likely failure modes based on statistical error profiles (Xie et al., 13 Aug 2025).
• Safety via Adaptive Control Barrier Functions: In physical multi-agent domains, MAAG is implemented through adaptive safe RL with state-dependent control barrier function (CBF) coefficients, enforcing set invariance constraints via quadratic programming, while tuning safety-performance trade-offs with Lagrangian multipliers and PID adaptation (Berducci et al., 2023).

3. Domains and Representative Applications

MAAG methodologies have been instantiated across diverse robotic, AI, and cyber-physical security settings.

Domain	Guard Mechanism	Key Guarantee
Robotic Patrolling & Surveillance	Voronoi/cluster-based partitioning, rerouting	Coverage, bounded idleness
Network Security	Heterogeneous mobile guards	Eternal security, limited topological moves
LLM Jailbreak Detection	Immune memory, dual-agent simulation	SOTA detection F1/accuracy
Collaborative Perception (Robotics)	PASAC consensus, CCLoss verification	Low misclassification, bounded queries
Federated Analytics/Computing	Agentic control plane, rule-based FSMs	Compositional safety invariants
Web/LLM Agents (Safety-Utility tradeoff)	Policy/utility multi-agent optimization	Pareto-optimal task/guard trade
Physical Security (Bodyguarding)	Multi-agent RL (centralized critics)	Emergent surround/protect behaviors

For instance, in collaborative autonomous driving perception (Hu et al., 28 Jun 2025), MAAG achieves object detection [email protected]/0.7 of 80.4/78.3 under white-box adversarial PGD attacks, nearly matching the benign upper bound (81.8/79.6) and dramatically outperforming naïve defenses.

4. Theoretical Guarantees and Performance Analysis

Formal guarantees provided by MAAG systems arise from algorithmic structure and mathematical modeling:

• Resilience to Attrition and Failure: After each agent loss in a patrolling system, the average node idleness inflates by at most a factor $m/(m-1)$, with the system maintaining coverage through localized, minimal communication–driven reconfiguration (Goeckner et al., 2023).
• Coverage and Security: In guarding networks, clusters formed under clique decompositions coupled with guard placement ensure full coverage and “eternal security”: the global network is always recoverable to a secure configuration with only the responsible guard moving per incident (Abbas et al., 2015).
• Jailbreak and Attack Detection: LLM security MAAGs achieve up to 0.98 accuracy and 0.96 F1 against diverse adversarial attack patterns, outperforming static moderation baselines by ≥20% F1, with memory update and dual-agent supervision loop (Leng et al., 3 Dec 2025).
• Perception Integrity Under Attacks: Adaptive threshold online learning maintains low false-positive/negative rates in benign/malicious consensus, with proven reliability guarantees and logarithmic query complexity with respect to agent count (Hu et al., 28 Jun 2025).
• Task Utility and Policy Compliance: Profile-aware guard systems (e.g., on the GAIA benchmark) achieve 70.95% Pass@1 accuracy and the lowest observed standard deviation (0.0115), significantly outstripping naïve or baseline supervisors (Xie et al., 13 Aug 2025). HarmonyGuard achieves up to +38% relative policy-compliant completion in web agent tasks (Chen et al., 6 Aug 2025).

5. Communication, Coordination, and Adaptation Mechanisms

MAAG architectures minimize communication via several mechanisms:

• Single-broadcast adaptation for node loss (patrolling), with only Voronoi neighbors replanning (Goeckner et al., 2023).
• Local consensus and verification with online adaptive thresholds for classifier reliability (perception) (Hu et al., 28 Jun 2025).
• Periodic signed telemetry and command channels decoupled from privacy backends (federated computing), enabling strong separation of duties in safety enforcement (Veeraragavan et al., 24 Jun 2025).
• Modular agent roles (monitoring, intervention, policy reflection) composed as hierarchical safety loops (LLM/agentic defense) (Leng et al., 3 Dec 2025, Barua et al., 23 Feb 2025).

Such communication strategies are critical for deployment in adversarial, low-bandwidth, or human–machine teamed settings.

6. Limitations, Open Problems, and Future Directions

Despite robust empirical and theoretical guarantees, MAAGs face several open challenges:

• Latency and Throughput: Dual-agent or simulation-based detection (e.g., LLM guardrails) introduces 8–12s query overhead, limiting real-time deployment at scale (Leng et al., 3 Dec 2025).
• Memory Management and Adaptation: Attack memory banks may grow or drift over time, calling for advanced clustering/pruning or hierarchical memory models (Leng et al., 3 Dec 2025).
• Threshold Selection and Policy Adaptation: Fixed-decision threshold rules require careful tuning; unsupervised or online reinforcement learning–based adaptation is an active research avenue (Barua et al., 23 Feb 2025, Hu et al., 28 Jun 2025).
• Scalability: For large-scale dense networks or agent populations ($n \gg 50$), sampling, consensus, and adaptation overheads can increase, motivating the design of lightweight or hierarchical guards (Hu et al., 28 Jun 2025).
• Compositionality in Mixed-Mode Systems: Federated or agentic systems with heterogeneous privacy or security backends require formal compositional safety verification, which remains an open challenge for cross-domain MAAG integration (Veeraragavan et al., 24 Jun 2025).

Proposed directions include adversarially learned threat models, continuous online adaptation, profile-aware controller upgrades, metacognitive self-reflection, and scaling MAAG frameworks to human–AI collaborative teams (Barua et al., 23 Feb 2025, Chen et al., 6 Aug 2025, Xie et al., 13 Aug 2025).

---

In summary, Multi-Agent Adaptive Guard systems represent a rigorously grounded, highly modular paradigm for resilient protection and supervision across physical, informational, and computational domains. Their architectures synthesize partitioned patrolling, immune-system–inspired memory, consensus-based verification, adaptive supervisory control, and formal guarantees, enabling robust defense and flexible adaptation in adversarial and dynamic environments (Goeckner et al., 2023, Abbas et al., 2015, Leng et al., 3 Dec 2025, Hu et al., 28 Jun 2025, Xie et al., 13 Aug 2025, Berducci et al., 2023).