Enabling Verifiable Trust and Policy-Driven Coordination for AI Agent Swarms in Defence

A Use Case Utilising the Agent Network System (ANS)

Abstract:

The operational efficacy of coordinated AI agent swarms in defence is paramount, whether these agents are embodied in autonomous drones and ground vehicles or exist as sophisticated software entities. This efficacy critically hinges on the secure discovery, verifiable identity and capabilities of individual agents, as well as policy compliant coordinated action.

This article provides a technical examination of the Agent Network System (ANS) v0.1.0 specification, an open standard designed as a foundational infrastructure to address these critical requirements for multi-agent AI systems. We explore its hybrid architecture, core operational mechanisms enabling secure agent swarm formation and tasking, multi-level trust model for individual agent verification, AI Bill of Materials (AIBOM) integration for software agent integrity, and its specific applicability to complex, policy-driven AI agent swarm operations in contested environments.

1. Introduction: The Imperative for a Foundational Trust Layer in Multi-Agent Defence Systems

Modern defence operations increasingly rely on complex ecosystems of interacting AI agents. These agents can range from autonomous physical platforms like Unmanned Aerial Systems (UAS) and Unmanned Ground Vehicles (UGV), to software-based entities such as AI-driven intelligence analysis tools, mission planning systems, and Ground Control Station (GCS) interfaces. When these agents operate collectively, forming "swarms" or collaborative groups, the challenges of establishing trust and ensuring secure, policy compliant coordination become paramount.

Key questions arise:

• How does an AI agent (e.g., a GCS agent) securely discover and verify the identity of another agent (e.g., an agent embodied in a specific drone) before issuing commands?
• How can we ensure that an AI agent, whether software or hardware instantiated, possesses the attested capabilities and that its underlying software components (its "AI Bill of Materials" - AIBOM) are authentic and uncompromised?
• How are complex multi-agent behaviours and task allocations governed by verifiable policies rather than solely relying on the internal logic of individual agents or pre-scripted interactions?

The Agent Network System (ANS) specification (currently v0.1.0 – Request for Comment) aims to provide this foundational "trust fabric" for such AI agent ecosystems. It is not a replacement for agent-to-agent (A2A) communication protocols but a complementary infrastructure layer that answers the "who is this agent?" and "are they trustworthy and authorised for this interaction?" questions before protocol specific communication commences. In this context, each drone, GCS, UAS or other participating system component is considered an AI agent registered and verifiable within the ANS.

2. The ANS Hybrid Architecture: Supporting Scalable and Trustworthy AI Agent Ecosystems (ANS Spec, Sec 3)

ANS proposes a hybrid, three-layer architecture designed to optimise for both high-performance discovery of AI agents and strong, verifiable trust between them:

• 2.1 Centralised High-Performance Layer:
• Agent Swarm : An orchestrator agent for a drone swarm needing to quickly lookup all registered AI agents (embodied as drones) within a designated operational area that report "active_sensor_suite_type_Y" and have a "high_integrity_AIBOM_status."
• 2.2 Hybrid Bridge Layer:
• Agent Swarm : When the AIBOM of an AI agent (e.g., the flight control software agent on a drone) is updated, the Synchronisation Engine ensures this change is consistently reflected across the ANS. Policies governing this agent's participation in swarm maneuvers that depend on that specific software version are then re-evaluated.
• 2.3 Decentralised Trust Layer:
• Agent Swarm : The initial registration and capability attestation of each AI agent (e.g., a new type of sensor processing agent or a drone agent) intended for critical multi-agent operations is anchored to a permissioned blockchain, providing an immutable record of its authorised configuration and provenance.

3. Core ANS Operations: Enabling Secure Dynamics for AI Agent Swarms (ANS Spec, Sec 4)

Within ANS, every participating entity (drone, GCS, UAS) is an agent.

• Register: Each AI agent is registered with its unique agent_id (e.g., uas_isr_alpha001.unit_xyz.ans, gcs_operator_beta.unit_xyz.ans, threat_analysis_engine_gamma.hq.ans), its public_key, its specific capabilities drawn from a Standardised Capability Taxonomy - Sec 7.3, and its AI Bill of Materials (AIBOM - Sec 5.6, Appendix E).
• Lookup: An AI agent (e.g., a mission planning agent) lookups other AI agents (e.g., available ISR ,intelligence, surveillance and reconnaissance, drone agents or data fusion agents) based on required capabilities and trust levels.
• Verify:
• Before an AI agent (e.g., a GCS agent) issues a critical command to another agent (e.g., an effector drone agent), it verifies the target agent's identity, AIBOM integrity (especially for critical software components like targeting or flight control agents), and policy compliance.
• AI agents within a swarm can verify each other before engaging in collaborative tasks or sharing sensitive data, ensuring they are interacting with legitimate and uncompromised peers.
• Policy Management: This is central to governing the interactions and collective behaviour of AI agent swarms.

• AI Agent Swarm Policy :

  {
    "policy_id": "Collaborative_Target_Engagement_Policy_003",
    "description": "Policy for multi-agent engagement of designated targets.",
    "triggering_agent_capability": "command_and_control_interface",
    "participating_agents_criteria": [
      {
        "role": "isr_sensor_agent", // Could be a drone agent or a ground sensor agent
        "min_count": 2,
        "required_capabilities": ["target_identification_confidence_high"],
        "verification_level": "standard",
        "aibom_components_verified": ["sensor_processing_firmware_v2.2+"]
      },
      {
        "role": "effector_agent", // Could be a drone agent with payload, or a ground-based effector agent
        "min_count": 1,
        "required_capabilities": ["precision_kinetic_effect_type_Z"],
        "verification_level": "blockchain",
        "aibom_components_verified": ["safe_arming_logic_v4.0", "guidance_software_v3.1_secure"]
      }
    ],
    "coordination_protocol_attestation_required": "secure_swarm_comms_v1",
    "action": "authorise_coordinated_engagement_tasking"
  }
  

  This policy dictates that a C2 agent can only authorise a coordinated target engagement if at least two ISR agents (with verified sensor software) and at least one effector agent (with blockchain verified identity and critical AIBOM components for safety and guidance) are available, verified, and attested to be using a specific secure communication protocol.

4. Advanced Trust Mechanisms for Secure and Capable AI Agent Collectives

• AI Supply Chain Verification: Ensuring each AI agent, whether a complex software suite running in a command centre or the embedded control system of an autonomous vehicle, is composed of validated, uncompromised software components.
• Contextual Attestations: An orchestrator agent, after verifying a group of diverse AI agents (e.g., drone agents, UGV agents, data analysis agents), could issue them a contextual attestation authorising them to form a temporary, task-specific multi-domain team.
• Zero-Knowledge Proof System: (Future) An AI agent from a coalition partner could prove its data processing algorithms adhere to agreed upon privacy or security standards (relevant for its AIBOM) without revealing the proprietary details of those algorithms.
• Rapid Revocation Mechanism: If any AI agent within a collaborative network is compromised, its ANS registration can be rapidly revoked, signaling to all other agents that it is no longer trusted for interaction.

5. A Practical Implementation Pathway: Policy-Driven Multi-Domain AI Agent Operations

• An ISR drone agent detects a potential threat. It verifies a ground-based sensor agent to correlate data. Both then verify and send data to an AI analysis agent at a command post. Based on the analysis agent's output (itself a verifiable agent with a known AIBOM for its algorithms), a mission commander agent (interfacing with a human) uses ANS to lookup and verify an appropriate effector agent (which could be another drone agent or a different type of weapon system agent) according to pre-defined engagement policies.

6. Conclusion and Call for Engagement: Building the Future of Trusted AI Agent Ecosystems in Defence with ANS

The Agent Network System specification offers a technically robust and architecturally sound approach to building the foundational trust layer required for future defence systems. Its emphasis on open standards, hybrid architecture, multi-level verification, AIBOM integration, and policy-driven interactions directly addresses the complex security and interoperability challenges faced by modern militaries. Defence organisations, industry partners, and research institutions are encouraged to:

• Review the ANS v0.1.0 specification and provide critical feedback.
• Identify and support pilot programmes to validate ANS concepts in realistic defence scenarios, such as the drone swarm management example.
• Contribute to the development of reference implementations and conformance test suites.
• Collaborate on refining the specification, particularly in areas like the Zero-Knowledge Proof system, validator governance for permissioned defence blockchains, and integration with existing military identity and C2 systems.

By fostering a collaborative approach around an open standard like ANS, the defence community can accelerate the development and deployment of secure, trustworthy, and interoperable AI-driven autonomous capabilities.