



anima                                                        Y. Yue, Ed.
Internet-Draft                                             X. Zhang, Ed.
Intended status: Standards Track                            China Unicom
Expires: 5 February 2026                                   4 August 2025


  Task-Oriented Multi-Agent Recovery Framework for High-Reliability in
                       Converged Mobile Networks
               draft-yue-anima-agent-recovery-networks-00

Abstract

   This document defines a task-oriented, agent-based method for fault
   recovery in converged public-private mobile networks.  The proposed
   method introduces a multi-agent collaboration framework that enables
   autonomous failure detection, scoped diagnosis, inter-domain
   coordination, and intent-driven policy reconfiguration.  It is
   particularly applicable in complex 5G/6G network deployments, such as
   Multi-Operator Core Networks (MOCN) and Standalone Non-Public
   Networks (SNPN), where traditional centralized management is
   insufficient for ensuring high service reliability and dynamic
   recovery.  The document also specifies protocol requirements for
   inter-agent communication, state consistency, and secure
   coordination, aiming to support interoperability and resilience
   across heterogeneous network domains.

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Copyright Notice

   Copyright (c) 2025 IETF Trust and the persons identified as the
   document authors.  All rights reserved.




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   This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions and Terminology . . . . . . . . . . . . . . . . .   3
   3.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   4
     3.1.  Dynamic Fault Recovery in Shared 5G MOCN
           Infrastructure  . . . . . . . . . . . . . . . . . . . . .   4
     3.2.  Autonomous Recovery in Enterprise SNPN  . . . . . . . . .   4
     3.3.  Cross-Domain Policy Conflict Resolution . . . . . . . . .   4
     3.4.  SLA-Aware Remediation in AI-Driven RAN  . . . . . . . . .   5
   4.  Problem Statement . . . . . . . . . . . . . . . . . . . . . .   5
   5.  Protocol Requirements . . . . . . . . . . . . . . . . . . . .   6
     5.1.  Agent Communications Interface  . . . . . . . . . . . . .   6
     5.2.  Message Semantics and Encoding  . . . . . . . . . . . . .   6
     5.3.  Reliability, Ordering, and Timeout Handling . . . . . . .   7
     5.4.  Security and Trust Requirements . . . . . . . . . . . . .   7
     5.5.  Behavior and State Consistency  . . . . . . . . . . . . .   7
     5.6.  Interoperability Considerations . . . . . . . . . . . . .   7
   6.  Task-Oriented Agent-Based Recovery Method for High-Reliability
           Assurance . . . . . . . . . . . . . . . . . . . . . . . .   8
     6.1.  Objectives  . . . . . . . . . . . . . . . . . . . . . . .   8
     6.2.  Agent Roles and Responsibilities  . . . . . . . . . . . .   8
     6.3.  Recovery Workflow . . . . . . . . . . . . . . . . . . . .   9
       6.3.1.  Scoped Fault Correlation  . . . . . . . . . . . . . .   9
       6.3.2.  Intent-Driven Recovery Evaluation . . . . . . . . . .   9
       6.3.3.  Inter-Domain Coordination . . . . . . . . . . . . . .   9
       6.3.4.  Execution and Safety Enforcement  . . . . . . . . . .  10
       6.3.5.  Feedback Loop and Adaptive Monitoring . . . . . . . .  10
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  10
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  10
   9.  Normative References  . . . . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10










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1.  Introduction

   As mobile networks evolve toward 5G and 6G architectures, new
   deployment paradigms such as Multi-Operator Core Networks (MOCN),
   Shared RAN, and Standalone Non-Public Networks (SNPN) have emerged to
   support both public and enterprise services.  These converged
   deployments introduce unprecedented complexity in terms of topology,
   administrative boundaries, resource sharing, and dynamic service
   intent management.

   Ensuring high reliability in such networks is increasingly difficult
   using traditional centralized network management systems, which often
   suffer from limited scalability, slow responsiveness, and single
   points of failure.  These limitations are particularly critical in
   enterprise and industrial environments, where service-level
   agreements (SLAs) mandate deterministic latency, availability, and
   adaptability.

   This document introduces a task-oriented, agent-based recovery method
   that enables distributed fault detection, context-aware correlation,
   inter-agent negotiation, and closed-loop policy execution.  Agents
   operate at various roles — including telemetry monitoring, domain
   coordination, policy interpretation, and action enforcement — and
   communicate through a structured Agent Communication Interface (ACI).
   The method is designed to autonomously localize faults, assess
   recovery strategies based on service intents, and coordinate recovery
   actions across administrative domains, with minimal human
   intervention.

   In addition to describing the recovery workflow and agent roles, this
   document outlines the associated protocol requirements to ensure
   secure, consistent, and interoperable interactions among agents.
   These requirements cover communication semantics, message formats,
   transport assumptions, and behavioral guarantees.  The goal is to
   enable standards-compliant, intent-aware, and autonomous fault
   management in future mobile network infrastructures.

2.  Conventions and Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 RFC2119 [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

   Abbreviations and definitions used in this document: *ACI: Agent
   Communication Interface. *DCA: Domain Coordination Agent. *EA:
   Execution Agent. *FDA: Fault Detection Agent. *FSM: Finite State



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   Machine. *LLM: Large Language Model. *MOCN: Multi-Operator Core
   Network. *MTTR: Mean Time to Recovery. *PIA: Policy Interpretation
   Agent. *SLA: Service-Level Agreement. *SNPN: Standalone Non-Public
   Network. *URI: Uniform Resource Identifier.

3.  Use Cases

   The method defined in this document applies to several real-world use
   cases in future mobile network environments:

3.1.  Dynamic Fault Recovery in Shared 5G MOCN Infrastructure

   In Multi-Operator Core Network (MOCN) deployments, multiple mobile
   network operators (MNOs) share the same RAN and transport
   infrastructure.  A node failure or link degradation in the shared
   segment can affect multiple tenant slices simultaneously.  With
   agent-based coordination, local agents at affected nodes can detect
   the fault, and domain-level agents from each operator can negotiate
   temporary recovery strategies (e.g., re-routing or resource
   reallocation) without requiring centralized orchestration or full-
   stack configuration reloading.

3.2.  Autonomous Recovery in Enterprise SNPN

   Standalone Non-Public Networks (SNPN) are often deployed by
   enterprises to support on-site applications such as industrial
   automation, AGV coordination, or safety monitoring.  In these
   environments, recovery must be both low-latency and intent-aware.
   For example, if a compute node hosting a real-time controller fails,
   the agent system can trigger service migration to a backup node based
   on the intent to maintain <10ms latency for URLLC traffic, without
   requiring manual administrator intervention.

3.3.  Cross-Domain Policy Conflict Resolution

   In hybrid deployments where a public network operator provides
   managed service slices to enterprises, misaligned policies across
   administrative domains may cause service disruptions (e.g., route
   loops, priority mismatches).  With inter-domain agent negotiation,
   agents can exchange scoped views of current state and intent,
   evaluate compatibility, and agree on a temporary policy contract to
   preserve service continuity until a global policy reconciliation
   occurs.








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3.4.  SLA-Aware Remediation in AI-Driven RAN

   With the rise of AI-native RAN optimization, agents embedded within
   distributed units (DU/CU) or edge compute nodes may detect
   performance anomalies (e.g., increased jitter, burst loss).  Rather
   than waiting for offline model retraining, the system can dynamically
   adapt configuration (e.g., buffer allocation, scheduler adjustment)
   using the agent-based recovery workflow to preserve SLA requirements
   in real time.

4.  Problem Statement

   In converged public-private mobile networks, ensuring service
   continuity and network reliability in the event of failures is a
   fundamental requirement, particularly for enterprise and critical
   infrastructure scenarios.  Traditional centralized network management
   systems often suffer from single points of failure and delayed
   recovery, which are unacceptable in contexts where deterministic
   availability and ultra-low downtime are essential.  Multi-agent
   systems enable fault-tolerant operation through distributed
   intelligence and redundancy.  When a failure occurs—such as link
   disconnection, node crash, or policy conflict—a well-coordinated
   group of agents can dynamically detect, localize, and mitigate the
   issue through real-time communication and cooperative decision-
   making.  This distributed resilience mechanism reduces mean time to
   recovery (MTTR) and minimizes the impact radius of failures.
   Moreover, in cross-domain environments (e.g., MOCN with multiple
   operators or SNPN with enterprise-hosted infrastructure), fault
   management becomes more complex due to administrative isolation and
   heterogeneous control planes.  Intelligent agents deployed at domain
   boundaries can negotiate fallback strategies, synchronize state
   across domains, and maintain policy consistency during partial
   outages.  For example, upon detecting performance degradation in a
   tenant slice, the agents can proactively rebalance traffic, reassign
   resources, or trigger intent re-interpretation without waiting for
   centralized orchestration.  Without agent-based failure
   collaboration, the system risks becoming fragmented, with isolated
   components unable to respond effectively to cascading failures.
   Therefore, enabling resilient, autonomous coordination among agents
   in failure scenarios is essential to support high-availability SLAs,
   enhance robustness against dynamic network threats, and reduce
   operational overhead in complex network environments.









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5.  Protocol Requirements

   To support the efficient and intelligent transmission of sensing data
   in 6G environments, enhancements to the MoQ protocol are proposed.
   These enhancements aim to enrich MoQ metadata or header extensions to
   include key information required for intelligent routing, data
   classification, service mapping, and QoS-aware scheduling in sensing-
   centric applications.

5.1.  Agent Communications Interface

   This section specifies the protocol-level requirements to support the
   agent-based recovery method defined in Section 5.  These requirements
   cover message formats, communication interfaces, timing constraints,
   behavioral consistency, and inter-domain negotiation semantics.  The
   goal is to ensure interoperability, reliability, and intent-aware
   execution of fault recovery workflows across diverse network domains
   and agent implementations.  REQ-1: The system SHOULD define a
   structured Agent Communication Interface (ACI) to support
   asynchronous and event-driven communication among agents.  REQ-2: ACI
   SHOULD support the following core message types: FAULT_EVENT: Sent
   from FDA to DCA; conveys detected fault condition.
   SCOPE_CORRELATION_QUERY/REPLY: Between DCAs; used for inter-domain
   fault localization.  INTENT_REQUEST/RESPONSE: Between DCA and PIA;
   conveys service-level intent and policy goals.  RECOVERY_PROPOSAL:
   Sent from initiating DCA to peer DCA(s); contains proposed joint
   recovery actions.  RECOVERY_CONTRACT: Formalizes agreement among
   domains on resource reallocation and rollback.

   EXECUTION_COMMAND: Sent from DCA to EA to enact recovery actions.
   EXECUTION_STATUS: Sent from EA to DCA to report outcome and
   validation results.  REQ-3: All ACI messages SHOULD include: Agent
   identity and role Timestamp Message type and version Unique
   transaction/session ID Integrity protection (e.g., signature or HMAC)
   REQ-4: The ACI protocol SHOULD support both push and pull modes for
   event dissemination and agent querying.

5.2.  Message Semantics and Encoding

   REQ-5: Protocol messages SHOULD be encoded using a format that is
   both human-readable and machine-processable.  JSON and CBOR are
   RECOMMENDED; protocol buffers MAY be used in constrained
   environments.  REQ-6: Each message type SHOULD conform to a pre-
   defined schema, including required and optional fields.  REQ-7:
   Message payloads involving intent retrieval or policy proposals
   SHOULD include a service identifier that maps to a known SLA or
   intent profile.




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5.3.  Reliability, Ordering, and Timeout Handling

   REQ-8: Protocol exchanges involving recovery workflows MUST support
   acknowledgment and retry mechanisms.  REQ-9: Agents participating in
   a recovery transaction MUST support: Timers for detecting negotiation
   or execution timeout Fallback strategies upon failure to reach
   consensus or apply action REQ-10: ACI message transport MUST
   guarantee in-order delivery of messages within a session context,
   particularly for multi-step negotiation sequences.

5.4.  Security and Trust Requirements

   REQ-11: All ACI communications MUST be secured using mutually
   authenticated channels.  REQ-12: Agents MUST maintain a local trust
   registry of peer agents and their associated roles, identities, and
   access policies.  REQ-13: Inter-domain messages MUST be
   cryptographically signed and include domain-level identifiers to
   prevent spoofing or replay.  REQ-14: Sensitive data in intent
   evaluation MUST be protected during transit and only exposed to
   authorized agents.

5.5.  Behavior and State Consistency

   REQ-15: Agents MUST implement finite state machines (FSMs) to ensure
   correct handling of message sequences and recovery states.  REQ-16:
   In case of multi-agent execution, agents MUST agree on task status
   codes to track workflow progress consistently.  REQ-17: Feedback and
   learning data SHOULD be stored in a common, queryable knowledge base
   accessible to policy training agents.

5.6.  Interoperability Considerations

   REQ-18: Implementations MUST support version negotiation for ACI
   messages to ensure forward compatibility.  REQ-19: Domain-specific
   extensions (e.g., for 5G MOCN, SNPN) MUST be encapsulated using an
   optional extension field, and MUST NOT interfere with baseline schema
   validation.  REQ-20: Recovery workflows MUST be idempotent where
   possible, allowing repeated execution without unintended side effects
   in failure or retry scenarios.












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6.  Task-Oriented Agent-Based Recovery Method for High-Reliability
    Assurance

   This part defines a distributed, agent-based recovery method that
   supports high-reliability service assurance in converged public-
   private mobile networks.  The method enables autonomous failure
   detection, scoped diagnosis, and intent-driven policy adaptation
   through coordination among multiple intelligent agents.  It is
   designed to address both intra-domain and inter-domain failure
   scenarios while maintaining SLA compliance.

6.1.  Objectives

   The method is designed to fulfill the following objectives: (1)
   Resilience through distribution: Eliminate single points of failure
   by decentralizing failure detection and recovery logic across agents.
   (2) Scoped collaboration: Allow agents to reason over localized
   context while supporting inter-agent negotiation for broader fault
   scenarios. (3) Intent consistency: Ensure that all recovery decisions
   align with user or service-level intents registered in the system.
   (4) Closed-loop adaptability: Continuously monitor recovery outcomes
   and feed them into learning or policy refinement processes. (5) The
   method is applicable in deployment environments such as 5G MOCN,
   SNPN, or 6G hybrid infrastructures involving multiple tenants and
   administrative domains.

6.2.  Agent Roles and Responsibilities

   The method introduces four distinct roles for intelligent agents,
   each fulfilling a key functional responsibility in the recovery
   workflow: (1) Fault Detection Agent (FDA): Resides at network or
   compute nodes; performs real-time telemetry monitoring.  Upon
   threshold violation, constructs a structured fault event including
   metadata such as event ID, node ID, timestamp, metric type, and
   severity. (2) Domain Coordination Agent (DCA): Aggregates events from
   multiple FDAs to determine failure scope and severity.  Responsible
   for intra-domain coordination and inter-domain negotiation when
   needed. (3) Policy Interpretation Agent (PIA): Retrieves and parses
   registered service intents.  Evaluates recovery options and generates
   adaptive policy updates based on current state and available
   resources. (4) Execution Agent (EA): Applies the reconfiguration
   actions (e.g., rerouting, resource migration, parameter adjustment)
   and performs post-configuration checks to ensure compliance and
   stability.  All agents communicate over an Agent Communication
   Interface (ACI), which provides structured messaging primitives for
   event reporting, status querying, negotiation, and command dispatch.





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6.3.  Recovery Workflow

   The recovery method consists of the following task-oriented workflow:
   ### Fault Detection and Event Generation FDA continuously monitors
   key performance metrics (e.g., latency, packet loss, CPU
   utilization).  On violation, FDA emits a structured fault event:
   +-------------------+-----------------------------+ | Field | Value |
   +-------------------+-----------------------------+ | event_id |
   e12345 | | node_id | node-A | | timestamp | 2025-07-21T08:00:00Z | |
   metric | link_loss | | value | 15.2 | | threshold | 10.0 | |
   severity | major |
   +-------------------+-----------------------------+ This event is
   transmitted to the local DCA via ACI.

6.3.1.  Scoped Fault Correlation

   DCA aggregates fault reports from FDAs and analyzes temporal-spatial
   correlations.  If patterns emerge indicating a localized or
   distributed failure domain, DCA maps the affected logical services
   (e.g., slices, functions, access nodes).  If the impact likely
   crosses domain boundaries (e.g., MOCN core or shared RAN), the DCA
   initiates inter-domain state queries.

6.3.2.  Intent-Driven Recovery Evaluation

   DCA invokes PIA with a fault-context descriptor.  PIA queries the
   intent registry and retrieves the affected service's constraints and
   goals, such as:
   +---------------------+----------------------------+ | Field |
   Value | +---------------------+----------------------------+ |
   intent_id | tenant-001-intent | | sla.latency | < 20ms | |
   sla.availability | 99.99% | | fallback_policy | [reroute,
   degrade_qos] | | priority | critical |
   +---------------------+----------------------------+ PIA evaluates
   multiple recovery strategies (e.g., traffic shift, resource
   migration, service downgrade) and scores them against SLA compliance
   and resource availability.

6.3.3.  Inter-Domain Coordination

   When faults span across domains, the DCA of the initiating domain
   sends a Recovery Proposal Message to peer DCAs.  Each DCA evaluates
   local resource availability and responds with either: Acceptance of
   shared recovery effort (with constraints), or Negotiation of a
   fallback agreement (with time limits and rollback conditions).  Upon
   consensus, a Recovery Execution Contract is established, which
   includes scope, roles, time windows, and validation checkpoints.




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6.3.4.  Execution and Safety Enforcement

   DCA dispatches a recovery command to EA, which applies configurations
   (e.g., policy updates, slice rerouting, traffic prioritization).  EA
   performs pre- and post-checks to verify: Policy consistency
   Compliance with intent System stability post-update

6.3.5.  Feedback Loop and Adaptive Monitoring

   After execution, FDA switches to enhanced monitoring mode in affected
   areas (e.g., higher-frequency sampling, link probing).  DCA collects
   performance data and sends summary logs to a shared knowledge base
   for: Post-mortem analysis Learning model refinement (e.g.,
   reinforcement learning agent tuning) If instability persists, PIA may
   auto-trigger policy reevaluation or escalate to supervisory agent
   layer.

7.  Security Considerations

   TBD

8.  IANA Considerations

   TBD

9.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

Authors' Addresses

   Yi Yue (editor)
   China Unicom
   Beijing
   China
   Email: yuey80@chinaunicom.cn








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   Xuebei Zhang (editor)
   China Unicom
   Beijing
   China
   Email: zhangxb170@chinaunicom.cn














































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