Abstract
Part 03 of 15 addresses AI safety in multi-agent network incident diagnosis, evidence-qualified root-cause assessment, coordinated remediation, asynchronous repair, and operational recovery verification. It distinguishes independent network observations from repeated derivative reports, competing diagnostic hypotheses from established faults, plan acceptance from actual configuration effects, and successful interventions from verified network-service recovery. Group B workflow B-CW-01 and six detailed embodiments address incident identity, source-qualified provenance, cascading cross-domain faults, concurrent interventions, partial repair, compensation, recurrence, and incident handoff. Consistency assessments are distinct from authority to change network resources. The foundational Part 01 provides shared evidence and state semantics; Parts 02, 04, 07, 11, 14, and 15 describe complementary radio, service, asynchronous, continuity, and protected-operation mechanisms. The appendix provides direct navigation across all fifteen parts. The technical problem is that conventional event aggregation and change orchestration can report a coherent incident diagnosis or completed repair without proving the independence, timing or scope of supporting observations or actual restoration of the intended service. This part describes evidence-qualified three-valued conclusions, separate operation admission, post-repair effect verification, implementation profiles, explicit failure paths and source-preserving recovery history. A runnable Python laboratory supplies synthetic reproducible tests and local processing measurements; it is not an operational network performance demonstration. It complements Part 01 shared evidence semantics and Part 02 radio observation qualification where incident decisions depend on radio telemetry. In plain terms: when AI agents diagnose and repair a network incident, conventional automation can report a confident root cause or a "completed" repair without proving that its supporting alarms were independent, correctly timed and about the same resource generation, or that the intended service actually recovered. This disclosure describes how to bind evidence to an incident, keep competing hypotheses honest, bind repair plans to current state, distinguish submitted, accepted, applied and observed effects, and verify recovery independently, returning SUPPORTED, CONTRADICTED or UNRESOLVED with evidence links, while never treating a diagnosis as permission to change the network.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Das, Sangam Kumar, "Verifying Multi-Agent AI Self-Healing in 5G-Advanced and 6G Networks Before Declaring Recovery", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12709