Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry
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arXiv:2604.00319v1 Announce Type: new Abstract: We develop algorithms for collaborative control of AI agents and critics in a multi-actor, multi-critic federated multi-agent system. Each AI agent and critic has access to classical machine learning or generative AI foundation models. The AI agents and critics collaborate with a central server to complete multimodal tasks such as fault detection, severity, and cause analysis in a network telemetry system, text-to-image generation, video generation
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Computer Science > Artificial Intelligence
[Submitted on 31 Mar 2026]
Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry
Syed Eqbal Alam, Zhan Shu
We develop algorithms for collaborative control of AI agents and critics in a multi-actor, multi-critic federated multi-agent system. Each AI agent and critic has access to classical machine learning or generative AI foundation models. The AI agents and critics collaborate with a central server to complete multimodal tasks such as fault detection, severity, and cause analysis in a network telemetry system, text-to-image generation, video generation, healthcare diagnostics from medical images and patient records, etcetera. The AI agents complete their tasks and send them to AI critics for evaluation. The critics then send feedback to agents to improve their responses. Collaboratively, they minimize the overall cost to the system with no inter-agent or inter-critic communication. AI agents and critics keep their cost functions or derivatives of cost functions private. Using multi-time scale stochastic approximation techniques, we provide convergence guarantees on the time-average active states of AI agents and critics. The communication overhead is a little on the system, of the order of \mathcal{O}(m), for m modalities and is independent of the number of AI agents and critics. Finally, we present an example of fault detection, severity, and cause analysis in network telemetry and thorough evaluation to check the algorithm's efficacy.
Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2604.00319 [cs.AI]
(or arXiv:2604.00319v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2604.00319
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From: Syed Eqbal Alam [view email]
[v1] Tue, 31 Mar 2026 23:33:56 UTC (949 KB)
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