# Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry

arXiv: https://arxiv.org/abs/2604.00319 · HTML: https://mlsanity.com/paper/2604.00319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 10 of 10)

- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [More Agents Is All You Need](https://mlsanity.com/paper/2402.05120/) (2024)
- [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://mlsanity.com/paper/2405.04324/) (2024)
- [PINT: Probabilistic In-band Network Telemetry](https://mlsanity.com/paper/2007.03731/) (2020)
- [007: Democratically Finding The Cause of Packet Drops](https://mlsanity.com/paper/1802.07222/) (2018)
- [Communication-efficient Distributed Multi-resource Allocation](https://mlsanity.com/paper/1807.10860/) (2018)
- [Distributed Algorithms for Internet-of-Things-enabled Prosumer Markets: A Control Theoretic Perspective](https://mlsanity.com/paper/1812.07636/) (2018)
- [Compact Data Structures for Network Telemetry](https://mlsanity.com/paper/2311.02636/) (2023)

## Abstract (arXiv)

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 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.