- Updated: August 26, 2026
- 2 min read
The Abstention Protocol: RCA for Clos Fabrics – A Deep Dive
The Abstention Protocol: RCA for Clos Fabrics
Abstract: Root cause analysis (RCA) in large datacenter networks is challenging because telemetry is noisy, partial, and asynchronous. Traditional score‑based approaches often become unstable or produce incorrect attributions. In this article we explore CoreSec, a production‑grade RCA system that replaces weighted fusion with a PAM‑style abstention algebra, delivering deterministic decisions and explicit abstention when evidence is ambiguous.
Why Abstention Matters
In hyperscale environments, noisy telemetry can lead to false positives. By introducing abstention flags the system can explicitly state “insufficient evidence” rather than forcing a potentially wrong decision. This results in more stable and explainable RCA outcomes.
CoreSec Architecture
- Telemetry agents equipped with control flags for deterministic composition.
- PAM‑style algebra that merges evidence while preserving abstention semantics.
- Topology‑aware configurations that map failure surfaces across Clos fabrics.
- Monotonic convergence as additional evidence becomes available.
Production Experience
Deployed across multiple hyperscale datacenters, CoreSec has demonstrated:
- Consistent RCA results without the need for frequent retuning.
- Improved operator confidence due to clear abstention signals.
- Scalable performance on large‑scale Clos topologies.
Internal Resources
For more details on our RCA solutions and related technologies, visit our internal knowledge base:
RCA Solutions,
Telemetry Management, and
About Ubos.tech.
Conclusion
The Abstention Protocol embodied in CoreSec provides a practical foundation for automated, reliable root‑cause analysis in modern cloud networks. By embracing abstention, operators gain deterministic, explainable insights even under noisy and incomplete telemetry conditions.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.