Diviner: Agentic RCA with Human-Governed Adaptation for Large-Scale Infrastructure

Pradipta Ghosh; Gaurav Mitra; Shripad Nadgowda; Akshil Shah; Junyu Liu; Canturk Isci; Syamla Bandla; Xinyao Hu; Onkar Deshpande; Anik Dasgupta

arXiv · 2610.21281

Diviner: Agentic RCA with Human-Governed Adaptation for Large-Scale Infrastructure

Pradipta Ghosh·Gaurav Mitra·Shripad Nadgowda·Akshil Shah·Junyu Liu·Canturk Isci·Syamla Bandla·Xinyao Hu·Onkar Deshpande·Anik Dasgupta

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Abstract

Root-cause analysis (RCA) in large-scale distributed systems is challenging because a single symptom can cross service, data, configuration, compute, and organizational boundaries. Responders must assemble incomplete evidence and coordinate experts who hold different parts of the causal path. Generic language-model agents do not solve this problem through tool access alone: they can use stale or mistimed context, interpret failed queries as absent symptoms, report unsupported system state, and overload observability services designed for human-paced use. We present Diviner (Domain-Informed Verifiable Incident Navigation through Evidence and Review), an agentic RCA system operating at Meta and organized around a four-stage reliability lifecycle. Encode combines a large corpus of reviewed incidents with service-owner expertise to produce scoped, versioned skills, workflows, typed tools, and an incident knowledge graph. Execute uses an adaptive Lead and context-isolated workers to apply relevant knowledge to point-in-time evidence and maintain evidence-linked hypotheses. Evaluate compares saved investigation trajectories with responder-reviewed outcomes to measure exact cause, useful direction, and Groundedness. Adapt uses recurring gaps and on-call feedback to derive focused operational- knowledge updates, admitting them only after replay, quality checks, and owner approval.

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Pradipta Ghosh, Gaurav Mitra, Shripad Nadgowda, Akshil Shah, Junyu Liu, Canturk Isci, Syamla Bandla, Xinyao Hu, Onkar Deshpande, Anik Dasgupta. 2026-10-15. Diviner: Agentic RCA with Human-Governed Adaptation for Large-Scale Infrastructure. https://arxiv.org/abs/2610.21281

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