Building an Autonomous AIOps Platform with a Root-Cause Ontology

The hard part of intelligent operations was never detecting the anomaly. Time-series anomaly detection, log anomaly detection — those algorithms have been production-ready for years.

What actually blocks deployment is the segment between alarm and remediation. An alarm tells you something is broken, but not what to do about it.

The real problem is missing semantics

Traditional AIOps root-cause analysis is, underneath, doing correlation in metric space:

• An anomaly on link A
• A new error code in device B's logs
• Config C was modified at 14:03

Correlate those and you get a ranked list of "root causes". But that is correlation, not causality. It does not know that a real, business-level dependency exists between "config changed" and "link degraded".

Correlation tells you they happened together. Only causality tells you which one to touch first to stop the bleeding.

Ontology modeling is how you supply that missing semantics

What I did was make the operations domain's knowledge explicit as an ontology graph:

class FaultEvent:
    """A single fault event"""
    occurred_at: datetime
    symptom: Symptom          # symptom layer: what users/business see
    impact_scope: set[Entity] # blast radius

class RootCause:
    """A root-cause hypothesis"""
    entity: Entity             # entity layer: device, config, version
    mechanism: Mechanism       # mechanism layer: why it produces that symptom
    confidence: float
    evidence: list[Evidence]   # evidence chain

The key is the three-layer split: symptom → entity → mechanism. The role of the LLM here is not to replace algorithms — it is to map unstructured operational context (logs, change records, tickets) onto entities in the ontology, which neither rules nor classical algorithms can do.

The trade-off

The honest conclusion: the upfront investment in ontology is heavy, and there is no shortcut around it. Once the ontology stands, everything downstream scales linearly — model size and inference speed become afterthoughts. Without it, an LLM only produces an answer that sounds plausible and is too risky to act on.

The last mile to autonomy

With an ontology and root-cause hypotheses in place, the closed loop becomes possible:

1. Symptom detection (algorithms)
2. Ontology mapping (LLM)
3. Root-cause ranking (algorithms + knowledge)
4. Remediation proposal generation (LLM + template constraints)
5. Execution and verification (controlled actions, with rollback)

Step 5 is the dividing line. Without a rollback mechanism, no matter how good the first four steps are, nobody will let it execute autonomously.