Technical structure: past conventional monitoring
Weave’s technical basis depends on a hybrid data graph structure. It processes totally different knowledge varieties by way of specialised analytical engines. It doesn’t try to drive all community knowledge by way of giant language fashions (LLM). This design alternative addresses accuracy issues inherent in making use of generative AI to specific networking knowledge.
“There’s truly a large threat of hallucination if you happen to’re processing time sequence knowledge by way of LLMs,” Subramaniyan mentioned. “So we truly are very particular and cautious to not course of any time sequence knowledge by way of LLMs.”
The system makes use of graph analytics for relationship modeling between community entities. It maintains vector databases for similarity searches. All elements feed right into a unified data graph. This captures each logical relationships (bodily connections) and semantic relationships (useful dependencies) inside the community infrastructure.
Distinguishing state modifications from anomalies
The core differentiator in Weave’s method lies in its capability to differentiate between official state modifications and real anomalies in real-time. Conventional monitoring instruments deal with each eventualities as deviations from baseline. Each require guide investigation to find out acceptable responses.
Weave addresses this by way of temporal evaluation. It considers change patterns over time. This functionality turns into important in large-scale networks. A whole bunch or 1000’s of configuration modifications might happen every day. The system learns from community engineer suggestions. It builds institutional data about what constitutes regular operational modifications versus points requiring intervention.
Integration and deployment mannequin
Weave doesn’t change present community monitoring infrastructure. It positions itself as a topology intelligence layer that enhances present instruments. The agent identifies particular community segments or nodes requiring consideration. This permits conventional monitoring instruments to focus their evaluation efforts extra successfully.
