
Technical architecture: beyond traditional monitoring
Weave’s technical foundation relies on a hybrid knowledge graph architecture. It processes different data types through specialized analytical engines. It does not attempt to force all network data through large language models (LLM). This design choice addresses accuracy concerns inherent in applying generative AI to precise networking data.
“There’s actually a massive risk of hallucination if you’re processing time series data through LLMs,” Subramaniyan said. “So we actually are very specific and careful not to process any time series data through LLMs.”
The system uses graph analytics for relationship modeling between network entities. It maintains vector databases for similarity searches. All components feed into a unified knowledge graph. This captures both logical relationships (physical connections) and semantic relationships (functional dependencies) within the network infrastructure.
Distinguishing state changes from anomalies
The core differentiator in Weave’s approach lies in its ability to distinguish between legitimate state changes and genuine anomalies in real-time. Traditional monitoring tools treat both scenarios as deviations from baseline. Both require manual investigation to determine appropriate responses.
Weave addresses this through temporal analysis. It considers change patterns over time. This capability becomes critical in large-scale networks. Hundreds or thousands of configuration changes may occur daily. The system learns from network engineer feedback. It builds institutional knowledge about what constitutes normal operational changes versus issues requiring intervention.
Integration and deployment model
Weave does not replace existing network monitoring infrastructure. It positions itself as a topology intelligence layer that enhances existing tools. The agent identifies specific network segments or nodes requiring attention. This allows traditional monitoring tools to focus their analysis efforts more effectively.