Linker Vision's agentic AI monitors 50,000 city cameras for cross‑department

Linker Vision's architecture for agentic AI involves automating event analysis from over 50,000 diverse smart city camera streams to enable cross-department remediation -- coordinating actions across teams like traffic control, utilities and first responders when incidents occur. The ability to query across all camera streams simultaneously enables systems to quickly and automatically turn observations into insights and trigger recommendations for next best actions. Automatic Analysis of Complex Scenarios With Agentic AI Agentic AI systems can process, reason and answer complex queries across video streams and modalities -- such as audio, text, video and sensor data.

This is possible by combining VLMs with reasoning models, large language models (LLMs), retrieval-augmented generation (RAG), computer vision and speech transcription. Basic integration of a VLM into an existing computer vision pipeline is helpful in verifying short video clips of key moments. However this approach is limited by how many visual tokens a single model can process at once, resulting in surface-level answers without context over longer time periods and external knowledge.

In contrast, whole architectures built on agentic AI enable scalable, accurate processing of lengthy and multichannel video archives. This leads to deeper, more accurate and more reliable insights that go beyond surface-level understanding. Agentic systems can be used for root-cause analysis or analysis of long inspection videos to generate reports with timestamped insights.

Levatas develops visual-inspection solutions that use mobile robots and autonomous systems to enhance safety, reliability and performance of critical infrastructure assets such as electric utility substations, fuel terminals, rail yards and logistics hubs. Using VLMs, Levatas built a video analytics AI agent to automatically review inspection footage and draft detailed inspection reports, dramatically accelerating a traditionally manual and slow process.