# Integrating Context-Aware Video AI Agents Into Enterprise Workflows

[NVIDIA Developer Blog](https://yomu.fyi/company/nvidia-developer-blog) · Tanya Lenz · Jul 16, 2026

**Type:** Problem & solution

## Summary

Video analytics AI agents often struggle to integrate into enterprise workflows because video platforms, organizational knowledge bases, and operational systems are isolated. To resolve this, NVIDIA NemoClaw orchestrates the Metropolis Video Search and Summarization blueprint alongside the Retrieval-Augmented Generation blueprint into an automated pipeline. Upfront human-in-the-loop interactions collect analysis parameters, objects of interest, and relevant retrieval queries before video ingestion begins. The system combines hierarchical video summaries with contextual enterprise reference documents to generate structured reports with timestamps and citations. Finally, NemoClaw executes programmatic downstream tasks, such as creating prioritized Jira tickets or dispatching industrial maintenance work orders directly into management tools.

## Context

Enterprise video systems, organizational knowledge bases, and operational tools such as ticketing platforms are traditionally siloed. Video analytics systems typically output static reports that require manual interpretation and human intervention to initiate downstream actions, creating delays in reacting to footage across enterprise environments.

## Approach / What changed

NVIDIA NemoClaw orchestrates the NVIDIA Metropolis Blueprint for Video Search and Summarization (VSS) and the NVIDIA AI Blueprint for Retrieval-Augmented Generation (RAG). The workflow collects parameters via human-in-the-loop prompts, queries vector stores for reference documents, performs hierarchical video summarization, and automatically generates structured reports with citations to trigger downstream operational actions like ticket creation.

## Takeaways

- The integration combines three agent tools: Long Video Summary for hierarchical video analysis, RAG for document retrieval from vector stores, and a report generator with timestamps and citations.
- Human-in-the-loop parameter collection scopes scenarios, detected events, and tracked objects either interactively through a terminal or programmatically for batch runs before processing.
- Computacenter deployed the pipeline to analyze drone, borescope, and thermal footage against OEM manuals, reducing footage-to-work-order creation in Maximo from 30–45 minutes to roughly 19 seconds.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning)

[Read original post](https://developer.nvidia.com/blog/integrating-context-aware-video-ai-agents-into-enterprise-workflows)