Agentic Video Intelligence for 24/7 Operations

Problem

Security and defense operators cannot watch every feed. Analysts burn out on false positives; true incidents arrive late because triage is manual. The platform targets real-time video intelligence: detect, classify, escalate, and drive downstream workflows without requiring a human on every frame.

Architecture

Stateful multi-agent orchestration with LangGraph and MCP:

Detection and event classification agents with persistent memory and tool use
Alert escalation pipelines that respect operational playbooks
FastAPI microservices behind WebSocket event streams for live dashboards

Ingestion: Kafka-based pipelines with asynchronous inference, engineered for sub-200ms ingestion-to-decision latency on hot paths.

Deployment modes: Cloud-native for iteration; self-contained inference stacks for air-gapped, on-premise defense infrastructure where outbound cloud calls are not an option.

Outcomes

Analyst intervention reduced by approximately 70% through automated detection triage and workflow handoff
End-to-end ownership of defense-sector deployments: infrastructure, inference, and React/Next.js operational dashboards

Lessons

Agents need state, not just prompts: classification and escalation are graphs, not single-shot completions.
Latency is a trust metric: operators abandon dashboards that lag the wall of cameras.
Design for disconnected environments early: packaging models and brokers for on-prem avoids a rewrite when classification moves to classified networks.

## Problem Security and defense operators cannot watch every feed. Analysts burn out on false positives; true incidents arrive late because triage is manual. The platform targets **real-time video intelligence**: detect, classify, escalate, and drive downstream workflows without requiring a human on every frame. ## Architecture Stateful **multi-agent orchestration** with LangGraph and MCP: - Detection and event classification agents with persistent memory and tool use - Alert escalation pipelines that respect operational playbooks - FastAPI microservices behind **WebSocket** event streams for live dashboards **Ingestion:** Kafka-based pipelines with asynchronous inference, engineered for **sub-200ms ingestion-to-decision latency** on hot paths. **Deployment modes:** Cloud-native for iteration; **self-contained inference stacks** for air-gapped, on-premise defense infrastructure where outbound cloud calls are not an option. ## Outcomes - Analyst intervention reduced by approximately **70%** through automated detection triage and workflow handoff - End-to-end ownership of **defense-sector** deployments: infrastructure, inference, and React/Next.js operational dashboards ## Lessons 1. **Agents need state, not just prompts**: classification and escalation are graphs, not single-shot completions. 2. **Latency is a trust metric**: operators abandon dashboards that lag the wall of cameras. 3. **Design for disconnected environments early**: packaging models and brokers for on-prem avoids a rewrite when classification moves to classified networks.