Integrating External AI Agents in Industrial Workflows

This webinar explores how external AI agents can be safely and effectively integrated into industrial workflows using FlowFuse. We'll show how to connect edge data, APIs, and AI services into production-ready pipelines that drive real operational value.

Industrial teams are collecting more data than ever, but turning that data into intelligent, automated action remains a challenge. External AI agents — from LLM-based assistants to domain-specific inference services — promise powerful decision support, but integrating them into operational workflows without compromising reliability, security, or governance requires the right architecture.

In this webinar we will demonstrate how FlowFuse enables structured integration of external AI agents into industrial systems. You'll learn how to orchestrate data from PLCs, MQTT brokers, and time-series databases into AI workflows, route agent outputs back into control systems, and maintain observability and control at the edge and across distributed environments.

Kristopher Sandoval will talk about:

• The difference between experimentation and production-grade AI integration
• Architectural patterns for integrating external AI agents into industrial systems
• Using FlowFuse and Node-RED to orchestrate AI-driven workflows
• Secure API integration and edge deployment considerations
• Observability, governance, and failure handling in AI-assisted automation

A live demo will showcase an external AI agent integrated into a FlowFuse-managed edge workflow, ingesting operational data and returning contextualized recommendations into a running industrial process.

This webinar is ideal for industrial developers, solution architects, automation engineers, and technical leaders exploring AI integration at the edge. Don't miss out on joining us live to learn how to move from AI experimentation to production-ready intelligent workflows with FlowFuse.