How to Architect an Agentic AI-Powered Predictive Maintenance Solution
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Predictive maintenance is a methodology that monitors the performance of critical assets and forecasts potential disruptions. With the emergence of Agentic AI, an entirely new era of end-to-end predictive maintenance is taking shape where issues are anticipated, remediation plans are generated and hyper-personalized guidance is offered to workforce. In this session, we will architect a robust predictive maintenance solution by leveraging an Agentic AI-based architecture and MongoDB Atlas developer data platform. We will also demonstrate how multiple AI agents can collaborate and work together to optimize operations across industries.
Chapters
00:00:00 - Introduction
00:01:42 - The High Cost of Unplanned Downtime
00:04:12 - The 5 Key Stages of Predictive Maintenance
00:05:49 - Data Challenges: Why Most Initiatives Stall
00:07:45 - How Agentic AI Transforms Manufacturing
00:11:22 - Scaling AI Agents: From Chatbot to Collaborative System
00:13:47 - Why MongoDB is the Foundation for Agentic AI
00:15:31 - System Architecture: An End-to-End Overview
00:25:13 - LIVE DEMO: Predictive Maintenance in Action
00:25:51 - Step 1: Real-Time Anomaly Detection & Root Cause Analysis
00:27:26 - Step 2: Automated Work Order Generation
00:28:27 - Step 3: Intelligent Maintenance Scheduling
00:30:00 - Fireside Chat with Rajeev Kaul from Accenture
00:31:18 - Applying Agentic AI to the Software Development Lifecycle (SDLC)
00:33:10 - Addressing Governance, Security, and IP Concerns with AI
00:35:43 - The Future of Agentic AI & Advice for Getting Started
Visit Mongodb.com → https://mdb.link/MongoDB
Read the MongoDB Blog → https://mdb.link/Blog
Read the Developer Blog → https://mdb.link/developerblog
Read more updates from .local NYC 2025: https://mdb.link/localNYCblog
Sign-up for a free cluster → https://mdb.link/EEQRD8ZT6Fc-register
Subscribe to MongoDB YouTube→ https://mdb.link/subscribe
Predictive maintenance is a methodology that monitors the performance of critical assets and forecasts potential disruptions. With the emergence of Agentic AI, an entirely new era of end-to-end predictive maintenance is taking shape where issues are anticipated, remediation plans are generated and hyper-personalized guidance is offered to workforce. In this session, we will architect a robust predictive maintenance solution by leveraging an Agentic AI-based architecture and MongoDB Atlas developer data platform. We will also demonstrate how multiple AI agents can collaborate and work together to optimize operations across industries.
Chapters
00:00:00 – Introduction
00:01:42 – The High Cost of Unplanned Downtime
00:04:12 – The 5 Key Stages of Predictive Maintenance
00:05:49 – Data Challenges: Why Most Initiatives Stall
00:07:45 – How Agentic AI Transforms Manufacturing
00:11:22 – Scaling AI Agents: From Chatbot to Collaborative System
00:13:47 – Why MongoDB is the Foundation for Agentic AI
00:15:31 – System Architecture: An End-to-End Overview
00:25:13 – LIVE DEMO: Predictive Maintenance in Action
00:25:51 – Step 1: Real-Time Anomaly Detection & Root Cause Analysis
00:27:26 – Step 2: Automated Work Order Generation
00:28:27 – Step 3: Intelligent Maintenance Scheduling
00:30:00 – Fireside Chat with Rajeev Kaul from Accenture
00:31:18 – Applying Agentic AI to the Software Development Lifecycle (SDLC)
00:33:10 – Addressing Governance, Security, and IP Concerns with AI
00:35:43 – The Future of Agentic AI & Advice for Getting Started
Visit Mongodb.com → https://mdb.link/MongoDB
Read the MongoDB Blog → https://mdb.link/Blog
Read the Developer Blog → https://mdb.link/developerblog
