Unlock Multi-Agent AI Predictive Maintenance with MongoDB

The manufacturing sector is navigating a growing number of challenges: evolving customer demands, intricate software-mechanical product integrations, just-in-time global supply chains, and a shrinking skilled labor force. Meanwhile, the entire sector is working under intense pressure to improve productivity, manage energy consumption, and keep costs in check. To stay competitive, the industry is undergoing a digital transformation—and data is at the center of that shift.

Data-driven manufacturing offers a powerful answer to many of these challenges. On the shop floor, one of the most critical and high-impact applications of these strategies is predictive maintenance. Downtime isn’t just inconvenient—it’s expensive. For example, every unproductive hour in the automotive sector now costs $2.3 million (according to Siemens "The True Cost of Downtime 2024" report). For manufacturers across all sectors, predictive maintenance is no longer optional. It’s a foundational pillar of operational excellence.

At its core, predictive maintenance is about using data to anticipate machine failures before they happen. It began with traditional statistical models, evolved with machine learning, and is now entering a new era. As equipment ages and failure behaviors shift, models must adapt. This has led to the adoption of more advanced approaches, including generative AI with retrieval-augmented generation (RAG) capabilities.

But the next frontier is multi-agent systems—AI-powered agents working together to monitor, reason, and act. We’ve explored how generative AI powers predictive maintenance in previous posts. In this blog post, we’ll go deeper into multi-agent systems and how MongoDB makes it easy to build and scale them for smart, responsive maintenance strategies.

Advance your data-driven manufacturing strategy with Agentic AI

AI agents combine large language models (LLMs) with tools, memory, and logic to autonomously handle complex tasks. On the shop floor, this means agents can automate inspections, reoptimize production schedules, assist with fault diagnostics, and more. According to a LangChain survey, 78% of companies are actively developing AI agents, and over half already have at least one agent in production. Manufacturing companies can especially benefit from agentic capabilities across a great variety of practical use cases, as shown in Figure 1.

Figure 1. Agent capabilities and related practical use cases in manufacturing.

But leveraging AI agents in industrial environments presents unique challenges. Integration with industrial protocols like Modbus or PROFINET is complex. Governance and security requirements are strict, especially when agents interact with production equipment. Latency is also a concern as AI models need fast, reliable data access to support real-time responses. And with agents generating and consuming large volumes of data, companies need a data foundation that is reliable and can scale without sacrificing performance.

Many of these challenges are not new to manufacturers—and MongoDB has a proven track record of addressing them. Industry leaders in manufacturing and automotive trust MongoDB to power critical IoT and telemetry use cases. Bosch, for example, uses MongoDB to store, manage, and analyze huge amounts of data to power its Bosch IoT Insights solution. MongoDB’s flexible document model is ideal for diverse sensor inputs and machine telemetry, while allowing systems to iterate and evolve quickly.

It’s important to remember that, at its core, MongoDB was built for change, so when it comes to integrating AI in the shopfloor, it’s no surprise that MongoDB is emerging as the ideal data layer foundation. Companies like Novo Nordisk and Cisco rely on MongoDB to build and scale their AI capabilities, and leading platforms like XMPro APEX AI leverage MongoDB Atlas to create and manage advanced AI agents for industrial applications.

MongoDB Atlas makes it easy to build AI Agents and operate them at scale. As both a vector and a document database, Atlas supports various search methods for agentic RAG, while also enabling agents to store short and long-term memory in the same database. The result is a unified data layer that bridges industrial IoT and agentic AI. Predictive maintenance is a perfect example of how these capabilities come together to drive real impact on the shop floor. In the next section, we’ll walk through a practical blueprint for building a multi-agent predictive maintenance system using MongoDB Atlas.

Building a multi-agent predictive maintenance system

This solution demonstrates how to build a multi-agent predictive maintenance system using MongoDB Atlas, LangGraph, and Amazon Bedrock. This system can streamline complex processes, such as detecting equipment anomalies, diagnosing root causes, generating work orders, and scheduling maintenance. At a high level, this solution leverages MongoDB Atlas as the unified data layer. LangGraph provides the orchestration layer, enabling graph-based coordination among agents, while Amazon Bedrock powers the underlying foundational models used by the agents to reason and make decisions.

The architecture follows a supervisor-agent pattern. The supervisor coordinates tasks and delegates to three specialized agents:

• Failure agent, which performs root cause analysis and generates incident reports.

• Work order agent, which drafts maintenance work orders with detailed requirements.

• Planning agent, which identifies the optimal time slot for the maintenance task based on availability and production constraints.

Figure 2. High-level architecture of a multi-agent predictive maintenance system.

This modular design enables the system to scale easily and adapt to different operational needs. Let’s walk through the full process in four key steps.

Step 1: Failure prediction kicks off the agentic workflow

The process begins with an alert—something unusual in the machine data or logs that could point to a potential failure. MongoDB provides a unified view of operational data, real-time processing capabilities, and seamless compatibility with machine learning tools. Sensor data is processed in real-time using Atlas Stream Processing integrated with ML inference models. Features like native support for Time Series data and Online Archive facilitate managing telemetry data at scale efficiently. All while the downstream applications remain up to date with the latest notifications and dashboards by using Atlas Triggers, Change Streams, and Atlas Charts. From there, the supervisor agent takes over and coordinates the next steps.

Figure 3. End-to-end failure prediction process that generates the alerts.

Step 2: Leverage your data for root cause analysis

The supervisor notifies the Failure Agent about the alert. Manual diagnostics of a machine can take hours—sifting through manuals, historical logs, and environmental data. The AI agent automates this process. It collects relevant documents, retrieves contextual insights using Atlas vector search, and analyzes environmental conditions stored in the database—like temperature or humidity at the time of failure. With this data, the agent performs a root cause analysis and proposes corrective actions. It generates a concise incident report and shares it with the supervisor agent, which then moves the workflow forward.

Figure 4. Failure Agent performing root cause analysis.

Step 3: Work order process automation

The Work Order Agent receives the incident report and drafts a comprehensive maintenance work order. It pulls from previous similar tasks to estimate time requirements, identify the necessary materials, and ensure the right skill sets are listed. All of this is pre-filled into a standardized work order template and saved back into MongoDB Atlas. This step also includes a human-in-the-loop checkpoint. Technicians or supervisors can review and modify the draft before it is finalized.

Figure 5 Work Order Agent is generating a draft work order and routing it for human validation.

Step 4: Finding the optimal maintenance schedule

Once the work order is approved, the Planning Agent steps in. Its task is to schedule the maintenance activity without disrupting production. The agent queries the production calendar, checks staff shift schedules, and verifies inventory availability for required materials. It considers alert severity and rescheduling constraints to find the most efficient time slot. Once the optimal window is identified, the agent sends the updated plan to the scheduling system.

Figure 6. Planning Agent is evaluating constraints to identify the optimal maintenance schedule.

While we focused on a predictive maintenance work flow, this architecture can be easily extended. Need agents for compliance reporting, spare parts procurement, or shift planning? No problem. With the right foundation, the possibilities are endless.

Unlocking manufacturing excellence with Agentic AI

Agentic AI represents a new chapter in the evolution of predictive maintenance, enabling manufacturers to move from reactive responses to intelligent, autonomous decision-making. By combining AI agents with real-time telemetry and a unified data foundation, teams can reduce downtime, cut maintenance costs, and boost equipment reliability. But to work at scale, these systems need flexible, high-performance infrastructure. With native support for time series data, vector search, stream processing, and more, MongoDB makes it easier to build, operate, and evolve multi-agent solutions in complex industrial environments. The result is smarter operations, greater resilience, and a clear path to manufacturing excellence.