Building an Agentic AI Fleet Management Solution

Fernando Morán and Kirill Makienko
August 19, 2025

Artificial intelligence is revolutionizing the manufacturing and motion industry, with AI-powered solutions now capable of delivering precise, real-time insights that can optimize everything from route planning to predictive maintenance.

Modern vehicles can generate an overwhelming amount of data—nearly 25 GB per hour, through a diverse range of sensors, according to an article from S&P Global Mobility. Contextualizing this data with user feedback, maintenance records, and technical knowledge becomes increasingly challenging as the system scales. These complexities can create inefficiencies, introduce overhead while processing data, and drive up operational costs, hindering the full potential of AI-driven systems.

An efficient fleet management architecture can address these problems by reducing redundancies, optimizing data retrieval processes, and enabling the seamless integration and use of embeddings. MongoDB’s flexible document model fits perfectly to this approach. Unlike legacy SQL databases, MongoDB excels at managing unstructured, semi-structured, and structured data. This capability allows fleet management software to ingest and process diverse data types, including vehicle signal data, geospatial zones, fleet configurations, query logs, route telemetry, maintenance records, and real-time performance scores.

In this post, we will use various MongoDB Atlas features—such as geospatial query operations, time-series collections, Atlas Charts, and aggregation pipelines—to create an agentic AI-powered fleet management system. This system demonstrates how an AI agent can enable intelligent data processing, providing real-time, context-aware responses to user queries in a streamlined manner.

Fleet management software with AI overview

A traditional fleet management system provides features like resource planning, route optimization, and maintenance scheduling which work together to improve Cost Management, Regulatory Compliance, and Overall Operational Effectiveness (OEE).

Our solution harnesses the power of MongoDB's flexible document schema, time-series collections, and geospatial query support to give fleet managers the ability to query, filter and operate on data effectively. Additionally, an AI Agent assists users in obtaining actionable insights through a chat-driven interface.

Figure 1. Architecture of the solution.
This diagram begins at the top left with a user asking a question. The leafy fleet then takes in this request and sends it to Orchestration and reasoning logic, which is powered by the LLM. MongoDB Atlas also supports the logic with Atlas Charts, Database Query, and Vector Search. A response is then sent back to the user.
The AI agent has a chatbot UI. The data captured by the agent is used to trigger an orchestration service which then calls various tools as required and gets data from MongoDB in order to complete its task.

In Figure 1, the telemetry data from our cars is stored in MongoDB in time series collections via microservices. In addition to the telemetry data we store stationary car information (e.g., brand, model, year, VIN, among others) and user configurations, such as past queries and fleet settings. All of this data is leveraged by the agentic system to answer user queries and provide deeper insights for future references to similar queries.

Figure 2 shows the user interface of the agentic system where queries can be submitted directly. Filters allow users to narrow results by fleet, time range, or geozone, while the AI Agent delivers answers using real-time and historical data.

Figure 2. Demo chat section.
Screen grab image of the Leafy Fleet chat demo.

When a user inputs a question into the chat box, the AI Agent analyzes it by embedding the query into metadata and searching for similar prior questions in the historical recommendations collection. Depending on the tools required, the system accesses contextual data across collections, such as time-series metrics, geospatial locations, or maintenance logs, through aggregation pipelines. Once the relevant data is assembled, the AI synthesizes the information into actionable insights, providing the user with an accurate and informative response.

MongoDB features for a fleet management system

RAG framework with MongoDB Vector Search

Agents powered by retrieval-augmented generation (RAG) are transforming fleet management systems by seamlessly integrating real-time contextual information during response generation. MongoDB’s flexible NoSQL model complements RAG by embedding fast, low-latency document data. Combined with Voyage AI’s cost-efficient embedding model, MongoDB accelerates vector search workflows for smarter decision-making

MongoDB’s Atlas Vector Search empowers the agent to operate proactively by connecting user queries with relevant insights stored in the database. For instance, when a fleet manager asks about the current positions of vehicles, the agent leverages MongoDB’s vector search to match the query against historical recommendations. If similar queries already exist, the agent retrieves pre-existing results instantly, reducing both latency and operational costs. In situations where no matching results are found, the agent complements vector search by invoking LLMs to dynamically generate answers, ensuring fleet managers receive accurate and actionable responses.

This streamlined workflow, powered by MongoDB’s unique combination of vector search and flexible data modeling, allows fleet managers to act on real-time, context-aware insights. From analyzing geospatial patterns to addressing systemic vehicle issues, MongoDB enables the agent to simplify complex decision-making while maintaining efficiency. By combining predictive AI capabilities with an optimized, scalable database, this solution transforms fleet management into a more proactive, data-driven process.

Polymorphysm

MongoDB’s document model allows storing polymorphic data structures within the same collection, meaning documents can vary in structure and embed other documents. This flexibility enables our demo to optimize workflows by storing application-specific metadata tailored to fleet operations.

For instance, the historical_recommendations collection stores query and recommendation histories generated by the system’s AI engine, with the ability to embed metadata dynamically, such as the initial question asked, the tool chosen, and the results it got. This enables improved context for future queries by streamlining read operations, and giving more context for our AI agent. For example, a document in this collection might appear as follows:

Figure 3. Document model of historical_recommendations.
Example code for this solution.

This variability in structure without sacrificing efficiency enables MongoDB to adapt to dynamic data storage requirements inherent in polymorphic workflows. By embedding detailed context and avoiding null values, the system can streamline read operations and provide richer context to the AI agent for future queries.

Time series collections

MongoDB's time series collections simplify working with time series data. These specialized collections provide several benefits, including automatic creation of compound indexes for faster data retrieval, reduced disk usage, and lower I/O overhead for read operations. This makes time series collections highly efficient for managing time-stamped data, such as a constant stream of sensor data from vehicles in our application. With these capabilities, fleet managers can enable near real-time access to data, empowering AI agents to rapidly extract actionable insights for fleet management.

In this demo, MongoDB optimizes query efficiency in our time series collections using its bucketing mechanism. This mechanism groups multiple data points within the same time range into compressed blocks, reducing the number of documents scanned during queries. This results in documents scanned during queries. By grouping multiple data points within the same time range, bucketing minimizes read operations and disk usage, enabling faster range queries and ensuring sustained, optimized cluster performance, even under a humongous load.

GeoSpatial queries

MongoDB’s native support for geospatial queries enables seamless integration of robust location-based functionalities. The ability to handle complex geographic data is a powerful tool for industries relying on real-time location-based decision-making.

In our demo, this capability is leveraged to locate vehicles under various conditions, such as identifying vehicles near or inside a specified geofence, while being able to filter by maximum or minimum distance. Also, geospatial queries can be incorporated directly into aggregation pipelines, enhancing AI-driven workflows powered by our AI Agent.

Key takeaways

MongoDB enables fleet managers to efficiently gather, process, and analyze data to uncover actionable insights. These capabilities empower managers to optimize operations, enhance vehicle oversight, and implement smarter, data-driven strategies that drive efficiency and performance. Visit MongoDB Atlas to start modernizing your fleet management system.

Ready to transform your fleet management operations? Unlock real-time insights, optimize systems, and make smarter decisions with MongoDB’s advanced features. If you're interested in exploring how MongoDB enables intelligent fleet management, check out our Leafy Fleet GitHub repository. Access the Leafy Fleet on GitHub.

Additionally, dive deeper into best practices for modeling connected vehicle signal data and learn how MongoDB’s flexible data model simplifies telemetry management at scale. Read the blog post.

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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. Clone the GitHub repository if you are interested in trying out this solution yourself. To learn more about MongoDB’s role in the manufacturing industry, please visit our manufacturing and automotive webpage .

August 18, 2025
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Streamlining Editorial Operations with Gen AI and MongoDB

Are you overwhelmed by the sheer volume of information and the constant pressure to produce content that truly resonates? Audiences constantly demand engaging and timely topics. As the daily influx of information grows massively, it’s becoming increasingly tough to identify what’s interesting and relevant. Consequently, teams are spending more time researching trends, verifying sources, and managing tools than actually creating compelling stories. This is where artificial intelligence enters the media landscape to offer newer possibilities. Tapping into AI capabilities calls for a flexible data infrastructure in order to streamline content workflows, provide real-time insights, and help teams stay focused on what matters most. In this blog, we will explore how combining gen AI with modern databases, such as MongoDB, can efficiently improve editorial operations. Why are your content ideas running dry? Creative fatigue significantly impacts content production. Content leads face constant pressure to generate fresh ideas under tight deadlines, leading to creative blocks. In fact, a recent report from Hubspot, 16% of content marketers struggle with finding compelling new content ideas . This pressure often compromises work quality due to time constraints, leaving little room for delivering authentic content. Another main hurdle is identifying credible and trending topics quickly. In order to find reliable pieces of information, a lot of time is spent on researching and discovery rather than actual creation. This leads to missed opportunities in identifying what’s trending and reduces the audience engagement as well. This presents a clear opportunity for AI, leveraged with modern databases, to deliver a transformative solution. Using MongoDB to streamline content operations MongoDB provides a flexible, unified storage solution through its collections for modern editorial workflows. The need for a flexible data infrastructure Developing an AI-driven publishing tool necessitates a system that can ingest, process, and structure a high volume of diverse content from multiple sources.. Traditional databases often struggle with this complexity. Such a system demands the ability to ingest data from many sources, dynamically categorize content by industry, and perform advanced AI-enabled searches to scale applications. Combining flexible document-oriented databases with embedding techniques transforms varied content into structured, easily retrievable insights. Figure 1 below illustrates this integrated workflow, from raw data ingestion to semantic retrieval and AI-driven topic suggestions. Figure 1. High-level architectural diagram of the Content Lab solution, showing the flow from the front-end through microservices, backend services, and MongoDB Atlas to AI-driven topic suggestions. Raw data into actionable insights We store a diverse mix of unstructured and semi-structured content in dedicated MongoDB collections such as news, Reddit posts, suggestions, userProfiles, and drafts, organized by topic, vertical (e.g., business, health), and source metadata for efficient retrieval and categorization. These collections are continuously updated from external APIs like NewsAPI and Reddit, alongside AI services (e.g., AWS Bedrock, Anthropic Claude) integrated via backend endpoints. By leveraging embedding models, we transform raw content into organised, meaningful data, stored in their specific categories (e.g., business, health) in the form of vectors. MongoDB Atlas Vector Search and Aggregation Pipeline enables fast semantic retrieval, allowing users to query abstract ideas or keywords and get back the most relevant, trending topics ranked by a similarity score. Generative AI services then draw upon these results to automate the early stages of content development, suggesting topics and drafting initial articles to substantially reduce creative fatigue. From a blank page to first draft – With gen AI and MongoDB Once a user chooses a topic, they’re taken to a draft page, as depicted in the third step of Figure 2. Users are then guided by a large language model (LLM)-based writing assistant and supported by Tavily’s search agent, which pulls in additional contextual information. MongoDB continues to handle all associated metadata and draft state, ensuring the user’s entire journey stays connected and fast. Figure 2. Customer flow pipeline & behind-the-scenes. We also maintain a dedicated userProfiles collection, linked to both the drafts and chatbot systems. This enables dynamic personalization so, for example, a Gen Z user receives writing suggestions aligned with their tone and preferences. This level of contextual adaptation improves user engagement and supports editorial consistency. User-generated drafts are stored as new entries in a dedicated drafts collection. This facilitates persistent storage, version control, and later reuse which is essential for editorial workflows. MongoDB’s flexible schema lets us evolve the data model as we add new content types or fields without migrating data. Solving the content credibility challenge Robust data management directly addresses the content credibility. When we generate topic suggestions, we capture and store the source URLs within MongoDB, embedding these links directly into the suggestion cards shown in the UI. This allows users to quickly verify each topic’s origin and reliability. Additionally, by integrating Tavily, we retrieve related contextual information along with their URLs, further enriching each suggestion. MongoDB’s efficient handling of complex metadata and relational data ensures that editorial teams can consistently and confidently vet content sources, delivering trustworthy, high-quality drafts. By combining Atlas Vector Search, flexible collections, and real-time queries, MongoDB assists greatly in building an end-to-end content system that’s agile, adaptable and intelligent. The next section shows how this translates into a working editorial experience. From raw ideas to ready stories: Our system in action With our current solution, the editorial teams can rapidly transition from scattered ideas to structured, AI-assisted drafts, all within a smart, connected system. The combination of generative AI, semantic search, and flexible data handling enables the workflow to become faster, more spontaneous and less dependent on manual effort. Consequently, the system focuses back on creativity as it becomes convenient to discover relevant topics from verified sources and produce personalised drafts. Adaptability and scalability become the essential factors in developing intelligent systems that can produce great results within the content scope. As editorial demands grow constantly, it necessitates an infrastructure that can ingest diverse data, produce insights, and assist in real-time collaboration. This system illustrates how AI coupled with a flexible, document-oriented backend can assist teams to reduce fatigue, enhance quality and accelerate the production without increasing difficulty. It’s not just about automation; it’s about providing a more focused, efficient, and reliable path from idea to publication. Here are a few next steps to help you explore the tools and techniques behind AI-powered editorial systems: Dive Deeper with Atlas Vector Search : Explore our comprehensive tutorial to understand how Atlas Vector Search empowers semantic search and enables real-time insights from your data. Discover Real-World Applications: Learn more about how MongoDB is transforming media operations by reading the AI-Powered Media article. Check out the MongoDB for Media and Entertainment page to learn more about how we meet the dynamic needs of modern media workflows.

August 26, 2025