MongoDB and Hope AI: Craft Enterprise Code with AI

Dorottya Nyarady
September 3, 2025 | Updated: September 5, 2025

The world of software development is constantly evolving, and the demand for tools that streamline processes, increase efficiency, and enable developers to easily create robust applications continues to rise. For this context, MongoDB and Bit.dev have teamed up to bring a transformative integration to the table, combining MongoDB’s leading database platform with Bit Cloud’s AI-powered, component-based development platform, featuring the Hope AI agent.

Bit Cloud is the platform that brings powerful AI and composability capabilities directly to developers through Hope AI. Designed to support smarter, faster development, Hope AI enables architecture planning with control, code generation, collaborative management, and production-ready output.

This partnership showcases how Bit Cloud, with Hope AI, empowers developers to innovate efficiently without sacrificing control. Let’s explore the features of Hope AI and see how it transforms the development process.

Figure 1. The release process.
The title of this diagram says Prompt to build new screens; one click collaboration with developers and business stakeholders; full compliance. Starting on the left of the diagram is Prompt, with the description prompt for building new screens and open a task. This flows to the refine stage, with a definition of build and change the components with AI or code changes. Next is review, with the definition of share and collaborate code with your engineers to fine tune. And finally, release, which means release components for deployment and reuse.

Going from concept to plan: AI-generated code architecture

One of Hope AI’s standout features is its ability to create a code architecture based on user input. This isn’t just about diving straight into coding—Hope AI first provides developers with a clear, visual plan for implementation. Think of it as having an AI architect that listens to your idea, understands your goals, and crafts a tailored blueprint for your application.

At this stage, Hope AI does not generate any code; this enables developers to focus on shaping the architecture to fit their unique requirements. Whether they need to make tweaks or add entirely new elements, this phase is highly customizable. Developers can approve the initial plan at their own pace, knowing that any changes are an integral part of the process.

Generating code for new or existing applications

Once the architecture is approved, Hope AI seamlessly transitions into code generation. This integration is ideal for developers building new applications as well as those enhancing existing ones.

For existing applications, adding features is simple—Hope AI can work off the current application and produce code that integrates directly into the existing framework. This versatility positions Hope AI as an innovative tool for projects in all stages of development, helping developers spend less time working around limitations and more time realizing their creative visions.

While its current focus is on web application development, Hope AI plans to expand its capabilities to mobile app development in future iterations, making this partnership even more promising for the broader developer community.

Gaining full control and ongoing flexibility

A major concern with AI-generated code is the possibility of losing control over what’s created. MongoDB and Hope AI address this head-on—developers maintain complete control and can review every line of the AI-generated code. If edits are required, users can make changes directly within the generated code, ensuring the final product aligns precisely with their vision and requirements.

Beyond initial creation, Hope AI remains an active participant throughout the development process. Need additional components or features later? The AI is always available to assist, making sure your code evolves alongside your project.

Achieving collaboration at the core

One of the most exciting features introduced by Hope AI is the ability to “snap” the code. This functionality enables developers to share and manage code with teammates, promoting seamless collaboration. Teams can work together on the project, implement updates, and review progress without any barriers. This collaborative aspect is crucial for modern development teams that thrive on interconnected workflows.

Taking a privacy-first approach to code creation

Another remarkable feature of Hope AI is its emphasis on privacy. The code generated by Hope AI is 100% private—no sharing for AI model training, no public access to your work. The data is protected, and only the designated project members can access the code.

In today’s data-sensitive landscape, this level of privacy is critical. Developers and organizations can trust that their intellectual property is secure and that the AI agent isn’t repurposing their unique codebase for other uses.

Integrating MongoDB: Configuring MongoDB Atlas credentials

MongoDB plays a crucial role in this collaboration. Hope AI is designed to use MongoDB Community Edition by default, offering developers access to MongoDB’s fast, efficient, and reliable database. However, developers and organizations have the flexibility to choose MongoDB Atlas if they prefer a fully managed, customizable solution. Hope AI fully supports integration with MongoDB Atlas, allowing seamless configuration for those who opt for it.

MongoDB Atlas opens up possibilities for advanced database management, including automatic scaling, global data replication, and powerful analytics features. With this level of customization, developers can create applications that meet even the most complex infrastructure demands.

Promoting the future of AI-assisted development

The collaboration between MongoDB and Hope AI signals more than just a technical partnership; it represents a vision for the future of development. By harnessing the power of AI to streamline coding and by empowering developers to maintain control over their projects, this integration creates an environment where creativity meets efficiency.

As Hope AI expands into mobile app support and MongoDB continues to innovate on its platform capabilities, this partnership will likely set the stage for groundbreaking developments that appeal to large-scale organizations.

Choosing MongoDB and Hope AI

For IT decision-makers, the MongoDB-Hope AI partnership is a win for teams wanting to build faster without compromising quality or security. Here’s why this collaboration stands out:

  1. Rapid prototyping and scaling: The AI-powered architecture design and code generation significantly reduce project timelines while safeguarding customization.

  2. Secure code management: Privacy-first code generation is designed to prioritize data protection and support security best practices.

  3. Enhanced team collaboration: The Snap functionality ensures teamwork thrives, making Hope AI suitable for distributed teams.

  4. On-premises availability: The product extends its flexibility by offering an on-premises deployment option, catering to businesses that require hosting within their own infrastructures.

  5. Integrated MongoDB services: MongoDB Atlas credentials provide unparalleled database management flexibility while MongoDB Community Edition caters to developers just getting started.

Building the future together

The integration of MongoDB and Hope AI empowers developers everywhere. Whether you’re designing from scratch, enhancing an existing app, or scaling your team’s efforts across platforms, this partnership promises the tools and capabilities to bring your ideas to life with unprecedented efficiency and control.

MongoDB and Hope AI are building the future—and developers are at the center of this exciting transformation.

Ready to unlock the full potential of AI-powered development? Visit the MongoDB AI Learning Hub to learn how to begin building AI applications with MongoDB.

Connect with Hope AI today to see how MongoDB and Hope AI can transform your ideas into reality.

← Previous

Accelerating Stablecoin Innovation in US Banking

The stablecoin market has become a global center of attention, with total market capitalization surging to over $250 billion today (including $155 billion by Tether, and $60 billion by Circle) from $120 billion just 18 months ago. What’s more, according to McKinsey 1 , the stablecoin market is forecast to reach more than $400 billion by the end of 2025 and $2 trillion by 2028. This rapid growth, however, brings considerable opportunities and challenges. In the U.S., the landscape is undergoing a major transformation following the approval of the GENIUS Act in July 2025—the first federal framework for dollar-backed stablecoins. The act requires fully backed reserves, monthly disclosures, independent audits for major issuers, and robust user protections. By removing key barriers, it enables banks to issue stablecoins, integrate them into core payment and settlement systems, and to develop new digital assets, products, and services. Several strategic initiatives are already reshaping digital-asset banking, from interbank settlements to retail services. For example: Consortium stablecoin: Major banks are exploring a jointly issued stablecoin for interbank settlements, using a private blockchain to facilitate high-volume transfers. Fiserv partnered with Circle and Solana to launch a stablecoin platform for approx 3,000 community and regional banks. While these projects use blockchain for on-chain transactions, they also require a robust off-chain data layer to handle complex banking operations at speed and scale. MongoDB provides the foundation for rapid development of stablecoin-enabled banking solutions, offering flexibility, scalability, security, resilience, and development agility. How does MongoDB enable stablecoin implementation? Blockchain networks handle on-chain data, immutable transaction records, and smart contracts, but stablecoin applications in banking demand more: user management, compliance reporting, real-time analytics, and integration with legacy systems. MongoDB operates as a powerful layer above the blockchain, managing off-chain data and complementing the blockchain’s strengths. MongoDB’s document model, scalability, and secure architecture align perfectly with the needs of new stablecoin banking platforms, acting as a bridge between blockchain’s decentralized ledger and banking operational requirements. Figure 1. MongoDB enabler for stablecoin. Here are key ways MongoDB empowers the data layer of new stablecoin banking systems: Flexible data model for diverse banking needs Stablecoin platforms manage diverse data types, such as user profiles, new and advanced types of KYC/AML records, transaction metadata, reserve balances, and several regulatory audit logs, which blockchains are not optimized to handle due to their focus on immutable records. MongoDB’s flexible schema document model excels at managing this sort of dynamic, heterogeneous data by storing complex, nested structures—like user wallets with transaction histories—in JSON-like documents, enabling fast queries without predefined schemas. This adaptability allows banks to iterate quickly, adding new fields for compliance or other requirements without costly schema migrations, unlike rigid SQL databases. MongoDB’s flexibility is particularly critical for complex off-chain workflows, such as user onboarding, comprehensive new KYC/AML required in the crypto high fraud risk ecosystem, and compliance reporting, making it an ideal solution for evolving stablecoin applications that must adapt to market conditions and evolving regulatory frameworks. Highly scalable off-chain data enablement Blockchains provide high throughput for on-chain transactions, but off-chain data, such as customer dashboards and real-time balances, demands equally fast processing and scalability to support banking applications. MongoDB delivers horizontal scaling via sharding and distribution of data across clusters, efficiently handling high transaction volumes for digital assets platforms; MongoDB can manage millions of customer interactions with low latency. The ability to scale horizontally complements blockchain’s transactions, ensuring seamless user experiences and making MongoDB a perfect fit for supporting stablecoin banking platforms. Coinbase , one of the largest cryptocurrency exchanges in the U.S., has effectively leveraged MongoDB to significantly enhance its platform scalability across 700 MongoDB clusters and overcome high volumes of unpredictable market traffic spikes. Seamless blockchain network integration Stablecoin platforms require tight integration between on-chain and off-chain systems to function effectively. MongoDB facilitates this with its robust APIs and connectors, enabling real-time syncing through change streams that capture off-chain data updates, such as user profile changes, and align them with blockchain events, ensuring data consistency. It also supports hybrid data storage by managing off-chain data like KYC records and transaction metadata while referencing on-chain transaction hashes, reducing blockchain bloat. MongoDB’s ability to bridge blockchain and legacy banking systems, through an operational data layer, enables seamless integration with existing infrastructure, making it a scalable off-chain data layer that offloads non-critical data from the blockchain while maintaining interoperability, streamlining stablecoin operations. Robust security of sensitive banking data Banking-grade stablecoin platforms demand robust security to protect user data and compliance records. MongoDB provides end-to-end encryption, both at rest, in transit or even in use, to safeguard sensitive information like wallet addresses and reserve balances. Its fine-grained, role-based access controls ensure that only authorized systems or personnel can access critical data, while change streams and logging support compliance by tracking data modifications to align with reserve transparency requirements. MongoDB’s enterprise-grade security complements blockchain’s cryptographic integrity, creating a trusted environment for stablecoin issuance and ensuring banks meet stringent security standards. AI-powered real-time insights AI is no longer optional—it’s foundational for any new platform, including digital assets and stablecoin banking. MongoDB empowers AI data layers through seamless AI integration and Atlas Vector Search , embedding, and reranking, enabling applications to deliver intelligent real-time services. By storing structured data alongside unstructured data, vector embeddings, MongoDB supports deep semantic search, similarity matching, and context-aware decisioning. This allows platforms to detect fraud, automate KYC/AML checks, and personalize experiences with high precision. MongoDB’s scalable, AI-ready architecture unlocks agentic AI to deliver actionable insight and instant risk management to ensure digital asset platforms can respond to threats, opportunities, and user needs with intelligence, agility, and efficiency. Multi-region deployment for global reach Stablecoin platforms often operate across jurisdictions, particularly for models targeting global settlements. MongoDB Atlas supports this with multi-region distributed clusters that ensure low-latency access for international users, critical for cross-border stablecoin use. Its automated backups and replica sets protect against outages, ensuring uninterrupted service for 24/7 crypto operations and no downtime for any data structure changes. MongoDB’s global scalability complements blockchain’s decentralized nature, enabling banks to expand stablecoin services worldwide with reliability and efficiency. Developer agility for rapid deployment With tight deadlines for stablecoin platform rollouts, banks need tools that accelerate development. MongoDB excels by offering a flexible schema design that allows developers to build and iterate quickly, adapting to new stablecoin features or blockchain protocols without delay. and to prototype and deploy stablecoin features quickly, staying ahead of the competition. Stablecoin opportunities and challenges ahead Opportunities Stablecoin platforms offer significant potential for U.S. banks, and MongoDB helps support these advancements. Opportunities include faster money transfers, enabling instant local and international cross-border payments for both retail and corporate clients, as well as interbank payment settlements, significantly reducing settlement times compared to traditional systems. In capital markets, stablecoins facilitate instant or same-day clearing and settlement, unlocking liquidity currently constrained by T+1 cycles, which could lead to a substantial increase in trading volumes, an area often constrained by intermediaries. Stablecoins simplify asset tokenization, enabling fractional assets, digital asset lending/staking, and opening new investment opportunities. By adopting stablecoin platforms, banks can lead the industry and establish new payment rails, positioning themselves as innovators in digital assets and open finance. Challenges Several potential challenges may need to be addressed in the adoption of stablecoin platforms. Interoperability could present issues, as stablecoins, including those pegged to the U.S. dollar and other currencies, may not be easily convertible, though ongoing industry developments and a coalition of banks/parties, this challenge might have less impact over time. The growing number of stablecoins raises considerations about which ones will gain prominence as well. Developing scalable and trusted custody solutions is complex and resource-intensive, which could pose new types of risks and challenges for banks. Additionally, existing cybersecurity and fraud detection systems in traditional banking require enhancements to fully support stablecoin transactions as the blockchain ecosystem has a higher risk exposure. Interacting with underlying blockchain networks may involve complexities due to the decentralized nature of distributed ledgers, potentially introducing both technical and regulatory new considerations. A solid foundation for digital assets MongoDB is the ideal off-chain layer for stablecoin platforms in U.S. banking, complementing blockchain networks with its flexibility, scalability, security, and AI-ready capabilities. Whether powering stablecoins for interbank transactions for retail consumer support, MongoDB enables banks to manage complex data structures, seamlessly integrate with blockchain networks, and deliver real-time secure services. As stablecoin adoption accelerates, it will inevitably bring regulatory uncertainty, heightened risk exposure, and increased demands for real-time compliance and fraud detection. MongoDB empowers financial institutions to adapt stablecoin platforms with confidence, paving the way for a broader adoption of digital assets and cryptocurrencies across banking in the future. To learn more about Agentic AI-powered investment portfolio management, check out our recent solutions page on in our documentation . Discover some of our related success stories, such as how Seracle builds the future of crypto trading with MongoDB's modern, multi-cloud database platform and how MongoDB Atlas Charts enables Kronos to trade billions on crypto markets every day. 1 The stable door opens: How tokenized cash enables next-gen payments; Matt Higginson, McKinsey & Company; July 21, 2025; https://www.mckinsey.com/industries/financial-services/our-insights/the-stable-door-opens-how-tokenized-cash-enables-next-gen-payments .

September 2, 2025
Next →

Multi-Agentic Ticket-Based Complaint Resolution System

In the AI-first era of customer experiences, financial institutions face increasing pressure to deliver faster, smarter, and more cost-effective service, all while maintaining transparency and trust. This blog introduces a multi-agentic architecture, co-developed with MongoDB and Confluent Data Streaming Platform , designed to automate ticket-based complaint resolution. It leverages the AI-native capabilities of both platforms within a real-time event streaming environment. With continuously active agents orchestrated across the entire system, financial institutions can automatically resolve common customer issues in seconds, significantly improving resolution times and boosting customer satisfaction. Specifically, this blog demonstrates the automation of customer complaint handling for a retail bank using a sequence of specialized AI agents. Customers interact through a simple ticket, while behind the scenes, Confluent Cloud for Apache Flink ® powers intent classification, MongoDB Voyage AI -backed semantic search, and contextual reasoning against live customer data to propose resolutions in real time. Structured events flow through a Confluent Cloud for Apache Kafka ® backbone to automate downstream ticketing and update audit logs, with Atlas Stream Processing (ASP) ensuring sub-second updates to the system-of-record database. Business context & challenge Retail banks face an immense operational challenge in managing the sheer volume of customer inquiries they receive on a monthly basis, often numbering in the millions. A substantial portion of these interactions, frequently ranging from 20% to 30%, is dedicated to addressing relatively routine and common issues that could potentially be automated or handled more efficiently. These routine inquiries typically revolve around a few key areas: Card declines or transaction failures: Customers frequently contact their banks when a credit or debit card transaction fails, or when a card is declined. This can be due to various reasons such as insufficient funds, security holds, incorrect PINs, or technical glitches. OTP / authentication problems: Issues related to One-Time Passwords (OTPs) or other authentication methods are a common source of customer frustration and inquiries. This includes not receiving an OTP, OTPs expiring, or difficulties with multi-factor authentication processes when trying to access accounts or authorize transactions. Billing or statement discrepancies: Customers often reach out to clarify charges on their statements, dispute transactions, or report discrepancies in their billing summaries. These inquiries can range from unrecognized transactions to incorrect fees or interest calculations. Addressing these high-volume, routine inquiries efficiently is crucial for retail banks. Streamlining these processes, perhaps through self-service options, AI-powered chatbots, or improved internal tools for customer service representatives, could significantly reduce operational costs, improve customer satisfaction, and free up resources to handle more complex or sensitive customer issues. Current pain points: Long holds or call-backs frustrate customers and drive support costs. The support team must manually sift through past cases and customer records to recommend next steps. Inconsistent audit trails hamper compliance and SLA reporting. Solution overview Our multi-agentic ticket-based complaint resolution system automates first‑line support by orchestrating a series of AI agents, built on MongoDB and Confluent technologies. Why a multi-agentic system? Traditional AI systems often employ a singular, all-encompassing model to handle a broad spectrum of tasks. However, this architecture proposes a more sophisticated and efficient approach: a multi-agentic system. Instead of a monolithic AI, this design breaks down complex responsibilities into smaller, manageable units. Each of these specialized AI agents is dedicated to a distinct, yet crucial, function. This division of labor allows for greater precision, scalability, and resilience within the overall system, as each agent can be optimized for its specific role without impacting the performance of others. This modularity also simplifies debugging and updates, as changes to one agent do not necessitate a re-evaluation of the entire system. Figure 1. Architecture layers & data flow. Architecture overview Ingestion & classification: The initial triage The initial phase of this customer service system is dedicated to efficiently receiving and categorizing incoming customer complaints. This is where raw, unstructured customer input is transformed into actionable intelligence. Ticket (web/mobile) - The customer's voice: This is the primary entry point for customer grievances. Whether submitted via a web portal, a dedicated mobile application, or other digital channels, customers provide their feedback as free-text complaints. This flexibility allows customers to articulate their issues naturally, without being confined to pre-defined categories. The system is designed to handle the nuances of human language, capturing the essence of the problem as expressed directly by the customer. Intent classification agent - Understanding the "why": Technology stack: At the heart of this classification lies a powerful combination of Confluent Flink for real-time stream processing and an LLM ( large language model ). Confluent Flink provides the robust framework necessary to process incoming tickets with low latency, ensuring that customer complaints are analyzed almost instantaneously. The integrated LLM is the intelligence engine, trained on vast datasets of customer interactions to accurately discern the underlying intent behind the free-text complaint. Core functionality: The primary role of this agent is to classify the complaint into specific, pre-defined intents. For example, a customer typing "My credit card isn't working at the ATM" would be classified under an intent like "card not working." Beyond just intent, the LLM is also adept at extracting explicit parameters embedded within the complaint. In the "card not working" example, it would extract the "card ID" if provided, or other relevant identifiers that streamline the resolution process. This automated extraction of key information significantly reduces the need for manual data entry and speeds up the subsequent steps. Semantic retrieval agent - Learning from the past: Technology stack: This agent leverages the advanced capabilities of Voyage AI and Atlas Vector Search . Voyage AI provides sophisticated embedding generation and re-ranking algorithms that are crucial for understanding the semantic meaning of the classified intent. This semantic understanding is then fed into MongoDB Atlas Vector Search, which is purpose-built for efficient similarity searches across vast datasets. Core functionality: Once the intent is classified and parameters extracted, the semantic retrieval agent leverages MongoDB’s Voyage AI and then re-ranks the embeddings to ensure the most relevant matches are prioritized. This process enables the system to efficiently retrieve the ‘N’ most similar past cases or knowledge base entries. This retrieval is critical for two reasons. Historical context: It provides agents (human or automated) with insights into how similar issues were resolved previously, offering potential solutions or precedents. Knowledge base integration: It pulls relevant articles, FAQs, or troubleshooting guides from the knowledge base, equipping the system with immediate information to address the customer's query. This proactive retrieval of information is a cornerstone of efficient problem resolution. Contextual reasoning: Assembling the puzzle With the initial classification and historical context in hand, the system moves to contextual reasoning, where all available data is brought together to formulate the most appropriate resolution. Contextual reasoning agent - The decision maker: Technology stack: MongoDB Atlas serves as the central data platform for this crucial stage. Its flexible document model and real-time capabilities are ideal for aggregating and querying diverse datasets quickly. Core functionality: This agent performs several critical functions: Live data fetching: It connects to and fetches real-time customer profiles and transaction data from various systems. This includes information such as account status, recent transactions, past interactions, and communication preferences. Access to live data ensures that the suggested resolution is based on the most current customer situation. Data merging: The fetched live data is then seamlessly merged with the semantic results obtained from the previous stage (i.e., the relevant past cases and knowledge base entries). This creates a holistic view of the customer's problem, enriched with both their current context and historical solutions. Resolution suggestion: Armed with this comprehensive dataset, the agent applies pre-defined business rules or leverages lightweight LLM prompts to select the best resolution suggestion. These business rules could involve decision trees, conditional logic, or specific protocols for certain complaint types. For more complex or nuanced situations, lightweight LLM prompts can be used to analyze the combined data and suggest a human-like, contextually appropriate resolution. This stage aims to automate as much of the resolution process as possible, only escalating to human intervention when necessary. Execution & eventing: Actioning the resolution The final phase focuses on executing the determined resolution and ensuring that the necessary downstream systems are updated. Resolution execution agent - Turning suggestions into action: Technology stack: This stage uses a scalable and event-driven infrastructure to process the resolution suggestion and convert it into a structured event. Core functionality: This agent takes the suggested action from the Contextual Reasoning Agent (e.g., "issue new card") and converts it into a structured resolution event. This event is a standardized, machine-readable message that contains all the necessary details to trigger the actual resolution in downstream systems. The structured format ensures consistency and interoperability across the ecosystem. Confluent Cloud: Technology stack: Confluent Cloud Core forms the backbone of the eventing architecture. It acts as a high-throughput, low-latency event bus, facilitating the seamless flow of resolution events across the enterprise. Core functionality: Confluent Cloud carries these structured resolution events to various downstream systems. A critical component here is the Confluent Schema Registry, which ensures that all events adhere to governed schemas. This schema enforcement is vital for data integrity and compatibility, preventing errors and ensuring that all consuming systems correctly interpret the event data. The event bus acts as the central nervous system, broadcasting resolution information to all interested parties. Downstream consumer - Closing the loop: This section describes the various systems that consume the resolution events from the Confluent Cloud event bus, ensuring a fully integrated and closed-loop system. Confluent MongoDB Sink Connector: This connector plays a crucial role in maintaining an audit trail and storing detailed resolution information. It efficiently logs all audit and resolution details into MongoDB, providing a historical record of all customer interactions and the actions taken to resolve their issues. This data is invaluable for analytics, reporting, and future system improvements. Confluent MongoDB Source Connector: This connector facilitates closed-loop integrations by streaming any external database changes back into Kafka. This means that if an action taken in a separate, external system (e.g., a manual card activation by a human agent) results in a database change, this change is streamed back into the event bus. This allows other systems within the ecosystem to react to these external changes, ensuring complete synchronization and a truly integrated customer service experience. MongoDB Atlas Stream Processing (ASP): ASP is designed for real-time data processing and updates. It actively listens on Kafka (the underlying technology of Confluent Cloud) for specific status-change events. When such an event occurs (e.g., a card replacement is initiated), ASP processes it and writes low-latency updates back to the Customer Profile collection in MongoDB Atlas. This ensures that the customer's profile is always up-to-date with the latest status of their issue, providing a consistent view for both customers and internal teams. Figure 2. Key benefits & metrics. Why this matters to management Cost reduction & operational efficiency: By automating routine complaint workflows and first-line resolutions, the system significantly reduces call center headcount and manual support costs. Leveraging serverless AI agents and managed services ensures predictable, elastic operational costs that scale with demand rather than fixed staffing. Faster, contextual resolutions with AI: End-to-end AI-assisted decisioning powered by intent classification, semantic search, and real-time customer context drives faster, more accurate resolutions. Personalized outcomes improve customer satisfaction while reducing friction across the support journey. Enterprise-grade observability & compliance: Every decision and action is governed by enforcement via the Confluent Schema Registry schema, time-stamped, and logged in MongoDB Atlas. This ensures a complete audit trail for internal governance and external regulatory audits streamlining compliance without added overhead. Trusted, scalable platforms: Built on MongoDB and Confluent, the architecture uses proven, enterprise-grade platforms for data, events, and real-time AI workflows. This provides reliability, observability, and high availability by default. Future-proof & extensible design: The modular design allows seamless onboarding of new AI agents for emerging use cases like loan servicing, KYC checks, or investment guidance without re-architecting. This adaptability ensures long-term value as business requirements evolve. Competitive advantage through personalization: Delivering intelligent, real-time resolutions is no longer optional, it’s a differentiator. The system enhances customer loyalty and satisfaction by resolving issues instantly, accurately, and in context. Closing thoughts Agentic architectures are going to be a large part of the future of customer service automation, and they require infrastructure that can reason in real time. MongoDB and Confluent provide a proven, production-ready foundation for deploying multi-agent systems that are cost-effective, explainable, and deeply personalized using the latest AI techniques. Whether you're in financial services or another vertical, this reference architecture offers a start for strategic thinking or even a tangible leap forward. Not yet a MongoDB user? Deploy a free instance of MongoDB Atlas and explore how Atlas can power your AI-driven applications with ease. Not yet a Confluent customer? Start your free trial of Confluent Cloud today ! New users receive $400 to spend during their first 30 days. Kickstart your journey with MongoDB Atlas and Confluent by trying out Confluent Cloud + MongoDB Atlas in real-world gen AI use cases with these Quick Starts: Small Business Loan Agent Chatbot on AWS Medical Knowledge Database on Google Cloud

September 4, 2025