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
®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
@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.
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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
