The landscape of generative AI is rapidly evolving, and AWS Bedrock is at the forefront of this transformation with groundbreaking innovations that address critical challenges in building production-ready AI solutions. A standout advancement is the expansion of Amazon Bedrock Agents to support multi-agent collaboration, revolutionizing how organizations can orchestrate complex AI workflows – the very solution we’ll explore in depth in this blog post.
AWS has significantly enhanced Bedrock’s capabilities with industry-first features that make it easier to build reliable, scalable AI applications and one of the major features is:
Multi-Agent Collaboration: The platform now natively supports building and coordinating specialized agents for complex workflows, enabling sophisticated architectures like the airport customer service solution we’ll discuss.
These advancements make AWS Bedrock an even more powerful platform for building enterprise-grade AI solutions. In this blog post, we’ll explore how to leverage these new capabilities, particularly the multi-agent collaboration feature, to create a robust customer service solution for high-traffic environments like airports and malls.
In this blog post, we’ll explore how these innovations, particularly the multi-agent collaboration capability, can be leveraged to transform a traditional single-agent customer service system into a more robust, scalable solution. We’ll demonstrate how the combination of specialized agents, automated reasoning checks, and efficient model deployment creates a superior customer experience in high-traffic environments like airports and malls.
At places like malls and airports, passengers often have a variety of queries—from finding food and beverage stalls to booking a cab or inquiring about city tours—and the sheer volume of queries and effort required to address them is significantly high.
1CloudHub is addressing these challenges by leveraging Generative AI-powered chatbots. These chatbots go beyond basic assistance, delivering seamless user experiences by combining advanced AI with innovative architecture. While single-agent setups have been the norm, Multi-agent collaborator system offers a scalable and efficient solution tailored for complex environments.
This can be addressed through a GenAI-powered chatbot to enhance the user experience by offering a range of services and assisting users while they are at a place like airport. The chatbot will serve as an intelligent assistant capable of addressing frequently asked questions (FAQs), assisting in booking transportation services, and facilitating participation in activities, such as city tours.
The existing approach to achieve the above requirement involves a single agent capable of handling multiple tasks. However, as the number of tasks and queries grows, managing everything through a single agent can quickly become cumbersome. This is where a new approach involving multiple specialized agents supervised by a master agent comes into play.
Answering FAQs with Enhanced Precision
The chatbot should address user queries using details of food stalls, services provided and retail stores in a place like airport. The chatbot is expected to quickly deliver accurate, context-aware answers to user queries in real-time. This reduces the time and effort required to navigate multiple pages or resources.
Cab Booking
Passengers should be able book cabs via the chatbot, which integrates with APIs for ride services and taxis. It must support real-time booking, offering options based on user preferences like cab type and occupancy ensuring a seamless experience.
City Tour Booking
The chatbot facilitates city tour booking application by interacting with the tour package details. It must provide real-time slot availability, registers users, and confirm bookings with details on timings.
The system uses a Bedrock agent to manage user queries at places like airports malls and so on, connecting to two distinct knowledge bases and action groups. The first knowledge base handles user FAQs, containing information on food stalls, retail stores, baggage storage, and other services. The second knowledge base holds details about city tour packages available. The Bedrock agent classifies incoming queries into specific intents: whether the query is related to FAQs, tour packages, or booking services.
For FAQ queries, the agent uses a Retrieval-Augmented Generation (RAG) process to fetch relevant documents from the knowledge base and respond with the required information. If the query concerns a city tour, the agent retrieves relevant details from the tour knowledge base.
For booking-related queries, such as booking a cab or a tour package, the agent triggers action groups defined by AWS Lambda functions. These functions are designed to handle cab bookings and tour reservations through OpenAPI schemas. The Lambda functions collect necessary details and process the requests, providing confirmations.
In the existing flow, a single Bedrock agent is responsible for all tasks: answering questions related to available services, booking cabs, and providing details about tours. The agent interacts with multiple knowledge bases and action groups to fulfil these needs. However, this approach can lead to challenges like adding a new action group in an existing agent requires altering the open-API schema and expects the prompt to handle the routing. This approach is prone to errors and may lead in degradation of the existing agent flow considering its monolithic architecture.
Existing Agent Architecture Flow:
Console Screenshots of Single agentic Flow:
The multi-agent collaborator system introduces a modular architecture where a supervisor agent orchestrates the flow of queries, delegating tasks to specialized sub-agents. This design replaces the legacy single-agent setup, ensuring better scalability, isolation, and accuracy.
The Supervisor Agent serves as the central coordinator. Upon receiving a query, it classifies the user’s intent into one of three categories: FAQ, cab booking, or tour-related queries. Based on the classification, the supervisor routes the query to the appropriate sub-agent.
Console Screenshots of Multi Agent Collaborator:
FAQ Bot:
This sub-agent is connected to a knowledge base containing detailed information about food stalls, retail stores, baggage storage, attractions, and other services. Using a Retrieval-Augmented Generation (RAG) process, the FAQ Bot retrieves precise answers for the user’s query and responds accordingly.
Cab Booking Agent:
This agent handles cab reservations through an action group implemented as an AWS Lambda function. Upon receiving a request, it collects details like destination, pickup time, and number of people, processes the booking, and provides a confirmation to the user.
Tour Enquiry and Booking Agent:
This agent has dual functionality. It connects to a knowledge base for answering questions about city tour packages and uses an action group to handle tour bookings. The action group processes user inputs such as selected tours and finalizing the booking process.
By delegating tasks to specialized agents, the system ensures that each query is handled efficiently without overloading any single agent. This modular workflow enhances performance, simplifies maintenance, and allows for seamless integration of new features or agents.
Multi Agentic Flow Architecture:
Advantages of Multi Agent Collaborator over Single Agent |
Isolation of Responsibilities:In multi-agentic flow, Each sub-agent handles a specific task, ensuring clear separation of workflows and preventing overlaps. Whereas a single agent is tasked with handling diverse queries such as FAQs, cab bookings, and tour-related inquiries, making it prone to errors in responses due to single agent workload. Hence a single agentic flow leads to an overburdened agent |
Handling Intent Misclassification:In multi-agentic flow, Specialized agents minimize intent misclassifications, as each agent is designed and fine-tuned for its specific domain. Whereas in a single agent, As the agent handles multiple workflows, misclassifying user intent becomes more likely, leading to incorrect responses or failed actions. |
Scalability:In multi-agentic flow, new services or features can be added as separate sub-agents without disrupting existing agents or workflows. Whereas in a single agent, adding new features or services is challenging since the agent’s complexity grows with each addition, making the system harder to manage over time. |
Modular Design:In multi-agentic flow, maintenance, debugging, and updates are easier as each agent is independent, allowing focused improvements. Whereas in a single agent, with a monolithic architecture, due to lack of modularity, isolating and optimizing specific workflows (action groups, Knowledge bases and OpenAPI Schema) is difficult. |
Troubleshooting:In multi-agentic flow, Issues are easier to diagnose and fix because of the clear demarcation between agents and tasks. Whereas in a single agent, Issues are harder to diagnose and fix, since agent’s context, knowledge base and action groups are a part of single agent leading to interconnected problems, making troubleshooting a complex task. |
Future-Proof System:Multi-agentic flow architecture supports seamless integration of additional agents or features, ensuring adaptability to evolving requirements. Whereas single agent is a limited and rigid system, whose architecture lacks flexibility, making it difficult to integrate with additional agents or features, and is not adaptable to evolving requirements. For example, single agent can only be connected with only a limited number of knowledge bases. |
Initially, the single-agent approach effectively handled diverse user queries, including FAQs, cab bookings, and tour-related inquiries. However, as the need arose to integrate new features like additional domains or knowledge bases, the system’s limitations became apparent. Tasking a single agent with multiple complex workflows led to intent misclassifications and reduced accuracy, as it struggled to differentiate between queries. Moreover, scaling the system or introducing new functionalities without disrupting existing operations proved challenging.
The multi-agent collaborator approach resolves these issues by introducing modularity and specialization. A supervisor agent classifies user queries and delegates them to dedicated sub-agents, each focused on a specific task, such as handling FAQs, managing cab bookings, or processing tour-related queries. This separation of concerns eliminates intent misclassification, enhances task reliability, and enables seamless integration of new features, making the system scalable, adaptable, and more efficient.
Transitioning to a multi-agent architecture isn’t just an upgrade—it’s a strategic investment in scalability, accuracy, and adaptability. Implement this modular approach today to unlock new possibilities, enhance user satisfaction, and prepare your system for the demands of tomorrow.
Contact 1CloudHub today for expert guidance on integrating Generative AI solutions to streamline processes, boost efficiency, and enhance customer experiences. Let’s transform your business with AI-driven innovation!
Written by
GenAI Solution Engineer
GenAI Tech Lead
Chief Technology Officer (CTO) and AWS Ambassador
Updated on December 30, 2024
