Build a voice travel concierge with Amazon Bedrock AgentCore, Managed Knowledge Base and Nova Sonic

來源原標題

Build a voice travel concierge with Amazon Bedrock AgentCore, Managed Knowledge Base and Nova Sonic

作者

Ravi Kumar

原始標籤

Advanced (300) · Amazon Bedrock AgentCore · Amazon Nova · Technical How-to

原始連結

https://aws.amazon.com/blogs/machine-learning/build-a-voice-travel-concierge-with-amazon-bedrock-agentcore-managed-knowledge-base-and-nova-sonic/

摘要原文

Airlines already have apps and websites where travelers check flights, pick seats, and manage bookings, and adding a natural voice layer opens those tasks to spoken requests. With this voice layer, a traveler can change a seat or check a delay by speaking, without leaving the app or navigating through screens. Building it requires careful engineering on several fronts. You stream audio in both directions and hold the thread of a conversation across many turns. You also reach your existing backend systems without coupling the agent tightly to them, and you scale when traffic spikes before a holiday weekend. This post shows you how to add a voice travel concierge to an airline application, built on three managed services. Amazon Bedrock AgentCore is an agentic platform for building, deploying, and operating AI agents securely at scale with your choice of framework and model. Amazon Nova Sonic on Bedrock is a speech-to-speech model for real-time voice. Amazon Bedrock Knowledge Bases is a fully managed retrieval augmented generation service that grounds answers in your own documents. A traveler speaks, and the concierge pulls up their itinerary, changes a seat, updates a meal preference, answers a policy question, and connects them to a live agent on request. The concierge runs alongside your existing screens rather than replacing them, so travelers move between tapping and talking in the same session. It runs on services that scale on demand, so you spend your time on the experience instead of the infrastructure. The AI layer connects to a sample airline backend with synthetic data, which accelerates your implementation when you adapt the pattern to your own systems. The project splits into modules, so you can reuse the pieces that fit your existing backend and expose them to the agent as tools. You will learn how to do the following: The architecture separates the front end, the AI agent, and the backend services into distinct layers, so you can develop and scale each one on its own. MCP is an open standard for connecting AI applications to external tools and data. It carries standardized messages between the agent and the backend, which keeps the two loosely coupled. The solution deploys the following services. The following diagram shows the solution architecture, organized into four sections. Figure 1: End-to-end architecture across the backend, the AgentCore Gateway, the AgentCore runtime, and the front end Section A covers the backend infrastructure. Five CDK stacks deploy DynamoDB tables, Lambda functions, API Gateway endpoints, Amazon Bedrock Knowledge Bases, and Amazon Cognito. Section B covers the AgentCore Gateway. One CDK stack creates the Amazon Bedrock AgentCore Gateway with the MCP protocol and exposes every backend endpoint as a tool the agent can call by name. Section C covers the AgentCore runtime. Two CDK stacks provision the runtime infrastructure. Amazon Elastic Container Registry (Amazon ECR) stores the container image, Amazon Simple Storage Service (Amazon S3) holds source uploads, and AWS CodeBuild produces the ARM64 Docker image. The Amazon Bedrock AgentCore runtime runs with WebSocket support using Strands Agents framework with Amazon Nova 2.5 Sonic. Section D covers the front end. One CDK stack deploys the React app on AWS Amplify. The next two steps sit outside the live conversation. They describe how the solution is built and how it runs day to day. Before you begin, confirm that you have the following in place. The solution deploys with a single CDK script. Clone the repository, configure your AWS credentials, and run the deployment script. For detailed deployment steps, see the README in the GitHub repository. API Gateway publishes a REST API with IAM-authorized endpoints and Lambda integration. Amazon DynamoDB stores the sample airline data model with single-digit millisecond latency and on-demand scaling. The data covers customer profiles, bookings, passengers, seat maps, purchase history, preferences, conversation transcripts, and flight status. Travelers ask about baggage limits, change fees, pet travel, and loyalty terms. The solution answers those questions using Retrieval Augmented Generation (RAG) through Amazon Bedrock Knowledge Bases, grounding responses in your airline policy documents. The repository ships with sample policy documents for baggage, cancellation and refunds, change and rebooking, fare class rules, loyalty terms, pet travel, special assistance, and upgrades. You upload your documents to Amazon S3 and create the knowledge base once . From there, Amazon Bedrock handles the rest, including embedding, chunking, indexing, storage, and retrieval. Storage runs on Amazon S3 Vectors, a capability of Amazon S3. Smart Parsing prepares source PDFs so tables and structured layouts retrieve accurately. When your policy documents change, you sync the knowledge base and the new content is available right away. There’s no pipeline to redeploy. Connecting the knowledge base to the agent is direct through AgentCore Gateway. You add the knowledge base as a Connectors target and choose standard or agentic retrieval, and the Gateway exposes it as a named MCP tool alongside your backend API tools. The agent then discovers it at runtime and calls it by name, with no custom Lambda or retrieval code to write. For the setup steps, see Add a knowledge base as a gateway target . To use your own policies, drop your PDF or text files into the policy documents folder and redeploy the knowledge base stack. Each session runs as a managed container on AgentCore runtime with microVM isolation, keeping travelers’ conversations separate under load. AgentCore provides automatic scaling, built-in monitoring, and session routing. For production deployments, add Amazon Bedrock Guardrails to filter prompt-injection attempts and validate response grounding. The confirm-before-write pattern in this solution asks the traveler to confirm before making changes. That pattern, together with the knowledge base citations that trace answers back to source documents, already provides a baseline of responsible AI practice. The agent uses the Strands BidiAgents framework to define the system prompt, the tools, and the conversation flow. Amazon Nova 2.5 Sonic brings the following capabilities to the concierge. Audio streams from the front end as 16 kHz PCM over the WebSocket to AgentCore runtime. Amazon Nova 2.5 Sonic transcribes the speech, the agent picks the right tools, and calls them through MCP. The AgentCore Gateway translates each MCP call into a REST request, Lambda runs the logic and returns results, and Amazon Nova 2.5 Sonic folds those results into a spoken reply. This solution uses Amazon Nova 2.5 Sonic , a speech-to-speech model with strong reasoning for real-time voice. For a voice concierge that calls backend tools and follows a detailed system prompt, that reasoning shows up in a few practical ways: Better tool calling and agentic task completion – The agent selects and chains the right tools reliably across multi-step requests, such as looking up an itinerary and then changing a seat in one conversation. Strong instruction following and reasoning – The model adheres closely to the system prompt, including formatting rules like reading confirmation codes and flight numbers one character at a time. Accurate responsible-AI handling – The model responds helpfully to genuine traveler requests while safely handling inappropriate ones. The solution uses Amazon Cognito User Pools and Identity Pools for role-based access. A traveler signs in with a username and password and receives JWTs, an access token and an ID token. The front end exchanges the ID token with the Cognito Identity Pool for temporary AWS credentials that consist of an access key, a secret key, and a session token. Those credentials sign the WebSocket connection to AgentCore runtime and the requests to API Gateway using Signature Version 4 (SigV4).