Generate presentations with Amazon Bedrock AgentCore

來源原標題

Generate presentations with Amazon Bedrock AgentCore

作者

Ian Lunsford

原始標籤

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

原始連結

https://aws.amazon.com/blogs/machine-learning/generate-presentations-with-amazon-bedrock-agentcore/

摘要原文

A financial services team spends significant time each week manually creating client PowerPoint presentations from quarterly reports, compliance documents, and market analyses. Each presentation requires reading hundreds of pages, extracting key data points, structuring slides, and generating supporting visuals. This post walks you through building an AI agent system using Amazon Bedrock AgentCore that automates this workflow, completing in under 90 seconds in our internal testing. This approach reduces presentation creation time while grounding content in source documents. The solution deploys a supervisor agent on AgentCore runtime, a capability of Amazon Bedrock AgentCore, using the Strands Agents framework to orchestrate document generation and summarization tools. Anthropic Claude Sonnet 4.6 (available on Amazon Bedrock) handles content creation, Amazon Titan Text Embeddings v2 powers vector search, and Stability AI Stable Image Inpaint (available on Amazon Bedrock) generates slide images. This post includes a public repository with the code assets. The solution deploys a single supervisor agent on Amazon Bedrock AgentCore runtime that selects between document generation and summarization tools based on user requests. A Retrieval Augmented Generation (RAG) pipeline uses Amazon OpenSearch Serverless for vector storage and semantic search across multimodal content. The solution includes a REST API built with .NET 8 minimal APIs, so you can integrate with existing enterprise applications through standard HTTP calls. AWS Lambda offers serverless compute, including containerized functions for PowerPoint generation. The solution secures the implementation with Amazon Virtual Private Cloud (Amazon VPC) isolation, AWS Key Management Service (AWS KMS) encryption, and AWS Identity and Access Management (IAM) least-privilege access control. This section covers AgentCore runtime, RAG, and content safety guardrails as they apply to this solution. A single AI agent generating documents from large knowledge bases must retrieve content, structure outlines, generate text, create images, and assemble files. Rather than implementing these competing responsibilities in a monolithic function, the solution uses a tool-based architecture where a supervisor agent routes requests to specialized tools. Amazon Bedrock AgentCore runtime provides a platform to build, connect, and optimize agents at scale, with any framework or model. The agent code runs in an isolated container with session management, built-in identity, and observability. The solution uses the Strands Agents framework (an open source SDK for building AI agents) to implement the supervisor logic. The supervisor agent exposes two tools: The agent determines whether a request requires document generation, content summarization, or both, then invokes the appropriate tool. You can add new capabilities by defining new tools in the agent code without modifying existing ones. RAG addresses a fundamental challenge in AI systems: delivering accurate, up-to-date information grounded in enterprise data rather than relying solely on the model’s training data. The implementation uses Amazon Bedrock Knowledge Bases , which combine vector search with large language models (LLMs) to retrieve relevant context before generating responses. The RAG pipeline works as follows: This approach is designed to help reduce hallucinated content by grounding generated documents in source information from the enterprise knowledge base. Note: The Amazon OpenSearch Serverless collection data plane must remain publicly accessible because Amazon Bedrock Knowledge Bases accesses Amazon OpenSearch Serverless from AWS-managed infrastructure, not through customer VPC endpoints. IAM data access policies protect the collection, and VPC-only access restricts the Amazon OpenSearch Serverless dashboard. Enterprise AI applications require content moderation to meet organizational standards and compliance requirements. Amazon Bedrock Guardrails offers built-in content filtering and safety mechanisms integrated throughout the document generation pipeline. Guardrails operate at multiple stages: The implementation creates custom guardrails through AWS Cloud Development Kit (AWS CDK) , configured with specific policies tailored to enterprise document creation. The agent applies guardrails at the model invocation level, providing consistent content safety without requiring manual configuration for each deployment. The following diagram illustrates the agent-based document generation system. Figure 1: Architecture of the Amazon Bedrock AgentCore RAG presentation generation system The data flow begins with user requests arriving through Amazon API Gateway , which routes them to a .NET-based Chat API running on AWS Lambda . The API maintains conversation state in Amazon DynamoDB . It then invokes the supervisor agent hosted on Amazon Bedrock AgentCore runtime. The supervisor agent analyzes the request and selects the appropriate tool. For document generation, it invokes the PPTX Generator Lambda (a containerized Python function). For summarization, the supervisor invokes the Summary Generator Lambda, which queries the knowledge base through the retrieve_and_generate API. The system processes, chunks, and embeds documents stored in Amazon S3 using Amazon Titan Text Embeddings v2. Amazon OpenSearch Serverless indexes the embeddings for fast semantic search. When tools query the knowledge base, Amazon OpenSearch Serverless retrieves relevant chunks, which are then supplied as context to Anthropic Claude Sonnet 4.6 (on Amazon Bedrock) for generating grounded responses. The system stores generated documents in Amazon S3 and returns presigned URLs for secure download. Security measures include: Before deploying, you need the following tools and access configured in your environment: The setup process includes configuring deployment parameters and initializing the AWS CDK environment. Set awscliprofile to your AWS CLI profile name, or leave empty to use environment variable credentials. This creates the AWS CloudFormation CDKToolkit stack required for AWS CDK deployments. The deployment consists of three phases: building artifacts, deploying infrastructure, and deploying the AgentCore runtime agent. The build phase compiles application code and packages deployment artifacts. Run the build script: This compiles the .NET projects (Chat API) and packages them as Lambda deployment packages. It builds the PPTX Generator container image using Docker or Finch and authenticates with Amazon Elastic Container Registry (Amazon ECR) . It then pushes the container image and stores build artifacts in the asset-output directory. The infrastructure deployment creates AWS resources and configures service integrations. Run the deployment script: The deployment creates four AWS CloudFormation stacks in dependency order: After the infrastructure stacks are deployed, deploy the supervisor agent to AgentCore runtime: This script builds the supervisor agent container (ARM64), pushes it to Amazon ECR, creates the AgentCore runtime and endpoint, and updates the Systems Manager parameter with the endpoint Amazon Resource Name (ARN). The Chat API references this endpoint ARN to route requests to the agent. After deployment, AWS CloudFormation outputs include the Amazon API Gateway endpoint URL and resource identifiers. The solution offers several configuration points: Edit agent/app.py to modify the supervisor agent’s system prompt and behavior. The agent uses Strands Agents with two tools defined in agent/tools.py : In pipeline-params.ini , adjust model identifiers: With inference profiles instead of direct model IDs, requests draw on a broader multi-Region capacity pool, which increases throughput and helps maintain consistent performance under load.