Generate presentations with Amazon Bedrock AgentCore

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. Solution overview 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. Key concepts This section covers AgentCore runtime, RAG, and content safety guardrails as they apply to this solution. Agent orchestration with Amazon Bedrock AgentCore 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: generate_pptx: Creates PowerPoint presentations by invoking the PPTX Generator Lambda function, which retrieves content from the knowledge base, generates slide outlines, creates AI images, and assembles the final document. generate_summary: Extracts and synthesizes information from the knowledge base using RAG to deliver accurate, grounded responses. 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. Retrieval Augmented Generation (RAG) for enterprise knowledge 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: You upload enterprise documents to Amazon Simple Storage Service (Amazon S3) . The system chunks and parses documents, applying special handling for multimodal content including tables, charts, and images. Amazon Titan Text Embeddings v2 converts each chunk into high-dimensional vectors that capture semantic meaning. Amazon OpenSearch Serverless stores the vectors, creating a searchable vector database. At query time, the system converts the user’s request into an embedding and performs similarity search to retrieve relevant document chunks. Retrieved chunks supply context to the language model, which generates responses grounded in the source material. 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. Amazon Bedrock Guardrails for content safety 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: Input processing: Filters user requests to help prevent injection issues and inappropriate content. Output generation: Monitors model outputs to verify compliance with content policies. Personally identifiable information (PII) handling: Detects and anonymizes or blocks PII. 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. Solution architecture 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: AWS WAF with managed rule sets and IP-based rate limiting on Amazon API Gateway. Amazon Cognito with the Plus feature plan and advanced security mode for threat protection. Amazon Bedrock Guardrails provisioned through AWS CDK for consistent content filtering. Amazon DynamoDB with point-in-time recovery, deletion protection, and customer-managed AWS KMS encryption. Amazon S3 buckets with versioning, server access logging, and block public access. AWS X-Ray tracing on Lambda functions for distributed debugging. Input validation with regex-based tenant ID and session ID verification. Tenant isolation through AWS Security Token Service (AWS STS) role assumption per tenant. Prerequisites Before deploying,