Amazon Bedrock AgentCore now supports a supervisor‑agent pattern that can switch between a PowerPoint generation tool and a summarization tool, enabling end‑to‑end creation of client decks from enterprise documents in under 90 seconds. Engineers responsible for AI workflows, platform automation, or security posture need to understand the new components, the data flow, and the isolation mechanisms introduced by this design.
Solution Overview
The implementation deploys a single supervisor agent on the AgentCore runtime. The agent decides whether a request calls for a full slide deck or a concise summary and then invokes the appropriate tool. The surrounding stack includes a .NET 8 minimal API for HTTP integration, AWS Lambda container functions for PowerPoint assembly, and a Retrieval‑Augmented Generation (RAG) pipeline that leverages Amazon OpenSearch Serverless for vector search.
AgentCore Orchestration with Strands Agents
The supervisor agent is written using the open‑source Strands Agents framework, which abstracts tool definitions and routing logic. Two tools are exposed:
- generate_pptx: Calls a Lambda function that pulls relevant chunks from the knowledge base, builds slide outlines with Anthropic Claude Sonnet 4.6, creates images via Stability AI Stable Image Inpaint, and assembles a .pptx file.
- generate_summary: Retrieves and synthesizes information using the same RAG pipeline but returns a text summary instead of a full deck.
Adding new capabilities simply requires defining additional tools; the supervisor logic remains unchanged.
RAG Pipeline and Data Flow
Enterprise documents are stored in Amazon S3 and processed into chunks that preserve tables, charts, and images. Each chunk is embedded with Amazon Titan Text Embeddings v2, producing high‑dimensional vectors that capture semantic meaning. These vectors are indexed in Amazon OpenSearch Serverless, forming a searchable vector collection. At query time, the user request is also embedded, and a similarity search returns the most relevant chunks. The retrieved context is supplied to Claude Sonnet 4.6, which generates output grounded in the source material, reducing hallucinations.
Security and Operational Controls
The solution runs inside an Amazon VPC, isolating the AgentCore runtime, Lambda functions, and OpenSearch endpoint from the public internet. Data at rest in S3, OpenSearch, and the Lambda container images is encrypted with AWS KMS keys. IAM policies follow the principle of least privilege, granting each component only the permissions required to read source documents, write generated files, and invoke the Bedrock models. Observability is provided by the built‑in AgentCore session management and logging, enabling traceability of tool invocations.
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What This Means For Practitioners
Adopting this pattern replaces manual slide‑deck assembly with an automated, model‑driven workflow, cutting weekly effort for finance and compliance teams. Engineers must provision and secure a VPC‑isolated AgentCore runtime, configure OpenSearch Serverless for vector storage, and manage KMS keys for encryption. Operationally, the supervisor agent simplifies adding new tools, but it also introduces a dependency on the Strands Agents SDK and on Bedrock model availability. Security teams should review IAM policies to ensure they do not grant broader access than required, and they should monitor VPC flow logs for unexpected traffic to the OpenSearch data plane, which must remain publicly reachable for Bedrock Knowledge Base access.
