A protein research copilot can be built using Amazon Bedrock AgentCore to transform how researchers search for structurally similar peptides across large datasets, enabling natural language queries, automated embedding generation, and AI-powered result summarization in a single conversational interface. The system uses the Strands Agents SDK to orchestrate three specialized tools within one agent, deploys to Amazon Bedrock AgentCore for production serving, and stores peptide embeddings in Amazon Aurora PostgreSQL-Compatible Edition with pgvector. The copilot parses natural language user input like “Find 10 similar peptides to the dengue virus peptide LPAIVREAI” into structured tool parameters, deploys a custom ML model (ESM-C 300M) as Amazon SageMaker AI serverless endpoint with bundled weights for fast cold starts, and combines vector similarity search (pgvector on Amazon Aurora PostgreSQL) with metadata filtering in a single query. Prerequisites include an AWS account with access to Amazon Bedrock foundation models (Anthropic Claude Sonnet 4.6), Python 3.12 or later, the AWS CLI configured with appropriate credentials, IAM permissions for Amazon Bedrock, Amazon SageMaker AI, Amazon Aurora, Amazon ECS, and AWS CodeBuild, bedrock-agentcore-starter-toolkit installed, and the IEDB virus epitope dataset. Estimated deployment time is 30–45 minutes.