Protein researchers face a time-consuming challenge: manually searching through thousands of peptide sequences to find structurally similar candidates is slow, error-prone, and requires deep domain expertise to interpret results. Building a protein research copilot can 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.

This post shows you how to build a conversational protein research assistant that combines three capabilities:

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.

By the end of this post, you will have built an end-to-end agent application that demonstrates how to:

The copilot follows a tool-use pattern where a single Strands agent orchestrates three specialized tools to handle the complete research workflow. When a researcher submits a natural language query, the agent parses it into structured parameters, searches for similar peptides using protein embeddings, and summarizes the results with scientific context.

This single-runtime, multi-tool design keeps the deployment simple while maintaining clear separation of concerns. Each tool encapsulates a distinct capability, and the orchestrator agent decides when and how to invoke them based on the user’s query.