hcls-build-agent

name	hcls-build-agent
description	Use when a developer wants to build a new healthcare or life sciences agent, structure tools and system prompts for an HCLS workflow, or create a Strands agent with domain-specific capabilities. Also use when someone asks about agent architecture, tool design, or system prompt patterns for clinical, genomics, or drug discovery use cases.

Building an HCLS Agent

When to use this skill

• Developer asks "how do I create a new HCLS agent?"
• Developer needs to structure tools, prompts, and workflows for a healthcare domain
• Developer is building an agent for genomics, drug discovery, clinical trials, or other HCLS workflows

Agent Architecture

An HCLS agent is composed of:

Agent = System Prompt + Tools (MCP) + Skills (Knowledge) + Guardrails

Steps

1. Choose a template

Template	When to use
agentcore_template/	Backend-focused: agent runtime + Gateway tools + Streamlit UI
FAST	Full-stack: React frontend + Cognito auth + CDK deployment

2. Define your agent's domain scope

• What HCLS workflow does it address?
• What data sources does it need? (databases, ontologies, literature)
• What actions should it perform? (query, analyze, generate, validate)
• What guardrails are needed? (PHI handling, clinical disclaimers, data validation)

3. Create tools

Tools are Python functions exposed via AgentCore Gateway (Lambda targets) or as local Strands tools.

# Local Strands tool
from strands import tool

@tool
def search_variants(gene: str, significance: str = "pathogenic") -> dict:
    """Search for genetic variants by gene name and clinical significance."""
    # Implementation
    pass

For Gateway tools (accessible to any MCP client), create Lambda functions and register as Gateway targets. See agents_catalog/28-Research-agent-biomni-gateway-tools/ for the pattern.

4. Write the system prompt

Include:

• Domain expertise and role definition
• Available tools and when to use each
• Output format expectations
• Clinical/scientific disclaimers
• Guardrails (what NOT to do)

5. Add MCP server connections

Reference existing MCP servers for domain tools the agent needs:

• Biomedical databases: deploy Biomni Gateway (mcp-servers/agentcore-gateway/biomni-research-tools/)
• Ontology lookup: deploy OLS server (mcp-servers/agentcore-runtime/ontology-lookup-service/)
• Literature: configure PubMed (mcp-servers/third-party/pubmed/)
• Genomics workflows: configure HealthOmics (mcp-servers/aws-public/aws-healthomics/)

6. Test

# Local testing with Strands
python main.py --prompt "Your test query"

# Test Gateway tools independently
python tests/test_gateway.py --prompt "Your test query"

References

• Reference implementation (simple): agents_catalog/24-Deep-Research-agent/
• Reference implementation (full Gateway): agents_catalog/28-Research-agent-biomni-gateway-tools/
• Reference implementation (FAST template): agents_catalog/35-Terminology-agent/
• Strands Agents docs: use the strands-docs MCP server
• AgentCore docs: use the agentcore-docs MCP server

AWS MCP Servers Used

When building infrastructure for the agent, use:

• aws-mcp — create IAM roles, Lambda functions, S3 buckets
• agentcore-docs — AgentCore API reference for Gateway/Runtime/Memory configuration
• aws-healthomics — if the agent needs genomics workflow capabilities