
Hcls Deploy Agent
- 1 installs
- 265 repo stars
- Updated August 5, 2026
- aws-samples/amazon-bedrock-agents-healthcare-lifesciences
HCLS Deploy Agent is a Claude Code skill that guides deploying a healthcare/life-sciences agent to Amazon Bedrock AgentCore with Gateway, Memory, Cognito auth, and Registry.
About
HCLS Deploy Agent is a deployment guide for putting a healthcare or life-sciences agent onto Amazon Bedrock AgentCore. It covers configuring the AgentCore Runtime, Gateway (tools exposed as MCP endpoints via Lambda targets), Memory, Cognito OAuth2 Identity, and the Registry. A developer uses it to run prerequisite CloudFormation, launch the agent container to ECR, verify it, and optionally register it for multi-agent discovery. It also lists the AWS MCP servers that assist during deployment.
- Deploys HCLS agents to Amazon Bedrock AgentCore: Runtime, Gateway, Memory, Identity, Registry
- Exposes tools as MCP endpoints via AgentCore Gateway (Lambda targets)
- Sets up Cognito OAuth2 authentication and conversation-memory persistence
Hcls Deploy Agent by the numbers
- 1 all-time installs (skills.sh)
- Ranked #14,102 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
hcls-deploy-agent capabilities & compatibility
- Capabilities
- agent deployment · mcp gateway · auth setup
- Works with
- aws · docker
- Use cases
- devops · ci cd · orchestration
- Pricing
- Free
What hcls-deploy-agent says it does
Gateway — Exposes tools as MCP endpoints (Lambda targets)
Launch (builds container, pushes to ECR, deploys to Runtime)
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| Installs | 1 |
|---|---|
| repo stars | ★ 265 |
| Last updated | August 5, 2026 |
| Repository | aws-samples/amazon-bedrock-agents-healthcare-lifesciences ↗ |
What it does
Deploy a healthcare/life-sciences agent to Bedrock AgentCore with Gateway MCP tools, Cognito auth, and memory.
Who is it for?
Developers deploying an HCLS agent to Bedrock AgentCore and exposing tools as MCP endpoints.
Skip if: Building agent logic from scratch; it assumes an agent to deploy already exists.
When should I use this skill?
A developer wants to deploy an HCLS agent, configure AgentCore Gateway/Runtime/Memory/Identity, or register in the Registry.
What you get
An HCLS agent deployed and verified on Bedrock AgentCore, optionally registered for multi-agent discovery.
- Deployed AgentCore Runtime, Gateway, Memory, and Identity
- Optional AgentCore Registry record
By the numbers
- 5 AgentCore deployment components
- 5-step deployment sequence
Files
Deploying an HCLS Agent
When to use this skill
- Developer asks "how do I deploy this agent?"
- Developer needs to configure AgentCore Gateway, Runtime, Memory, or Identity
- Developer wants to expose tools as MCP endpoints
- Developer wants to register an agent in the AgentCore Registry
Deployment Components
AgentCore Deployment
├── Runtime — Hosts the agent container
├── Gateway — Exposes tools as MCP endpoints (Lambda targets)
├── Memory — Conversation persistence (semantic, summary, preferences)
├── Identity — Cognito OAuth2 authentication
└── Registry — Agent discovery for multi-agent workflowsSteps
1. Prerequisites
# Install AgentCore CLI
pip install bedrock-agentcore
# Configure
agentcore configure --entrypoint main.py \
-rf agent/requirements.txt \
-er <IAM_ROLE_ARN> \
--name <agent-name>2. Deploy Infrastructure
Run the prerequisite script (creates Lambda tools, Cognito, Gateway):
./scripts/prereq.shThis deploys CloudFormation stacks for:
- Lambda functions (tool handlers)
- IAM roles (agent execution, Gateway invocation)
- Cognito user pool (OAuth2 authentication)
- AgentCore Gateway (MCP endpoint with tool targets)
- AgentCore Memory (conversation persistence)
3. Deploy Agent Runtime
# Remove stale config
rm -f .agentcore.yaml
# Launch (builds container, pushes to ECR, deploys to Runtime)
agentcore launch4. Verify
# Invoke the deployed agent
agentcore invoke '{"prompt": "Hello, can you help me?"}'
# Test Gateway tools independently
python tests/test_gateway.py --prompt "Test query"
# Test memory
python tests/test_memory.py load-conversation5. Register in AgentCore Registry (optional)
For multi-agent discovery, register the agent:
import boto3
client = boto3.client('bedrock-agentcore')
client.create_registry_record(
registryName="hcls-registry",
recordName="<agent-name>",
description="<rich description for semantic search>",
descriptorType="MCP", # or "A2A" for agent-to-agent
descriptors={...}
)Deployment Templates
| Template | What it deploys |
|---|---|
agentcore_template/ | Backend: Runtime + Gateway + Memory + Streamlit UI |
| FAST | Full-stack: React/Amplify + Cognito + AgentCore + CDK |
AWS MCP Servers Used
When deploying, the following AWS MCP servers help:
agentcore-docs— API reference for Gateway/Runtime/Memory/Registryaws-mcp— create IAM roles, manage CloudFormation stacks, configure S3strands-docs— framework patterns for agent code
References
- Deployment scripts:
agentcore_template/scripts/ - Full deployment example:
agents_catalog/28-Research-agent-biomni-gateway-tools/scripts/prereq.sh - FAST template deployment:
agents_catalog/35-Terminology-agent/
Related skills
FAQ
What components does an AgentCore deployment include?
Runtime (container host), Gateway (tools as MCP endpoints), Memory (conversation persistence), Identity (Cognito OAuth2), and Registry (agent discovery).
How is the agent launched?
agentcore launch builds the container, pushes to ECR, and deploys it to Runtime after prereq.sh provisions the infrastructure.