An end-to-end reference architecture for building agentic workflows using
Strands Agents and Amazon Bedrock AgentCore.
This repository demonstrates how to design and deploy a multi-agent chatbot that combines tool execution, memory, browser automation, and agent-to-agent collaboration. It is intended as a realistic, extensible sample for teams exploring advanced agent architectures on AWS.
Short.mp4
 Coding Agent |
 Financial Analysis |
 Browser Automation |
 Web Search |
- Multi-agent orchestration with Strands Agents
- Integration with Amazon Bedrock AgentCore
- Tool-enabled agents (search, finance, weather, browser, code interpreter)
- Autonomous browser, documentation, and analysis workflows
- Modular architecture adaptable to real customer use cases
- Infrastructure-as-Code (CDK) for repeatable deployment
If you are building agentic AI applications on AWS and want a concrete, end-to-end example, this repository is designed to be read, run, and extended.
Quick links:
Architecture · Key Features · Quick Start
This sample combines Strands Agent orchestration with Amazon Bedrock AgentCore services:
| Component | Role |
|---|---|
| Strands Agents | Multi-turn reasoning and tool orchestration |
| AgentCore Runtime | Managed, containerized agent execution |
| AgentCore Memory | Persistent conversation state and summarization |
| AgentCore Gateway | MCP-based tool integration with SigV4 authentication |
| AgentCore Code Interpreter | Secure Python execution for analysis and document generation |
| AgentCore Browser | Headless browser automation with live view |
| AgentCore Identity | End-user authentication and 3LO OAuth delegation |
| AgentCore Observability | Trace collection and agent execution monitoring |
| Amazon Nova Act | Visual reasoning model for browser automation |
- Strands-based agent orchestration
- Amazon Bedrock AgentCore Runtime, Gateway, and Memory
- MCP Gateway tools (Google, Wikipedia, ArXiv, Yahoo Finance, Tavily, Open-Meteo)
- Agent-to-Agent (A2A) collaboration — including remote Claude Agent SDK (Claude Code) for agentic coding tasks
- Built-in Code Interpreter for charts and documents
- Multimodal input and output (vision, charts, documents, screenshots)
- Real-time voice interaction with Amazon Nova Sonic 2
Tools are organized into skills — grouped units with SKILL.md instructions that the agent loads on demand. Instead of injecting all tool documentation into every prompt, the agent activates only the skills it needs:
- L1 Catalog — Skill names and one-line descriptions (always in system prompt)
- L2 Instructions — Full SKILL.md loaded via
skill_dispatcherwhen activated - L3 Execution — Tool calls via
skill_executor
This keeps prompt size small while giving the agent access to detailed instructions when needed.
Design notes:
skills/
├── visual-design/ # Charts, posters, infographics (Code Interpreter)
├── code-interpreter/ # General code execution
├── browser-automation/ # Nova Act browser tools
├── word-documents/ # Word document generation
├── excel-spreadsheets/ # Excel spreadsheet generation
├── powerpoint-presentations/ # PowerPoint generation
├── gmail/ # Gmail read/search/delete (3LO OAuth)
├── google-calendar/ # Calendar events (3LO OAuth)
├── notion/ # Notion pages and databases (3LO OAuth)
├── github/ # GitHub repos, issues, PRs, code (3LO OAuth)
└── ... # 20+ skills (web search, finance, maps, weather, and more)
| Tool Category | Protocol | Examples | Authentication |
|---|---|---|---|
| Local Tools | Direct Python | Web Search, URL Fetcher, Visualization | None |
| Built-in Tools | AWS SDK / WebSocket | Code Interpreter, Browser (Nova Act) | IAM |
| Gateway Tools | MCP | Google Search, Maps, Wikipedia, ArXiv, Finance | SigV4 |
| Private API Tools | MCP (3LO OAuth) | Gmail, Google Calendar, Notion, GitHub | OAuth 2.0 |
| A2A Tools | A2A | Research Agent, Browser-Use Agent | SigV4 |
Total: 100+ tools across 20 tool groups See docs/guides/TOOLS.md for full details.
Real-time voice interaction using Amazon Nova Sonic 2, Strands BidiAgent, and AgentCore Runtime WebSocket.
- Seamless switching between voice and text within a single session
- Shared conversation history across both modes
- Full tool execution support during voice conversations
The system uses AgentCore Memory with:
- Short-term session memory
- Long-term summarized memory
Long conversations are compacted using a context summarization strategy to retain key information while controlling token growth.
Design notes:
Prompt caching reduces input token usage by reusing system prompts, stable instruction blocks, and repeated conversation context across agent loop iterations.
This project originally implemented caching via custom Strands hooks. The approach has since been upstreamed into the Strands SDK as a built-in feature (strands-agents/sdk-python#1438):
from strands.models import BedrockModel, CacheConfig
model = BedrockModel(
model_id="us.anthropic.claude-sonnet-4-6-v1",
cache_config=CacheConfig(strategy="auto")
)Design notes:
Agent-to-Agent communication is handled via the A2A protocol, allowing the supervisor agent to delegate tasks to specialized worker agents such as a deep research agent. Multiple agents collaborating in sequence — each handling its own role — are coordinated using the Swarm pattern.
This architecture also includes a Claude Agent SDK (Claude Code) instance deployed as a remote A2A agent on AgentCore Runtime. The supervisor delegates agentic coding tasks — multi-file implementation, refactoring, test suites — to this agent over A2A, with an S3-backed workspace that persists files across sessions.
Design notes:
- Financial research agents
- Technical research assistants using multi-agent patterns
- Autonomous web automation agents
- Memory-backed conversational assistants
- Hybrid research workflows using MCP, A2A, and AWS SDK tools
- AWS account with Bedrock access
- AWS CLI configured
- Docker installed
- Node.js 18+ and Python 3.13+
git clone https://github.com/aws-samples/sample-strands-agent-with-agentcore.git
cd sample-strands-agent-with-agentcore
cd chatbot-app
./setup.sh
cd ../agent-blueprint
cp .env.example .env
# configure credentials
cd ../chatbot-app
./start.shFrontend will be available at http://localhost:3000.
The interactive deployment script supports: 1. AgentCore Runtime 2. Frontend + BFF 3. MCP Gateway tools 4. A2A runtimes 5. Full stack deployment
cd agent-blueprint
./deploy.sh
sample-strands-agent-chatbot/
├── chatbot-app/
│ ├── frontend/
│ └── agentcore/
└── agent-blueprint/
├── chatbot-deployment/
├── agentcore-gateway-stack/
├── agentcore-runtime-stack/
└── agentcore-runtime-a2a-stack/
- DEPLOYMENT.md
- docs/guides/TROUBLESHOOTING.md
MIT License. See LICENSE for details.