Build Production-Ready AI Agents in Minutes with Amazon Bedrock AgentCore and Kiro
Most businesses struggle to deploy AI agents that can scale beyond prototypes. Amazon Bedrock AgentCore with Kiro's MCP server gives you production-ready agents with memory, API gateways and full observability - configured through simple CLI commands.
Why AgentCore Solves the AI Agent Scaling Problem
Most AI agents never make it past the prototype stage because teams underestimate the infrastructure needed for production. Without proper memory management, API gateways, and observability, agents fail under real-world loads or become impossible to maintain.
Amazon Bedrock AgentCore provides these production essentials out of the box. The runtime environment handles scaling automatically while giving developers tools to configure:
- Conversation memory - Both short-term (session) and long-term (user preferences)
- API gateway - Converts existing APIs/Lambdas into agent-usable formats
- Identity management - IAM and Cognito integration for secure access
- Full observability - Monitoring, logging and tracing built-in
90% faster to production: Teams using AgentCore deploy working agents in days instead of months by eliminating infrastructure plumbing work.
Getting Started with Kiro's MCP Server
Kiro's MCP server acts as your AI agent development assistant, providing documentation lookup, code generation, and configuration help through natural language commands.
To set it up:
- Install the MCP server plugin in your IDE (like Curo shown in the video)
- Add the Amazon Bedrock AgentCore MCP server connection
- Use natural language queries like "show me how to create a hello world agent"
The MCP server will generate starter code, handle API conversions, and guide you through configuration - dramatically reducing the learning curve for AgentCore's capabilities.
Local Testing Your First Agent
Before deploying to production, you'll want to test agents locally. The workflow is simple:
- Run the agent locally with UV (Python tool)
- Send test prompts via curl commands
- Verify responses match expectations
For example, to test a basic agent that echoes inputs:
uv run hello_agent.py # Starts local server on port 8080 curl -X POST http://localhost:8080/invocations -d '{"prompt":"Hello my name is Eric"}' The agent should respond with confirmation of your input. This quick feedback loop lets you iterate rapidly before deployment.
Configuring Short and Long-Term Memory
Memory transforms agents from stateless responders to contextual assistants. AgentCore makes memory management simple:
Short-term memory maintains conversation context within a session, while long-term memory persists user preferences across interactions.
To add memory to your agent:
- Run
uv run setup_memoryto create memory resources - Add the returned memory ID as an environment variable
- Test with sequential curl commands to verify context persistence
The MCP server can automatically generate memory-enabled agent code and explain the different memory strategies available.
Production Deployment in One Command
What normally takes days of Docker, ECR and IAM configuration happens with a single command in AgentCore:
uv run agent-core launch This automatically:
- Builds Docker containers for your agent
- Pushes them to ECR
- Configures IAM roles
- Sets up the runtime environment
- Deploys to Amazon Bedrock
The CLI guides you through options like authorization (IAM vs Cognito) and header allowlists during deployment. Everything needed for production readiness is handled automatically.
Frontend Integration Options
Once deployed, you have multiple options for connecting frontends to your agents:
- Direct HTTP calls - Simple POST requests to the agent endpoint
- Agent Client SDK - JavaScript library for easier integration
- Session management - Maintain context across frontend interactions
The MCP server can generate frontend integration code samples tailored to your framework (React, Vue, etc). For complex use cases, CDK constructs are available to customize the integration layer.
Pro Tip: Start with direct HTTP calls for prototyping, then move to the SDK as your frontend needs grow more complex.
Watch the Full Tutorial
See the complete workflow from local testing to production deployment in the video tutorial below. Pay special attention at 4:30 where we demonstrate memory configuration and at 8:15 where we deploy with a single command.
Key Takeaways
Amazon Bedrock AgentCore with Kiro's MCP server fundamentally changes how teams deploy AI agents by providing:
- Production infrastructure out of the box (memory, APIs, observability)
- CLI-driven deployment that handles Docker, ECR and IAM automatically
- Natural language assistance for configuration and troubleshooting
In summary: What used to take weeks of infrastructure work now happens in minutes through simple commands, letting you focus on your agent's business logic instead of plumbing.
Frequently Asked Questions
Common questions about Amazon Bedrock AgentCore
Amazon Bedrock AgentCore is a runtime environment for deploying production-ready AI agents at scale. It provides built-in capabilities like long/short-term memory, API gateways, identity management, and full observability - eliminating the need to build these components from scratch.
Unlike prototyping frameworks, AgentCore handles all the infrastructure concerns automatically, letting developers focus on their agent's business logic and user experience.
- Automatically scales to handle production workloads
- Includes conversation memory out of the box
- Simplifies API integration through built-in gateways
Kiro's MCP server acts as a development assistant for AgentCore, providing documentation lookup, code generation, and configuration help through natural language commands.
Instead of searching through docs, you can ask the MCP server questions like "how do I add memory to my agent?" and it will generate the necessary code and configuration automatically.
- Generates starter code for common agent patterns
- Handles API conversions automatically
- Guides you through advanced configuration
This combination lets you deploy production-ready AI agents in minutes instead of weeks. The key benefits fall into three main categories:
Speed: What used to require weeks of infrastructure work now happens through simple CLI commands. The MCP server eliminates the learning curve for advanced features.
- 90% faster time to production
- No Docker/ECR/IAM expertise required
- Natural language assistance for configuration
Local testing follows a simple workflow using UV (a Python tool) and curl commands:
1. Run the agent locally with uv run agent.py
2. Send test prompts via curl to localhost:8080
3. Verify responses match expectations
- Rapid iteration without deployment delays
- Simple curl commands for basic testing
- Option to use the AgentCore launch command for more complete local testing
AgentCore supports both short-term and long-term memory out of the box:
Short-term memory maintains conversation context within a single session, while long-term memory persists user preferences and data across multiple interactions.
- Multiple memory strategies available
- Simple CLI commands to configure
- Automatically scales with your agent
Yes, AgentCore includes a gateway that can automatically convert existing APIs and Lambda functions into formats your agents can use.
The MCP server helps configure these integrations through simple natural language commands like "connect my agent to our CRM API" - it will generate the necessary configuration and code.
- Works with REST, GraphQL, and Lambda functions
- Automatic request/response conversion
- Built-in authentication handling
The AgentCore CLI handles all deployment steps automatically through a single command:
uv run agent-core launch packages your agent, configures all infrastructure, and deploys to Amazon Bedrock. This includes:
- Building Docker containers
- Pushing to ECR
- Configuring IAM roles
- Setting up the runtime environment
GrowwStacks specializes in implementing production-ready AI solutions using Amazon Bedrock and other platforms. Our team can:
1. Design custom agent workflows for your specific business needs
2. Build and configure agents with memory, API integrations, and other advanced features
3. Handle deployment and scaling to production environments
- Free 30-minute consultation to assess your needs
- Custom agent development tailored to your use case
- Ongoing support and optimization
Ready to Deploy Production AI Agents in Days Instead of Months?
Every day without AI agents is a day your competitors gain ground. GrowwStacks can have your first agent deployed and integrated in under a week.