faa5672f84
- Updated pyproject.toml to use latest versions of boto3, botocore, awscli, and agentcore packages - Merged main branch changes with conflict resolution - Restructured README.md to follow template format with overview, prerequisites, setup, and execution sections - Created detailed documentation structure in docs/ folder with specialized content files - Updated package dependencies to use version constraints instead of local wheel files - Removed production-specific language and focused on demo/sample implementation - Added comprehensive documentation covering agents, configuration, deployment, and development
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Deployment and Security
This is a sample implementation that provides examples from a demo environment with simulated data. The agent is deployed in a self-managed way for demonstration purposes.
Deployment Options
Demo Implementation: This sample shows a self-managed deployment running on virtual machines with Python 3.12+ runtime environment. The implementation uses simulated data and mock APIs to demonstrate the multi-agent architecture and capabilities.
Amazon Bedrock AgentCore Runtime: For production scenarios, Amazon Bedrock AgentCore Runtime provides a serverless, auto-scaling environment that eliminates infrastructure management overhead and provides built-in security, monitoring, and cost optimization.
Security Considerations
When working with infrastructure data, consider these general security practices:
- Implement API authentication using OAuth2 or API keys for infrastructure endpoints
- Use AWS IAM roles for Bedrock access instead of long-lived credentials
- Enable TLS encryption for API communications
- Implement audit logging for agent actions and investigations
- Use secret management systems for credential storage
- Apply principle of least privilege for API access
- Regularly rotate API keys and tokens
- Monitor for unusual access patterns or suspicious activities