AWS Launches Amazon Bedrock AgentCore Code Interpreter for Secure AI Code Execution
AWS introduces the Amazon Bedrock AgentCore Code Interpreter, a managed service enabling AI agents to securely execute code in isolated sandbox environments, addressing security and scalability challenges.
Amazon Web Services (AWS) has launched the Amazon Bedrock AgentCore Code Interpreter, a fully managed service designed to enable AI agents to securely execute code in isolated sandbox environments. This new tool aims to address critical challenges around security, scalability, and infrastructure management when deploying AI agents that require computational capabilities.
Key Features and Benefits
- Secure Sandbox Architecture: The service provides low-latency session start-up times and compute-based session isolation, ensuring complete workload separation. It also offers configurable network access policies, supporting both isolated sandbox and controlled public network modes.
- Advanced Session Management: Persistent session state allows for multi-step code execution workflows, while automatic session and resource cleanup ensures efficiency.
- Comprehensive Python Runtime Environment: Pre-installed data science libraries like pandas, numpy, and matplotlib, along with visualization tools such as seaborn and bokeh, are included.
- Enterprise Integration: The service integrates with AWS Identity and Access Management (IAM) and AWS CloudTrail, providing robust access controls and audit trails for compliance.
How It Works
The AgentCore Code Interpreter operates as a secure, isolated execution environment where AI agents can run code (Python, JavaScript, and TypeScript), perform complex data analysis, and generate visualizations. The workflow involves:
- Deployment and Invocation: An agent is built and deployed using frameworks like Strands, LangChain, or LangGraph.
- Reasoning and Tool Selection: The agent’s underlying LLM analyzes the prompt and selects the Code Interpreter as the appropriate tool.
- Secure Code Execution: The agent generates code, which is executed in a dedicated sandboxed session.
- Observation and Iteration: Results are returned to the agent, enabling iterative problem-solving.
Practical Use Cases
- Automated Financial Analysis: Agents can analyze billing data, generate visualizations, and provide textual summaries.
- Interactive Data Science Assistant: Data scientists can use agents for exploratory data analysis, running iterative code executions.
Getting Started
To begin using the AgentCore Code Interpreter, developers can clone the GitHub repo and follow the provided setup instructions. The service is available in multiple AWS regions and uses a consumption-based pricing model.
Conclusion
The AgentCore Code Interpreter represents a significant advancement in AI agent development, minimizing infrastructure complexity while enhancing security and scalability. For more details, refer to the AWS documentation.
Image: Workflow diagram
Related News
Lenovo Wins Frost Sullivan 2025 Asia-Pacific AI Services Leadership Award
Lenovo earns Frost Sullivan's 2025 Asia-Pacific AI Services Customer Value Leadership Recognition for its value-driven innovation and real-world AI impact.
Baidu Wenku GenFlow 2.0 Revolutionizes AI Agents with Multi-Agent Architecture
Baidu Wenku's GenFlow 2.0 introduces a multi-agent system for parallel task processing, integrating with Cangzhou OS to enhance efficiency and redefine AI workflows.
About the Author
Alex Thompson
AI Technology Editor
Senior technology editor specializing in AI and machine learning content creation for 8 years. Former technical editor at AI Magazine, now provides technical documentation and content strategy services for multiple AI companies. Excels at transforming complex AI technical concepts into accessible content.