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The practical path is Amazon Bedrock AgentCore Runtime plus the open-source bedrock-agentcore-starter-toolkit. You wrap an agent in a BedrockAgentCoreApp, test it locally over HTTP, configure a deployment, and deploy it with the agentcore CLI. When the agent must execute generated Python, JavaScript, or TypeScript, add AgentCore Code Interpreter and choose its network mode and IAM role deliberately. Runtime sessions isolate execution and can preserve conversational context; Code Interpreter sessions provide a managed microVM for code and file operations.
What you are building
There are two AWS capabilities to distinguish:
- AgentCore Runtime is the managed host for your agent application. It is framework-agnostic, supports multiple open-source frameworks and models, and gives each runtime session an isolated execution environment. Reusing a session ID lets a workflow retain context across invocations.
- AgentCore Code Interpreter is the managed execution tool for agent-generated code and files. It runs Python, JavaScript, or TypeScript in a dedicated microVM with isolated CPU, memory, and filesystem resources.
Runtime alone is sufficient when the agent only calls its model and ordinary tools. Add Code Interpreter when it must calculate with untrusted input, transform files, run data-science code, or execute other arbitrary snippets without putting that code in your main application process.
The starter toolkit supplies the command-line workflow. The minimal lifecycle is:
- Create a Python 3.10 or newer virtual environment.
- Install
bedrock-agentcore,strands-agents, andbedrock-agentcore-starter-toolkit. - Expose your agent through a
BedrockAgentCoreAppentrypoint. - Run and call the app locally on port 8080.
- Run
agentcore configure, thenagentcore deploy. - Check provisioning with
agentcore statusand invoke the deployed runtime.
Prerequisites and isolation choices
Software and AWS access
- Python 3.10 or later.
- An AWS account and credentials configured for the region in which you will deploy.
- Permission to create or use the IAM, S3, ECR, CodeBuild, CloudWatch, AgentCore Runtime, and (if selected) Code Interpreter resources that your deployment requires.
- A model and framework supported by the agent code you intend to run.
The toolkit defaults to the us-west-2 region during configuration. You can select another region with the configure command’s -r option. Region availability and model access are separate AWS concerns, so verify both before deployment.
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Decide what “sandbox” means for your workload
A Runtime session isolates the agent process, but it is not automatically a general-purpose code-execution sandbox. For generated code, use Code Interpreter and select one of its network modes:
| Network mode | Reach | Use when |
|---|---|---|
| Sandbox | Limited access to AWS services | Untrusted code needs selected AWS capabilities but should not have public internet access. |
| Public | Public internet access | The code genuinely needs external sites or package endpoints and that exposure is acceptable. |
| VPC | Private resources through your VPC, while remaining isolated from the public internet | The agent must work with internal services, databases, or private endpoints. |
Network mode does not replace authorization. Attach a dedicated, least-privilege IAM execution role and grant only the AWS actions the code needs.
Step 1: Create an isolated Python project
Keep the agent and its dependencies in a dedicated virtual environment. The following commands create a project directory, activate the environment, upgrade packaging tools, and install the packages used by the AWS quickstart.
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python -m pip install --upgrade pip
pip install bedrock-agentcore strands-agents bedrock-agentcore-starter-toolkitOn Windows PowerShell, activate the environment with .venvScriptsActivate.ps1. Keep the environment active for every local test and toolkit command so the CLI and the Python imports resolve to the same installation.
Step 2: Wrap the agent in a BedrockAgentCoreApp
The runtime needs a clear entrypoint. Create my_agent.py with an application object, an agent object, and a function decorated with @app.entrypoint. The function reads a prompt from the incoming payload, calls the agent, and returns a JSON-serializable value.
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from strands import Agent
app = BedrockAgentCoreApp()
agent = Agent()
@app.entrypoint
def invoke(payload):
prompt = payload.get("prompt", "You are a helpful assistant.")
result = agent(prompt)
return {"result": str(result)}
if __name__ == "__main__":
app.run()The exact agent construction can be more elaborate: you may supply a model, tools, system instructions, or framework-specific middleware. The important adapter contract is that the entrypoint accepts the decoded request, performs the work, and returns data that can be serialized as JSON. Keep credentials out of source files; use the AWS credential chain and IAM roles instead.
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Step 3: Run and test locally
Start the application from the project directory:
python my_agent.pyThe quickstart exposes an HTTP endpoint on port 8080. In a second terminal, send a JSON request to /invocations:
curl -X POST http://127.0.0.1:8080/invocations
-H "Content-Type: application/json"
-d '{"prompt":"Explain why least-privilege IAM matters."}'Confirm that the process starts cleanly, the endpoint accepts the payload, and the response is valid JSON before involving AWS deployment resources. Local testing catches import errors, malformed payload handling, and non-serializable return values at the cheapest point.
Step 4: Configure the AgentCore deployment
From the same activated environment, point the toolkit at the entrypoint:
agentcore configure -e my_agent.pyThe command writes .bedrock_agentcore.yaml and prompts for memory settings. Memory is optional. If each invocation should be stateless, disable it explicitly:
agentcore configure -e my_agent.py --disable-memoryChoose a region during the prompt or pass one directly:
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Step 5: Deploy the runtime
Deploy the configured application with:
agentcore deployThe toolkit can package direct code deployment or build a container through AWS CodeBuild. Depending on your configuration, it creates or uses IAM roles, S3 and ECR resources, memory resources, and CloudWatch resources. Deployment output includes the runtime ARN and a log location; save both with the version of your source and configuration.
Do not assume deployment success means the agent is ready to serve traffic. Provisioning can continue after the command returns, and missing permissions or model access can surface only when the runtime starts.
Step 6: Check status and invoke the deployed agent
Check the resource state:
agentcore statusWhen provisioning is complete, invoke it through the toolkit:
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Runtime sessions and context isolation
A runtime session is the unit of isolated execution and conversational continuity. Repeated invocations with the same session can preserve context, while separate IDs isolate users, tenants, or jobs. Treat the ID as an application-level boundary, not merely a request correlation number.
- Generate a new, unpredictable session ID when starting an unrelated task.
- Persist the ID with the conversation or job record if later requests must see prior context.
- Never reuse one global ID for all users.
- Define what happens when a session expires or is replaced; callers should be able to restart without assuming prior in-memory state still exists.
AWS also documents InvokeAgentRuntimeCommand for deterministic shell commands in the same runtime session. That is useful for controlled operational actions, but it does not turn arbitrary shell access into a safe design by itself; authorization, input validation, and network policy still apply.
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Adding the managed Code Interpreter sandbox
When to add it
Use Code Interpreter when the agent must execute generated code or manipulate files. Typical examples include calculating over a user-uploaded dataset, converting a document, producing a chart, or running a repeatable Python transformation. Keep ordinary business logic in the agent application when code execution is not required; the additional session, IAM, and network surface is unnecessary in that case.
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Use the managed aws.codeinterpreter.v1 resource, or create a custom Code Interpreter when you need a deliberate network mode and execution role. Start a session before executing code. The session provides state and file persistence while it is active.
Execute code with the Python client
The direct Python client follows a start, invoke, stream, and stop pattern:
from bedrock_agentcore.tools.code_interpreter_client import CodeInterpreter
code_client = CodeInterpreter("<Region>")
code_client.start()
try:
response = code_client.invoke("executeCode", {
"language": "python",
"code": 'print("Hello World!!!")'
})
for event in response["stream"]:
print(event)
finally:
code_client.stop()The finally block matters. Stopping the session releases temporary resources and makes lifecycle behavior explicit even when code execution raises an exception.
Use the boto3 operations directly
With boto3, the equivalent lifecycle is explicit: call start_code_interpreter_session, then invoke_code_interpreter, and finally stop_code_interpreter_session. The invoke request identifies the language and code body. The response is streamed, so consume every event and surface execution errors to the calling workflow rather than silently treating an incomplete stream as success.
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The API also exposes command execution and file-management operations. Restrict those operations to the smallest set your agent needs, and validate filenames, paths, and command arguments before passing model-generated values to them.
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Session duration, state, and cleanup
Code Interpreter state is temporary. Variables and files persist during the active session, not as a promise that they will survive session termination. AWS documents a default timeout of 900 seconds (15 minutes) and configurable session timeouts up to eight hours. Select a duration that matches the job rather than keeping sessions open indefinitely.
- Stop a session when the task completes, including error paths.
- Persist durable outputs to an approved storage service before stopping if the user needs them later.
- Design long jobs to checkpoint progress because a timeout or infrastructure interruption can end the session.
- Do not use a previous session’s filesystem as an implicit data store.
Runtime session continuity and Code Interpreter session continuity are related but not identical. Decide whether the agent’s conversation, the interpreter’s files, or both need to persist, then map each requirement to the corresponding session identifier and lifecycle.
IAM and network hardening
Use a dedicated execution role
Create a role for the runtime or interpreter rather than granting broad permissions to a developer identity. Grant only the actions required to create, start, invoke, and stop Code Interpreter sessions, plus the specific AWS services the executed code must access. Separate deployment permissions from runtime permissions so a compromised agent cannot provision unrelated infrastructure.
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- Scope resource ARNs where the service supports resource-level permissions.
- Allow read-only access when writes are not required.
- Keep secrets in managed secret stores and expose only the secret a task needs.
- Review CloudTrail and CloudWatch records for unexpected calls.
Select the narrowest network mode
Start with Sandbox for code that needs limited AWS-service access and no public internet. Choose VPC when private resources are required. Enable Public only when an explicit requirement justifies internet exposure, and pair it with outbound controls, package allowlists, and input handling that assumes external content is untrusted.
Assume model output is untrusted input
Prompt instructions are not a security boundary. Treat generated code, shell commands, paths, URLs, and package names as attacker-controlled values. Validate them, constrain the available tools, cap execution time and output size, and refuse operations outside the task’s allowlist.
Runtime-only versus Runtime plus Code Interpreter
| Decision axis | Runtime only | Runtime plus Code Interpreter |
|---|---|---|
| Arbitrary code execution | Not provided by default; the agent uses its declared tools and application code. | Supported through managed execution of Python, JavaScript, or TypeScript. |
| Network reach | Determined by the runtime deployment and its surrounding AWS networking. | Explicit Code Interpreter choice: Sandbox, Public, or VPC. |
| IAM scope | Runtime role and tool permissions. | Runtime permissions plus a least-privilege interpreter execution role. |
| State | Runtime session can preserve agent context when the session ID is reused. | Interpreter variables and files persist only during its active session; timeout is 900 seconds by default and configurable up to eight hours. |
| Framework compatibility | Framework-agnostic runtime host supporting multiple open-source frameworks and models. | Same host, with an additional managed execution tool. |
| Operational overhead | Configure, deploy, invoke, and monitor the runtime. | All runtime work plus interpreter lifecycle, network, IAM, file cleanup, and execution-stream handling. |
Observability and production operations
Enable AgentCore observability and CloudWatch logging before production troubleshooting. Record the runtime ARN, deployment region, configuration file revision, model identifier, and session ID with each request so an operator can connect an application error to the corresponding runtime and interpreter events.
Useful operational signals include:
- Runtime provisioning state and deployment failures.
- Invocation latency, timeout rate, and stream termination errors.
- Interpreter session starts, stops, and forced terminations.
- IAM access-denied events and unexpected service calls.
- Network failures that distinguish DNS or egress problems from application exceptions.
- Input and output sizes, especially for file operations.
Keep logs free of credentials and sensitive user files. Apply retention and access controls appropriate to the data handled by the agent.
Troubleshooting checklist
The local server does not start
- Confirm the virtual environment is active.
- Run
python -m pip show bedrock-agentcore strands-agentsand verify the packages are installed in that environment. - Check that the file contains
if __name__ == "__main__": app.run(). - Resolve Python import errors before testing the HTTP endpoint.
The local invocation returns an invalid response
- Send a JSON object with a
promptfield and theContent-Type: application/jsonheader. - Return strings, numbers, arrays, or objects that can be serialized as JSON.
- Inspect the agent result conversion; framework response objects may need to be converted to text or a plain dictionary.
Configuration or deployment is denied
- Check the selected region and whether the required Bedrock model is available and enabled there.
- Review the IAM error for the exact missing action rather than adding administrator access.
- Confirm that the deployment role can use the required S3, ECR, CodeBuild, CloudWatch, memory, and runtime resources.
- Run
agentcore statusand inspect the log location printed by deployment.
Code execution cannot reach a service
- Verify the interpreter’s network mode matches the requirement: Sandbox for limited AWS access, VPC for private resources, or Public only for deliberate internet access.
- Check the interpreter execution role’s specific service action and resource permissions.
- For VPC mode, verify routing, security groups, DNS, and private endpoints.
State or files disappear
Confirm that the same intended session ID is being reused and that the interpreter session has not timed out or been stopped. Move any output that must survive the session into durable, authorized storage before cleanup.
Quick Recap
A safe reference workflow
- Develop the agent in a Python 3.10+ virtual environment and keep dependencies pinned for repeatable builds.
- Expose one small
BedrockAgentCoreAppentrypoint and test it locally through/invocations. - Configure memory deliberately; disable it when the design is stateless.
- Deploy with the starter toolkit, record the runtime ARN and logs, and verify status.
- Use stable, isolated runtime session IDs for the context boundaries your application requires.
- Add Code Interpreter only for a demonstrated code-or-file requirement.
- Choose Sandbox or VPC by default for untrusted code, grant a dedicated least-privilege role, and enable Public networking only when necessary.
- Stop interpreter sessions explicitly, persist required outputs, and monitor CloudWatch and IAM events.
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