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The practical AWS pattern is to host your agent in Amazon Bedrock AgentCore Runtime and use the open-source bedrock-agentcore-starter-toolkit to package, configure, deploy, and invoke it. If the agent must execute Python, JavaScript, or TypeScript, add AgentCore Code Interpreter rather than giving the main runtime unrestricted shell or network access.
This guide walks from an isolated Python project to a deployed runtime, then shows how to create a managed code-execution session with deliberate IAM and network controls. Runtime sessions provide isolated execution environments and can preserve conversational context when you reuse a session ID; Code Interpreter sessions run in dedicated microVMs with temporary state.
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What AWS’s agent sandbox consists of
AgentCore Runtime hosts the agent
AgentCore Runtime is the managed host for your agent. AWS describes it as a fully managed service for deploying and operating agents securely at scale with any framework and model. The runtime is framework-agnostic, so the starter example can use the open-source Strands Agents library while other supported frameworks can be adapted to the same runtime entrypoint.
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Code Interpreter is the managed execution sandbox
AgentCore Code Interpreter is a managed tool that runs agent-generated code and file operations in a containerized environment. AWS documents support for Python, JavaScript, and TypeScript. Each tool session runs in a dedicated microVM with isolated CPU, memory, and filesystem resources.
Code Interpreter has three network modes:
| Network mode | Reach | Use it when |
|---|---|---|
| Sandbox | Limited access to AWS services | The code needs narrowly scoped AWS access but should not browse the public internet. |
| Public | Public internet access | Internet access is an explicit requirement and the code and data have been reviewed for that exposure. |
| VPC | Private resources while remaining isolated from the public internet | The agent must reach private databases, services, or endpoints in your VPC. |
The network mode and IAM execution role jointly determine what sandboxed code can reach. Neither setting should be treated as a substitute for the other.
Choose Runtime-only or Runtime plus Code Interpreter
Start with Runtime-only when the agent handles text, calls approved tools, and does not need to execute untrusted code. Add Code Interpreter when the agent must calculate with generated programs, transform uploaded files, or run scripts whose contents are not known in advance.
| Decision axis | Runtime-only | Runtime plus Code Interpreter |
|---|---|---|
| Arbitrary code execution | Not provided by the hosting layer | Managed execution for Python, JavaScript, and TypeScript |
| Network reach | Controlled by the runtime’s surrounding AWS design | Explicitly choose Sandbox, Public, or VPC for the interpreter |
| IAM scope | Runtime role and any tool roles | Runtime permissions plus a least-privilege interpreter execution role |
| State | Context can persist when the same runtime session ID is reused | Code state and files persist only while the interpreter session is active |
| Operational overhead | One runtime to configure and observe | Runtime plus interpreter lifecycle, permissions, and session cleanup |
| Framework compatibility | Framework-agnostic runtime | Same runtime compatibility, with an additional managed tool |
Prerequisites and project isolation
The current AgentCore Runtime quickstart requires Python 3.10 or newer. Use a separate virtual environment for each agent so that the runtime, framework, and toolkit dependencies do not collide with another project.
- An AWS account with permission to create or use the resources required by the deployment.
- Python 3.10 or newer installed locally.
- A configured AWS credential source for the CLI and SDK you will use.
- A region selected for the runtime. The toolkit defaults to
us-west-2; choose another region explicitly when required by your data-residency or service-availability design.
Create the project and install the packages named in the quickstart:
mkdir my-agent
cd my-agent
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
pip install bedrock-agentcore strands-agents bedrock-agentcore-starter-toolkitOn Windows, activate the environment with .venvScriptsactivate instead of the Unix source command.
Build the minimal AgentCore entrypoint
The runtime needs a BedrockAgentCoreApp object and an entrypoint function. The entrypoint receives a payload, extracts the prompt, invokes the agent, and returns a JSON-serializable value.
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from bedrock_agentcore.runtime import BedrockAgentCoreApp
from strands import Agent
app = BedrockAgentCoreApp()
agent = Agent()
@app.entrypoint
def invoke(payload):
prompt = payload.get("prompt", "Say hello")
result = agent(prompt)
return {"result": str(result)}
if __name__ == "__main__":
app.run()Save this as my_agent.py. The decorator marks invoke as the function AgentCore calls. The fallback prompt prevents a missing prompt key from producing an avoidable exception. Converting the result to a string ensures the returned object is JSON serializable; in a production agent, return a deliberately structured dictionary containing only the fields your caller needs.
Keep the entrypoint narrow
- Read only the input fields your contract defines, such as
prompt. - Return plain strings, numbers, arrays, and objects rather than framework-specific response objects.
- Keep credentials and network clients outside the incoming payload; obtain them through the execution role and approved configuration.
- Use separate runtime session IDs when users or jobs must not share conversational state.
Run and test the agent locally
Start the file from the activated virtual environment:
python my_agent.pyThe quickstart exposes an HTTP endpoint on port 8080. In a second terminal, send a JSON POST to /invocations:
curl -X POST http://localhost:8080/invocations
-H "Content-Type: application/json"
-d '{"prompt":"Tell me a short joke"}'A successful response confirms that the entrypoint accepts the expected payload and produces a serializable result before you add cloud deployment variables or managed tools. If port 8080 is already occupied, stop the process using it or adjust your local launch arrangement according to the runtime’s current local-server options; do not change the invocation path in the request.
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Configure the deployment
From the project directory, run:
agentcore configure -e my_agent.pyThe toolkit writes .bedrock_agentcore.yaml and prompts for memory options. Without a region flag, configuration defaults to us-west-2. Select another region with -r:
agentcore configure -e my_agent.py -r eu-west-1If the agent should not use managed memory, disable it explicitly during configuration:
agentcore configure -e my_agent.py --disable-memoryMemory is a design decision, not just a deployment switch. Enable it when the application needs the service to manage conversation-related state. Disable it when your application owns state elsewhere or when retaining no managed memory is the safer boundary.
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Deploy to AgentCore Runtime
Deploy the configured project with:
agentcore deployThe toolkit can package direct code deployment or build a container through AWS CodeBuild. Depending on your configuration, it creates or updates IAM, S3, ECR, memory, and CloudWatch resources. At the end, it reports the runtime ARN and a log location; record both as deployment outputs rather than relying on a local filename.
Container builds add a build-service dependency and can take longer than packaging direct code, but they are useful when the agent has system packages or repeatable image requirements. Direct code deployment is simpler for the minimal Python entrypoint. Whichever path you choose, inspect the generated configuration and IAM changes before using the deployment for sensitive workloads.
Check provisioning before invoking
Use the toolkit status command:
agentcore statusWait until provisioning has completed before sending an invocation. An ARN being printed during deployment does not necessarily mean that every backing resource is ready. CloudWatch logs returned by the deployment are the first place to look when status remains pending or an invocation fails after packaging.
Invoke the deployed runtime
For a quick command-line check, pass a JSON object to the toolkit:
agentcore invoke '{"prompt": "tell me a joke"}'For application code, use the AWS SDK operation invoke_agent_runtime. Supply the runtime ARN, a runtime session ID, the request payload, and a qualifier. A stable session ID preserves context for that workflow; generate a separate ID for each user or isolated job.
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import boto3
client = boto3.client("bedrock-agentcore", region_name="us-west-2")
response = client.invoke_agent_runtime(
agentRuntimeArn="arn:aws:bedrock-agentcore:REGION:ACCOUNT_ID:runtime/RUNTIME_ID",
runtimeSessionId="user-123-session-001",
payload=json.dumps({"prompt": "Summarize this task"}).encode("utf-8"),
qualifier="DEFAULT"
)
print(response)Replace the placeholder ARN with the ARN returned by deployment and use the region in which the runtime exists. Keep session identifiers unguessable enough for your application’s isolation model, and never reuse one across users who should not share context.
Add Code Interpreter for controlled code execution
Use the managed aws.codeinterpreter.v1 resource. The lifecycle is explicit: create or select the resource, start a session, invoke executeCode, consume the streamed result, and stop the session.
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Direct Python client 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 even when code execution raises an exception. The API also exposes command-execution and file-management operations, so define which of those your agent is allowed to call instead of exposing every operation by default.
Boto3 lifecycle
The equivalent SDK flow uses start_code_interpreter_session, invoke_code_interpreter, and stop_code_interpreter_session. Keep the session identifier returned by the start call and pass it to subsequent operations. Stream the invocation response as it arrives rather than assuming the entire result is a single text value.
- Call
start_code_interpreter_sessionfor the selectedaws.codeinterpreter.v1resource. - Call
invoke_code_interpreterwithexecuteCode, the language, and the code body. - Process output and file-operation events from the response stream.
- Call
stop_code_interpreter_sessionin normal completion and error-cleanup paths.
Understand session duration and persistence
The documented default Code Interpreter timeout is 900 seconds (15 minutes). Session-characteristics documentation allows a configurable timeout of up to eight hours. Code variables and files persist during the active session, but you should treat the session as temporary and not assume files survive termination. Stop sessions when the job is complete, even if automatic termination is enabled.
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Use a dedicated least-privilege execution role
Give the interpreter an execution role containing only the AWS actions required by the code. AWS’s documented Code Interpreter permissions cover the create, start, invoke, and stop operations; your role may also need narrowly scoped permissions for the AWS resources the code is meant to read or write. Avoid attaching broad administrator policies simply to make an experiment work.
- Separate the runtime role from the Code Interpreter role when their responsibilities differ.
- Scope resource ARNs, actions, and conditions as tightly as the workload allows.
- Review permissions whenever the agent gains a new tool or file workflow.
- Do not place long-lived access keys in prompts, source files, uploaded files, or generated code.
Choose the least permissive network mode
Use Sandbox when limited AWS-service access is sufficient. Use VPC when private resources are required and public internet access is not. Select Public only when the agent genuinely needs to reach public endpoints, and then treat downloaded code and exfiltration paths as part of the threat model.
Separate sessions by trust boundary
A session is a state boundary. Reusing one session ID is useful for a multi-turn workflow, but it also allows later calls to see the state and files created by earlier calls. Use distinct runtime and interpreter session IDs for different users, tenants, or jobs unless shared state is intentional.
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Enable CloudWatch AgentCore observability before production troubleshooting. Correlate the runtime session ID, interpreter session ID, deployment ARN, and request identifier in your application logs. Record which network mode and IAM role were used for each execution so that an unexpected access path can be investigated.
- Monitor deployment and invocation logs in the CloudWatch location returned by deployment.
- Alert on repeated invocation failures, session-start failures, and unusually long code execution.
- Apply a cleanup policy for interpreter sessions that are no longer needed.
- Keep prompts, uploaded files, and generated code out of logs when they contain secrets or personal data.
- Test the failure path: expired sessions, denied IAM actions, unavailable private endpoints, and malformed payloads.
Troubleshooting common failures
The local endpoint does not respond
Confirm that python my_agent.py is still running, that the request uses port 8080 and path /invocations, and that the JSON has a prompt field. A local success isolates deployment problems from entrypoint problems.
Configuration targets the wrong region
Check the region recorded in .bedrock_agentcore.yaml and rerun configuration with agentcore configure -e my_agent.py -r REGION. The runtime ARN, SDK client region, Code Interpreter resource, and IAM assumptions must refer to the intended region.
Deployment fails while creating resources
Review the IAM error and the CloudWatch or deployment log location. The deployment may need permissions for the selected packaging path, including CodeBuild, S3, ECR, memory, or logging resources. Fix the missing permission in the deployment identity or pre-create a constrained resource according to your organization’s policy, then rerun deployment.
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The runtime is deployed but invocation fails
Run agentcore status, verify that the caller is using the returned runtime ARN and qualifier, and confirm that the payload is encoded as JSON. Inspect logs for a non-serializable return value, missing environment configuration, or an exception inside the entrypoint.
Code Interpreter cannot reach a service
Check the selected network mode first. Sandbox intentionally limits external reach, Public is required for public internet endpoints, and VPC requires the private networking path to be configured. Then inspect the interpreter execution role for the specific AWS action and resource that was denied.
Files or variables disappeared
They were likely tied to a session that expired or was stopped. Persist required outputs to an approved durable store before stopping the session, and create a new session for the next job.
A practical rollout sequence
- Prove the agent logic locally with the minimal entrypoint and a JSON response.
- Configure the runtime in the intended region, explicitly choosing whether managed memory is enabled.
- Deploy and record the runtime ARN and log location.
- Invoke with a stable test session ID, then verify that a new session ID does not inherit the old conversation.
- Add Code Interpreter only after the Runtime-only path is healthy.
- Start with Sandbox networking and a dedicated least-privilege role.
- Exercise code execution, file handling, timeout, and cleanup paths.
- Enable observability and document the session, IAM, and network decisions before production traffic.
What to retain from the design
AgentCore Runtime is the managed host and the starter toolkit supplies the CLI deployment path. BedrockAgentCoreApp is the adapter that exposes your framework agent to that host. Code Interpreter is the separate managed boundary for generated code and files. IAM roles and network mode determine what that code can reach, while runtime and interpreter sessions determine how long state remains available. Treat every session as temporary, stop it explicitly, and keep the execution role and network mode no broader than the workload requires.
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