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Export harness to code - Amazon Bedrock AgentCore
When to Use ExportWhat is supported todayCLI UsageEXPORT_NOTES.mdDeployment Options

Export harness to code

Export harness to code takes a managed harness configuration and generates the equivalent agent as editable Python source code using the Strands framework. The generated agent is a normal AgentCore runtime agent: you own the code, can modify it freely, and deploy it like any other agent. The generated agent mirrors the harness’s model, tools, skills, memory, execution limits, and filesystem mounts.

When to Use Export

Use export when you have a working harness and want to:

  • Customize beyond what the harness config allows - add custom tool logic, change the agent loop, inject middleware/hooks, or integrate libraries the harness doesn’t expose

  • Own the code - move from a declarative config to source code you control, review, and version like the rest of your application.

  • Graduate a prototype - start fast with a harness, then export when you’re ready to invest in a hand-maintained agent.

If the harness configuration already does everything you need, you don’t have to export - keep running it as a harness.

What is supported today

  • Framework - Strands, with other frameworks like Claude Agents SDK coming soon

  • Language - Python only

  • Source - The harness must be part of an AgentCore CLI project

  • Build types - CodeZip (default) and Container

Harness features carried into the generated agent:

  • Model provider and config (Bedrock, OpenAI, Gemini - including model ID and API key references)

  • Tools - Remote MCP tools, Gateways, inline function tools, AgentCore Browser, AgentCore Code Interpreter, built-in shell and file_operations tools

  • Memory

  • Execution limits

  • Conversation truncation

  • Skills (Path, S3, Git)

  • Filesystem mounts (session storage, EFS, S3) for VPC harnesses

  • Authorizer configuration

CLI Usage

Command Syntax

Example
AgentCore CLI
agentcore export harness --name <name> [options]

Options

Flag Description Default

--name <name>

Name of the harness to export

Required

--target-agent-name <name>

Name for the generated runtime agent

<harnessName>Agent

--build <type>

Build type: CodeZip or Container

CodeZip

--json

Output results as JSON

False

Examples

Basic export with defaults:

Example
AgentCore CLI
# Basic export - generates MyHarnessAgent using CodeZip
agentcore export harness --name MyHarness
# Export with a custom agent name
agentcore export harness --name MyHarness --target-agent-name MyProductionAgent
# Export as a Container build (generates Dockerfile)
agentcore export harness --name MyHarness --build Container
Interactive

Run agentcore export harness without --name to launch the interactive export wizard.

  1. If the project has more than one harness, select the harness to export. (With a single harness, the wizard skips this step.)

    Select the harness to export

  2. Enter a name for the generated runtime agent, or accept the default <harness>Agent .

    Name the generated runtime agent

  3. Choose the build type: CodeZip (package Python source as a zip, the default) or Container (build a Docker image via ECR and CodeBuild).

    Select the build type

  4. Review the export configuration and confirm.

    Review and confirm the export

After export, review EXPORT_NOTES.md , then run agentcore deploy to deploy the generated agent.

After export

Deploy the generated runtime agent using the AgentCore CLI.

Example
AgentCore CLI
agentcore deploy

The exported agent is added as a normal runtime, so it deploys alongside the rest of your AgentCore CLI project.

EXPORT_NOTES.md

After every export, EXPORT_NOTES.md is written listing items requiring manual follow-up. Export writes notes to EXPORT_NOTES.md in the agent directory listing each item, why it needs attention, and exactly what to do. Always read EXPORT_NOTES.md before deploying.

Deployment Options

After exporting, you can deploy your agent to multiple hosting environments:

  • AgentCore Runtime (managed) - Use agentcore deploy to host in the AgentCore Runtime, which manages infrastructure, scaling, and credential provisioning automatically.

  • Self-hosted - Deploy the generated Python agent anywhere that supports Python 3.12+:

    • AWS services: Lambda, ECS/Fargate, EC2

    • Kubernetes clusters

    • On-premise servers

    • Any containerized environment