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How to build a custom harness tuned to your product workflow with Harnessrouter

Create a harness with its own instructions, tools, MCP server and skills, then test it before use.

Who it is forA product developer who needs an agent to behave a specific way for a recurring workflow in their app.

The problemA built-in harness does not know your product rules, so each task needs the same instructions repeated.

The outcomeA reusable custom harness that you call by its harness ID for your product tasks.

What you need first

  • Docker installed
  • About 4 GB of free disk space
  • A provider API key for at least one supported model provider
  • Python 3 if you use the Idempotency-Key example

Steps

  1. Open Agent harnesses and select New harness
  2. In Add harness, set the Name, Base harness, and Default model, then select Create and configure
  3. In Harness Settings, add Agent instructions, configure Tools (optionally Add MCP), and add Skills
  4. Select Save Changes
  5. Select Run Task to test the saved configuration

Prompt for your agent

Using the HarnessRouter repo at https://github.com/HarnessRouter/harnessrouter, create a custom harness named <harness-name> on a supported base harness and add agent instructions for <workflow>. Expected output: a saved harness that completes a test task. Verify that the test run finishes before reporting the harness as ready.

Caveat. The base harness cannot be changed after creation, so pick it carefully before you save.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for HarnessRouter/harnessrouter and "Build a custom harness tuned to your product workflow"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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