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How to tune a marketing agent on AutomationBench tasks with Gear

Run one Gear optimization round on AutomationBench Marketing tasks to improve an agent's pass rate.

Short answer: Run npm install --global rsi-gear@latest agent-hitch@latest @deepseek-ai/dsh@latest to install Gear, the Hitch evaluation runner, and DSH; then run hitch eval setup harbor to set up the Harbor evaluation runner; install the bundled Refine Skill in your agent environment by following the quick start guide, then configure AutomationBench as the benchmark. All steps.

Who it is forA marketing operations lead who wants an agent to handle Marketing automation tasks more reliably and has no benchmark of their own yet.

The problemA general agent setup completes only part of the Marketing tasks, and tuning instructions and tools by hand is slow and hard to measure.

The outcomeA proposed round of harness changes, evaluated on the Marketing tasks, with each harness version and its results recorded so you can inspect and reuse the best one.

What you need first

  • npm installed, to run the global install of rsi-gear, agent-hitch, and DSH
  • An agent environment such as Codex, Claude Code, or DSH that can use the bundled Refine Skill
  • A benchmark of representative tasks with clear success criteria, either an existing one or your own in Harbor format
  • Access to the models you choose for the Meta agent and the task agent

Steps

  1. Run npm install --global rsi-gear@latest agent-hitch@latest @deepseek-ai/dsh@latest to install Gear, the Hitch evaluation runner, and DSH.
  2. Run hitch eval setup harbor to set up the Harbor evaluation runner.
  3. Install the bundled Refine Skill in your agent environment by following the quick start guide, then configure AutomationBench as the benchmark.
  4. In your agent session, invoke /refine or submit a request to improve performance on AutomationBench's Marketing tasks and run one round of optimization.
  5. Review the recorded harness versions and their evaluation results, then keep the version that scores best for your use.

Prompt for your agent

Use the Gear repo at rsi-gear/gear to run one Refine Skill optimization round on AutomationBench's Marketing tasks for <target agent setup>. Report the pass rate before and after the round and the path to the recorded harness versions. Verify that each retained harness version has a matching evaluation result before you report success.

Caveat. Gear optimizes the harness only; the model training loop is experimental, so this use case changes instructions, tools, and workflows and does not retrain the model.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for rsi-gear/gear and "Tune a marketing agent on AutomationBench tasks"

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