How to create an agent harness

Key takeaways

• Most teams should configure a harness, not write one from zero.
• Three paths: managed product setup, open harness deploy, minimal DIY loop.
• Start with one workflow, explicit tools, and a human approval gate.
• Building a production harness is an infrastructure project (memory, sandbox, auth, evals), not a weekend prompt.

Table of contents

1. Pick a path
2. Path A: Configure a managed harness
3. Path B: Deploy an open harness
4. Path C: Minimal DIY loop
5. Checklist before you call it done
6. FAQ

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Pick a path

Path	When it fits	Starting point
Configure managed	You want results this week	Claude Cowork, ChatGPT agent, Grok Bot
Deploy open	You need ownership, messaging, or local models	Hermes, OpenClaw
DIY minimal	You are learning or embedding a tiny agent in a product	Model API + tool loop + logging

If your goal is SME ops (booking, renewals, inbox), read How to set up an AI agent for SMEs first. That guide is workflow-first; this one is harness-first.

Path A: Configure a managed harness

How to create an agent harness experience without writing runtime code:

1. Choose the product that matches the job (docs → Cowork; web chores → ChatGPT agent; multi-bot overnight → Grok Bot).
2. Create one specialist identity with a one-paragraph charter.
3. Connect the fewest tools that can finish the job.
4. Set approvals for sends, spends, and irreversible actions.
5. Run a supervised pilot on real tasks for two weeks.
6. Add memory or schedules only after the happy path works.

You are creating an agent on a harness, which is what most buyers mean.

Path B: Deploy an open harness

1. Pick Hermes or OpenClaw (memory/skills vs gateway/channels). See examples.
2. Provision a machine (laptop for experiments; VPS for always-on).
3. Wire model credentials (API and/or local).
4. Attach channels you will actually use (Telegram, WhatsApp, Slack, CLI).
5. Define tools and MCP servers with least privilege.
6. Persist memory and backups before you trust it overnight.
7. Add a kill switch and log review.

This is the closest literal answer to "create an agent harness" without inventing one.

Path C: Minimal DIY loop

For learning or embedding:

1. Call a chat model with tools enabled.
2. Execute tool calls in your process.
3. Feed results back until a stop condition.
4. Add a workspace directory and a transcript store.
5. Add auth scopes and human confirmation for dangerous tools.
6. Only then consider multi-agent orchestration.

Skip reinventing memory and messaging unless that is your product. Prefer Hermes/OpenClaw patterns or a managed API.

Checklist before you call it done

• One named job and stop condition
• Explicit allow/deny tool list
• Durable logs
• Human escalation path
• Cost ceiling
• Eval set of 10 real tasks you can re-run