AI Agent Setup Checklist

Choose a primary harness: OpenClaw for persistent operations, Claude Code for coding-heavy workflows, or Hermes for your preferred agent environment.

The best first harness depends on whether you need messaging surfaces, coding depth, or a broader autonomous workflow environment.

Define one narrow first job the agent should do well.

Examples: weekly content research, repo maintenance, lead enrichment, or FAQ drafting.

Decide what the agent can do autonomously and what always requires human review.

This is one of the highest-leverage setup decisions you can make.

Write down the inputs, expected outputs, and where outputs should be saved or delivered.

Clear I/O design makes agent behavior much easier to debug.

Define what a successful run looks like before the first live workflow begins.

If success is vague, you will not know whether the agent is helping or hallucinating.

Confirm model access, budget constraints, and fallback choices.

Different workflows may need different models for cost, speed, or reasoning quality.

Enable only the tools needed for the first workflow, such as browser access, file access, or shell execution.

Over-provisioned agents are harder to trust and harder to contain.

Create a clean workspace or repo structure for prompts, outputs, notes, and logs.

Messy workspaces produce messy autonomous behavior.

Store credentials securely and keep secrets out of prompts, code, and repos.

If the harness uses environment variables or secrets managers, set them up properly from the start.

Set up the surface where the agent should report results, such as local files, Telegram, Discord, or repo commits.

Useful agents need a reliable output destination, not just a terminal window.

Create clear operating instructions, preferred workflows, and boundaries in your workspace guidance files.

This gives the harness repeatable context instead of relying on fragile chat history alone.

Set explicit approval rules for destructive commands, public actions, or third-party communications.

Your future self will be glad you did this before the first bad automation incident.

Decide what the agent should remember between sessions and where that memory should live.

Persistent notes and curated memory prevent the same mistakes from repeating.

Make sure runs leave behind logs, files, or commits you can inspect later.

Debuggability is a feature, not a luxury.

Pick a sensible sandbox or runtime posture based on the task risk level.

Read-only and low-risk tasks should not need the same permissions as deployment tasks.

Run the workflow in dry-run mode or on a non-critical target first.

You want proof the workflow behaves sensibly before letting it touch real assets.

Review the first few outputs manually and correct weak spots in prompts, tools, or memory.

Early supervision compounds into much better long-term performance.

Decide whether the workflow should run on demand, on a cron schedule, or through heartbeat-style monitoring.

The right trigger model is part of the product design of the workflow.

Track one or two real business metrics tied to the workflow.

Hours saved, tasks shipped, qualified leads enriched, or content published are better than vague enthusiasm.

Only after one workflow works reliably, choose the second workflow to layer in.

Stacking unstable automations is the fastest way to lose confidence in the whole system.