A Sharp OpenClaw ACP Setup for Codex, Claude Code, and Gemini CLI

ACP is useful when coding agents need continuity instead of starting cold for every command. By the end, you'll know how to run CLI coding agents through OpenClaw without losing context or control. The short answer: use OpenClaw where a persistent agent needs context, tool access, and clear operating rules; keep brittle, high-risk, or one-off work closer to a human until the workflow proves itself.

The short answer

• use ACP for persistent coding sessions, explicit workspace rules, and tool-specific handoffs between Codex, Claude Code, and Gemini CLI.
• Write boundaries before connecting powerful tools.
• Use concrete integrations where the work already happens.
• Keep a human approval point for risky steps.

A good agent setup is not magic. It is a small operating system: the right instructions, the right tools, the right memory, and a clear boundary for when the agent should stop and ask.

Use the same standard you would use for a capable assistant. If the task has a known trigger, a known source of truth, and a known finish line, it is a candidate for automation. If the task depends on taste, politics, or unclear authority, make the agent prepare options and let a person decide.

Where it helps first

The best first use case is narrow, repeated, and already painful. Do not begin with a full-company transformation. Begin with one workflow where the input and output are visible.

Example: use OpenClaw ACP to connect a Fireflies meeting summary to a Linear issue, then post the decision in Slack or Telegram.

Apply the rule this way: pick the bottleneck that repeats every week and has a clear owner. The useful test is whether the agent can explain what it did, which source it used, and what it still needs from a person. If it cannot do that, the workflow is not ready to run quietly in the background.

What to keep human

Agents are strongest at gathering, drafting, summarizing, routing, and checking. Humans should keep control over promises, deletes, payments, production changes, and sensitive customer messages.

Example: let the agent draft a Gmail follow-up and attach context, but require approval before it sends.

Apply the rule this way: automate preparation and routing before automating final authority. This is where concrete integrations matter. Slack is good for shared visibility, Telegram is good for quick private approval, Gmail is good for drafts and follow-ups, Linear is good for owned work, and Fireflies is good for converting meetings into decisions.

Common mistakes that create extra work

The usual mistake is adding a tool before defining the operating rule. Another is connecting every integration on day one, which makes failures harder to diagnose.

Example: if Slack alerts, Telegram alerts, and email notifications all fire for the same event, the agent saved no time.

Apply the rule this way: start with one integration path and add the second only after the first is trusted. Write that boundary into the agent's instructions before the next run. Most reliability gains come from these small corrections, not from switching models or adding more tools.

A practical first step

1. Write the workflow in one sentence.
2. Name the trigger, data source, output, and approval owner.
3. Connect one integration first.
4. Review failures after one week and tighten the rules.

Keep the first version boring. One channel, one workflow, one human owner, and one visible success metric beats a sprawling assistant nobody trusts.

Recap

• Narrow workflows beat broad demos.
• Concrete integrations reveal real friction.
• Human approval protects risky steps.
• Expansion should follow evidence, not enthusiasm.

Next step

For the next practical layer, read: /blog/agentic-context-engineering-how-to-give-ai-agents-better-working-context.