Connecting OpenClaw with Make: A Practical Guide | Cody

Make is where a lot of cross-tool automation ends up living: lead routing, CRM syncs, approval flows, alerts, data cleanup, and complicated multi-step operational handoffs. A Make AI assistant is most useful when it helps teams inspect scenarios, understand which automation owns a workflow, review failed or noisy executions, and trigger approved flows from Slack without opening every scenario map by hand. Make's API is stronger than many teams realise, and its newer AI Agents API adds another layer of automations worth monitoring carefully.

How OpenClaw Integrations Work

OpenClaw is a self-hosted AI assistant that runs on your own server — typically an EC2 instance — and connects to Slack. It uses Claude under the hood to process requests. Out of the box, OpenClaw doesn't ship with pre-built connections to third-party tools. Instead, integrations are built using the skills system: markdown files in ~/.openclaw/skills/ that give Claude instructions for a particular domain, combined with HTTP tool calls to any API you expose to it.

In practice, adding a real integration means: getting API credentials from the third-party service, building or configuring a small proxy/endpoint that OpenClaw can call, and writing a skill file that tells Claude how to use it. For some tools this is an afternoon of work. For others — like Make — it's considerably more involved.

Connecting OpenClaw with Make: Step by Step

Step 1: Decide Which Scenarios and Agents Should Be Visible

Before wiring anything up, decide which Make scenarios people actually ask about in Slack. Good starting points are lead routing, CRM syncs, handoff workflows, spreadsheet updates, approval chains, and alerting scenarios that already create operational confusion when they fail. If your team is using Make AI Agents, decide whether Cody should only inspect them or also help trigger approved agent runs.

Step 2: Expose Safe Scenario Monitoring and Trigger Paths

Use the Make API to list scenarios, inspect whether they are active or paused, review execution history, and pull the details people need to debug a broken flow. For actions, keep things narrow: expose approved webhook or proxy-based trigger paths rather than every scenario control. Make's webhook-driven work is asynchronous, so Cody should confirm what it launched and tell the team what callback, log, or follow-up check to expect rather than pretending the workflow already finished.

Step 3: Write the Skill File Around Real Scenario Names, Inputs, and Failure Modes

Write ~/.openclaw/skills/make.md with your real scenario names, what each one does, which inputs or payload fields matter, which flows are safe to trigger, and the failure patterns the team keeps hitting. The assistant becomes much more useful when it can answer questions like "which Make scenario owns this handoff" or "why did this scenario pause" instead of dumping raw module or execution data into chat.

Challenges and Caveats

Make Has Multiple API Regions

Make's API is not just eu1.make.com and us1.make.com. The public docs now list multiple production zones, including eu1, eu2, us1, and us2. If your proxy points at the wrong region, the failure can look like a bad token or missing scenario rather than an obvious routing mistake.

Webhook and Scenario Runs Are Asynchronous

A webhook trigger tells you the scenario accepted the payload, not that the whole automation finished successfully. Cody can confirm what was sent and help inspect later execution history, but it should not treat Make like a synchronous request-response API unless you have built an explicit callback or completion check.

Scenario Maps Get Complex Faster Than Teams Expect

Make is powerful because one scenario can fan out into routers, filters, iterators, retries, and multiple downstream systems. That also makes it easy to overwhelm people with raw execution detail. Your proxy and skill should summarise the trigger, critical path, fragile modules, and likely failure points, otherwise the assistant just recreates scenario-map sprawl in Slack.

The AI Agents API Is Newer Than the Rest of the Platform

Make now exposes AI Agents endpoints in open beta. That is useful because it gives Cody a concrete surface for agent inspection and controlled triggering, but it also means the product area is still moving. Treat AI-agent actions more cautiously than read-heavy scenario monitoring, and expect the API surface to evolve.

Skip All of This — Use Cody Instead

Cody gives your team a Make AI assistant in Slack, so people can inspect scenarios, explain failures, watch automation risk, and trigger approved workflows without living inside scenario maps, execution history, and webhook setup screens all day.

Get started with Cody →

Related Guides

Need the model-flexible version? See: How to Connect Make to OpenClaw: Setup, Models, and Workflow Guide.