Switch from Make to an AI Agent
Make automates data flows. An AI agent automates conversations and decisions. Combine both for UAE business workflows that actually think.
Make (Integromat) vs OpenClaw AI Agent
Make (Integromat) limitations
- Scenarios are visual automation chains, no conversational or reasoning capability
- Operations-based pricing penalizes complex scenarios with many modules
- Error handling is manual: you define what happens on each failure type
- No WhatsApp Business API integration for customer conversations
- Cannot handle unstructured inputs like free-text, images, or voice
- Complex scenarios with routers and iterators become hard to maintain
OpenClaw advantages
- Adds reasoning and conversation to your automation workflows
- WhatsApp Business API as a full communication channel, not just a notification hook
- Handles ambiguity and exceptions through reasoning, not predefined error paths
- Flat pricing instead of per-operation charges
- Processes customer messages, images, and documents as workflow inputs
- Non-technical team members can update behaviour without learning the visual builder
Migration steps
-
Map active scenarios
Document all active Make scenarios, their modules, routers, and error handlers. Note data transformations and filters.
2-3 hours -
Identify agent-ready workflows
Separate scenarios into two groups: those that benefit from AI (customer-facing, decision-heavy) and those that remain as automations.
1-2 hours -
Build agent workflows
Convert identified scenarios into AI agent behaviour. Complex routers and filters become reasoning-based decisions.
3-5 hours -
Test scenario by scenario
Verify each migrated workflow end-to-end. Compare outputs against original Make scenario results.
2-3 hours -
Launch and transition
Activate agent workflows and pause corresponding Make scenarios. Monitor during 14-day launch care.
14 days
Data portability: Make scenarios can be exported as JSON blueprints. We use these to understand your automation logic and rebuild customer-facing workflows as AI agent behaviour, while keeping simple data sync scenarios on Make if preferred.
Reasoning Engine
Make scenarios follow fixed paths. The AI agent evaluates context and makes decisions, handling the exceptions that break rigid automation.
Conversational Trigger
Workflows start from customer messages on WhatsApp, not just scheduled triggers or webhook events.
Dynamic Routing
No more complex router configurations. The agent routes based on conversation context and intent, adapting in real time.
No Operation Counting
Make bills per operation. A scenario with 10 modules running 500 times costs 5,000 operations. The AI agent has flat pricing.
Arabic-English Processing
Process Arabic customer inputs, documents, and messages. Make modules have no language awareness.
Graceful Error Handling
When something goes wrong, the agent adapts and communicates. No silent failures or generic error handler notifications.
FAQ
Can I keep some scenarios on Make?
Yes. Simple data sync scenarios (CRM to spreadsheet, form to database) can stay on Make. The AI agent replaces scenarios that involve customer interaction or complex decision-making.
We have complex scenarios with iterators and aggregators. Can those migrate?
Yes. Complex data transformation scenarios translate to agent workflows. The agent handles iteration and aggregation through reasoning rather than visual modules.
What about Make webhooks that other systems call?
The AI agent can receive webhooks from external systems. Your existing integrations continue to work with a new endpoint.
How does this affect our Make subscription?
You can reduce your Make plan based on which scenarios you migrate. Many businesses keep a basic Make plan for simple syncs and use the AI agent for everything customer-facing.
Ready to make your automations smarter?
Book a session and add intelligence to your workflows.