What it does
Make is a visual workflow automation platform, formerly known as Integromat. It lets users build multi-step “scenarios” that move data between SaaS tools, trigger actions from events, transform records, and call external services through APIs. Its canvas-based builder is designed to make automation logic easier to inspect than a long list of linear steps.
For AI-agent work, Make can serve as the operational layer around a model or agent: receive a form submission or support ticket, send relevant context to an AI service, route the result for review, update a CRM or database, and notify a person or another system. It is especially useful when an agent needs dependable connections to the rest of a business stack rather than just a chat interface.
Who it's for
Make suits solo founders and small teams that use several web tools and want to automate repetitive handoffs without building every integration from scratch. It is a strong candidate for operators who are comfortable thinking in triggers, branches, mappings, and error paths—and who want more control than a simple one-trigger, one-action automation.
It can also work well for technical founders prototyping AI-enabled operations before committing engineering time to custom infrastructure. Complex scenarios can become sophisticated quickly, so it is less ideal for someone who wants fully autonomous “employees” with no workflow design or ongoing monitoring.
Why founders may try it
- Build lead routing, enrichment, follow-up, reporting, and back-office workflows across connected apps.
- Use visual branching and data transformation to put guardrails around AI outputs.
- Start with prebuilt integrations, then extend workflows with webhooks and APIs when needed.
- Keep humans in the loop for approvals, exceptions, and sensitive customer-facing actions.
Make is best viewed as flexible automation infrastructure for AI workflows, not a replacement for defining the process, permissions, and failure handling behind an agent.
Consider before adopting
Scenario design requires some systems thinking. Usage can depend on how often workflows run and how many operations they perform, so estimate volume before putting high-frequency processes into production. Test duplicate prevention, retries, data privacy, and fallback paths carefully—particularly where AI-generated content can change customer records or send messages.