What we can build with Make
ShooflyAI builds on Make when a workflow needs branching logic, loops, and multi-step routing across many apps, using its visual scenarios to wire AI agents into the exact path your process follows. The scenarios live in your account, so you own and can edit every step.
These are example builds, not client case studies. We scope the real build to your stack and put an ROI estimate on it in an Operating Assessment first.
With Make and AI agents you can run reasoning inside visual scenarios that branch, loop, and call hundreds of apps in a single flow: Make hands data to an agent, the agent decides or extracts, and routers send the result down the right path. Make's own next-generation AI Agents now live in the Scenario Builder with multimodal file handling and a reasoning panel that shows each step. ShooflyAI builds custom agents wired into your Make scenarios, designed around the exact decision tree your process follows, with a human approving the steps that matter. The point is ownership: the scenarios live in your account and your company keeps the code, prompts, data, and IP, rather than renting the whole stack from a vendor.
10 ways to use Make with AI agents
- Parse documents into structured dataA scenario ingests files and an agent extracts fields routed to your database modules.
- Branch workflows on agent decisionsMake routers split the scenario based on how the agent classifies each incoming item.
- Enrich and route records in one flowThe agent enriches a record, then Make routes it to the right system based on the result.
- Process attachments at scaleIterators loop through attachments and the agent summarizes or extracts data from each.
- Draft and post content updatesAn agent generates copy and Make publishes it across connected content and social modules.
- Reconcile data between systemsThe agent resolves mismatches as Make syncs records across CRMs, sheets, and databases.
- Run multi-step approval flowsMake pauses on agent output and routes approvals before completing downstream actions.
- Transform and validate inbound dataThe agent checks and reformats data mid-scenario before Make writes it to your systems.
- Aggregate insights from many sourcesMake collects data from several modules and the agent synthesizes a single summary.
- Trigger agents on schedules or webhooksScheduled or webhook scenarios kick off agent runs and fan results out to your apps.
Using Make on its own vs. a custom ShooflyAI agent
| Dimension | Make AI Agents | Custom ShooflyAI agent |
|---|---|---|
| Setup | Visual builder with agent templates and module tools | Scoped build mapped to your real process, longer up front |
| Handles your multi-step workflows | Strong branching and loops within the Make canvas | Custom reasoning and memory across multi-stage flows |
| Works across your other systems | Connects 3,000+ apps through prebuilt modules | Those apps plus your internal APIs and databases |
| Who owns and maintains it | Scenarios live in your Make account on their platform | You own the code, prompts, and IP outright |
| Cost model | Operations-based pricing that scales with run volume | Build cost, then you run it on infrastructure you control |
Frequently asked questions
Can I use AI agents with Make?
Yes. Make scenarios can call an AI agent as a module, pass it structured data, and branch on its response. Make excels at multi-step flows where the agent is one reasoning step among many app actions.
How is Make different from Zapier for AI agents?
Make uses a visual canvas with richer branching, loops, and data manipulation, which suits complex agent workflows. Both connect agents to apps, but Make gives more granular control over each step.
Do I own the agent ShooflyAI builds for Make?
Yes. You own the code, prompts, data, and model setup. The agent runs in your environment and Make orchestrates when and how it is called within your scenarios.
Can a Make scenario chain several agent calls together?
Yes. Make can route an agent's output into another module or a second agent, with filters and routers in between, enabling multi-stage reasoning inside one scenario.
What tasks fit a Make AI agent best?
Multi-step jobs like enriching then routing records, parsing documents and updating systems, or running a decision tree across several apps benefit most from Make's branching control.
Want to see what we would build for you?
We start with an Operating Assessment that maps your highest-value workflows and puts a hard ROI estimate on them before any build. You own the code, the data, and the IP.
Get your Operating Assessment →