The outcome

Operate an agentic workflow whose inputs, actions, approvals, and failures remain observable and recoverable.

Make is a visual automation and AI-agent orchestration platform. Building visual automations and bounded AI agents that combine triggers, structured scenarios, knowledge, and tools across business applications. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Select the trigger and define accepted senders, payload schema, rate limit, sensitive fields, duplicate key, and expected response time.

  2. 02

    Filter malformed, unauthorized, bot-generated, or repeated events before the Run an agent module and map only the fields the task requires.

  3. 03

    Require structured agent output and route proposed external actions through validation, policy checks, and human approval when impact is meaningful.

  4. 04

    Add bounded retries, error handlers, idempotency, timeouts, and an alert path so a provider or tool failure cannot repeat an action silently.

  5. 05

    Run shadow tests, review scenario history and credit use, redact retained payloads, then publish gradually with an owner and rollback switch.

Working standard

What good use looks like.

  • Filter before invoking the model.
  • Separate proposals from consequential actions.
  • Make every retry idempotent.

Make AI Agent (New) is documented as an open beta and agent outputs are unpredictable. Limit data and connection permissions, distinguish deterministic automation from agentic judgment, validate tool inputs and outputs, require approval for consequential actions, prevent loops and duplicate retries, and monitor retained scenario data and credit use.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.