Start with the job, not the trigger
An automation trigger decides when a workflow begins. The AI step might classify a support request, summarize a document or draft a reply, but it cannot do useful work until the right input arrives. Before choosing a trigger, write down what counts as a new task, how soon it needs attention and whether a person must approve the result.
Consider a shared inbox. A request for an immediate draft may benefit from a workflow that starts when a message arrives. A daily digest can wait for a set time. If the inbox system cannot notify other tools about new messages, the workflow may need to check periodically instead. These are different ways to start the same AI task; none makes the AI’s output inherently more accurate.
Use webhooks when the source can announce an event
A webhook is a message one service sends to another when something happens, such as a form submission or an updated record. It is a good fit when a source system supports event notifications and the workflow should begin promptly. The receiving endpoint can check the event, place a task in a queue and return a quick acknowledgment. The slower AI work can then happen separately, without making the source wait for a generated response.
Webhooks need care because deliveries may be repeated, delayed or arrive out of order. Give each event a stable identifier and record whether it has already been handled, so a retry does not send two replies or create two records. Verify that incoming requests are authorized using the source’s available signing or authentication method. Accept only the events the workflow needs, and avoid passing unnecessary personal information into prompts.
Use polling when notifications are unavailable
Polling means asking a system at intervals whether anything has changed. It works when an app offers a way to list or search records but cannot send a suitable webhook. A polling workflow might look for documents added since its last successful check, then send each new document to an AI categorization step. The interval is a trade-off: frequent checks reduce waiting time but create more requests and processing overhead.
A reliable poller remembers its progress, such as the last processed record identifier or update time. It should also handle records that change while a check is running. If the source permits it, checking a small overlapping window and deduplicating by record identifier can help avoid missed work. Respect the source system’s request limits, and decide what should happen when a check fails: retry later rather than silently moving the progress marker forward.
Use schedules for work that has a natural cadence
A schedule starts a workflow at a chosen time or interval, whether or not a new event has arrived. This suits a weekly summary, a morning review queue or a periodic check for records that need follow-up. It can also make AI usage easier to plan by collecting eligible items into a batch. The cost is that an item may wait until the next run, so a schedule is a poor fit when prompt action matters.
Define the time window for each run clearly. For a daily digest, specify which records belong to that day, which time zone applies and what happens if a run is missed. Prevent two runs from working on the same batch at once, and store a record of completed batches. A scheduled job can also serve as a backup check for a webhook-based workflow, provided both paths use the same duplicate-handling rules.
Make the workflow safe to rerun
Whichever trigger you choose, separate detecting work from acting on it. Keep a small record of the source item, its processing status and the resulting action. That makes it easier to retry an AI call after a temporary failure without repeating an external action such as emailing a customer. Set a rule for uncertain outputs: a classification can go to a review queue, while a draft can remain unsent until a person approves it.
For a first version, choose the simplest trigger that meets the timing requirement. Test a normal item, a duplicate, a failed run and an item that should not be processed. Check what data reaches the AI service and who can see the output. Once those paths are understandable, adding faster triggers or more frequent runs is usually easier than untangling an automation whose behavior was never defined.
