Beyond successful execution
How do you monitor an automation’s outcome?
Automation outcome monitoring verifies the useful result after execution. An existing runner reports REST Run status and metadata to Glnc, where latest-run or windowed rules evaluate the evidence. Status history, acknowledgment, and optional remediation add response context. Glnc supplements the runner; it does not execute or schedule the automation.
How is an outcome different from execution?
Execution evidence answers whether a process started, completed, failed, or timed out. Outcome evidence answers whether that execution produced the result people depend on. A completed sync, for example, can still report fewer records than its minimum expectation.
Glnc supplements the automation platform. It receives reported evidence; it is not the automation’s executor, scheduler, or host.
Which outcomes can Glnc evaluate?
Output rules compare fields in Run metadata with ==, !=, >, >=, <, or <=. Windowed rules can evaluate values across a trailing period instead of judging only the newest Run.
The useful field depends on the automation: a record count, an artifact flag, or another value the runner can report without including secrets.
How does an automation report evidence?
The runner sends a full-access bearer API key and no X-Entity header to the account-level monitoring endpoint. It can report a terminal status in one request or report started and complete that Run later.
What happens when an outcome fails?
A failed output rule contributes a warning or failing status and a reason tied to the reported field. Operators can acknowledge an incident without changing it to healthy. If a remediation queue and stage are configured, the Check can also create a remediation work item.
Where does outcome monitoring fit in the stack?
Keep the existing scheduler, workflow platform, or agent. Add Glnc after execution as an account-level record of when work was expected, what the runner reported, whether the result met its rule, and what context a responder needs.
Next step
Define one consequential expectation.
Read the API contract before reporting production evidence, or join the private beta to start with one recurring automation.