The most reliable method is an agent log — a record written to your database or a file after every run, showing the status, what was produced, and any errors. Without logging, you are guessing whether the run happened at all.
Why “It Probably Ran” Is Not Good Enough
Scheduled agents fail silently more often than you expect. A data source becomes temporarily unavailable. A rate limit is hit. An MCP connection times out. An unexpected input format causes an error mid-run. In all of these cases, the agent stops without finishing — and without a log, you have no way of knowing it happened until you notice the output is missing or wrong. By then, a week of failed runs might have accumulated.
Logging is the difference between a professional automated system and a fragile one. It does not need to be complex. The simplest useful log is a single database row written at the end of every run containing a timestamp, a status (success, partial, error), and a summary of what was produced. That record lets you verify in seconds that the run happened and completed as expected.
Three Ways to Monitor Scheduled Agent Runs
The first and most direct approach is an agent log table in your WordPress database. The TrainingSites system uses a wp_agent_logs table where every skill writes a completion record after running. You can query that table any time to see when each agent last ran, what it produced, and whether any errors occurred. This is the most integrated approach and the one that scales best across many agents.
The second approach is output verification. Rather than checking a log, you check whether the expected output exists. Did the community post appear? Is the draft newsletter in FluentCRM? Did the articles get published? This is a manual check but still useful — especially during the trust-building period when you are reviewing outputs anyway.
The third approach is a summary notification. The agent sends itself a brief status message at the end of each run — either a community post visible only to you, an email summary, or a file updated with the run timestamp. This approach adds a self-reporting layer without requiring you to check a separate log system.
What This Means for Educators
Build logging into every skill you schedule. It takes five minutes to add a log entry at the end of a skill, and it saves hours of debugging when something eventually goes wrong — because something always eventually goes wrong.
The Simple Rule
If there is no log, you do not know if it ran. Write the log entry as the last step of every skill. No exceptions for production agents.