Setting up logging before your agent does anything real is one of the highest-leverage decisions you can make. A simple five-field log table — skill name, timestamp, status, input summary, output summary — gives you the oversight foundation everything else builds on.
Why Day One Matters
Logging is almost always added retroactively, after something goes wrong. By then, you’ve already lost the history that would have told you whether this was the first time the problem occurred or the tenth. Setting up logging before your agent runs means you have a baseline from the very beginning — which makes anomalies detectable because you know what normal looks like.
It also builds a habit. Agents that are logged from day one tend to stay logged. Agents that are built without logging tend to run for months before someone realizes there’s no record of what they’ve been doing.
The Minimum Viable Log Structure
You don’t need a sophisticated observability platform to start. Five fields in a database table or a simple log file are enough: timestamp (when did the run happen?), skill or agent name (which agent was it?), status (success, error, or partial?), input summary (a plain-English description of what the agent was given), and output summary (a plain-English description of what it produced or did). In the Claude Cowork system, this maps directly to the wp_agent_logs table, which every skill is designed to write to automatically after each run.
Add two more fields when you’re ready: duration (how long did the run take?) and time saved estimate (how many minutes of manual work did this replace?). These turn your log from a debugging tool into a ROI tracker — which makes it much easier to justify expanding your agent setup over time.
What This Means for Educators
For coaches and consultants running campus automation, an activity log is how you answer the question “Is this actually working?” with evidence rather than assumption. After 30 days of logging, you’ll know exactly how many successful runs each agent completed, where failures concentrated, and what the automation was worth in time saved.
The Simple Rule
Create the log table before the agent runs. Add a logging step to every agent skill file. Check the log before you check the output — the log tells you whether the output is trustworthy.