One of the more interesting features I recently built into a legal software application was support for event tolling—a process used to pause countdown timers in legal workflows when certain conditions are met. On paper, it’s just a date adjustment. In reality, it’s a complex, high-stakes chain of logic that affects deadlines, rights, and outcomes.

But what I didn’t expect was how much this feature would deepen my thinking about data integrity, automation, and design.

The Original Flaw: Manual Recalculation

In the original design, the process went something like this:

1. A user added a qualifying event that should pause a legal deadline.
2. The event was saved.
3. A separate method existed to update the related deadline—but it had to be triggered manually.

This worked fine in the short term… until I asked myself:

“What happens if I forget to call that update method somewhere down the line?”

The answer, of course, was data drift—calculated deadlines becoming inaccurate because the update logic wasn’t enforced. That’s not just a technical bug. In a legal context, it’s a potential compliance issue.

The Fix: Automate or Fail

The fix was obvious but essential: embed the recalculation directly into the method that adds the tolling event. From now on, when a tolling event is saved, it automatically triggers a deadline update on the related record.

I updated the repository layer, added the dependency injection needed, and adjusted the interface. Then I wrote a fresh unit test to prove the whole thing worked:

• Create a test record
• Apply a tolling event that pauses time for 10 days
• Verify that the new deadline reflects that 10-day pause
• Assert the math checks out

The test passed, the design got cleaner, and I slept better.

System Integrity Starts with Enforcement

Here’s the broader principle I took away from all of this:

If your application depends on humans remembering to do something critical, your system is already broken.

If it’s vital—whether for financials, compliance, workflow timing, or audit trail reasons—then it must be automated, not optional.

One More Thing: Make a Default System User

There was one last loose end. The update logic requires a userId to track who made the change. In this case, since the system itself was making the change, I needed a clean solution.

So I created a default system user (User ID = 1). Every automated update can now be attributed to that account, preserving accountability and audit trail consistency without faking human involvement.

It’s a small detail, but in long-lived software, these little decisions pay dividends later.

Final Thoughts

Event tolling is a complex concept—especially in legal tech—but the biggest lesson wasn’t about business logic. It was about defensive architecture.

If your system supports cascading updates, interdependent data, or audit-sensitive workflows, don’t rely on hope or habit. Enforce the outcome. Automate the linkage. Test the entire pipeline.

Because the moment you forget to update one field, someone else might notice. And they won’t assume it’s just a minor bug.

About the Author

My name is Paul A. Jones Jr., and I am a software engineer and legal tech founder developing tools for professionals in law and other regulated industries. I write about systems thinking, modern workflows, and SaaS applications at PaulJonesSoftware.com. Follow me on Twitter: @PaulAJonesJr.