Testing, Monitoring, and Improving Automations

The Automation That Worked Perfectly (Until It Didn’t)

In the previous lesson, we explored error handling and edge cases. Now let’s build on that foundation. You built your automation. It ran flawlessly in testing. You deployed it on Monday. By Wednesday, everything was still great. By the second month, you’d forgotten about it entirely – it just worked.

Then, four months later: the data source changed its API format. Your automation silently started dropping 30% of records. Nobody noticed for two weeks.

Testing, monitoring, and continuous improvement aren’t the glamorous parts of automation. But they’re the difference between an automation that works today and one that works reliably for years.

What You’ll Learn

By the end of this lesson, you’ll test automations systematically before deployment, set up monitoring to catch problems early, optimize performance over time, and maintain automations as your business and tools evolve.

From Building to Operating

Lessons 3-6 covered designing, building, and error-proofing automations. This lesson covers everything that happens after: testing, deploying, monitoring, and improving. Design is the beginning of an automation’s lifecycle, not the end.

Testing Before Deployment

Testing Levels

Think of testing as a pyramid with three levels:

Level 1: Step testing (test each step individually)

For each step in your automation:

• Does it accept the expected input?
• Does it produce the expected output?
• Does it handle invalid input gracefully?
• Does the error handling work?

Test plan for Step 3: Create customer record

Test 1: Valid input → Expect: record created, ID returned
Test 2: Missing email field → Expect: validation error, logged
Test 3: Duplicate customer → Expect: existing record used, warning logged
Test 4: API unavailable → Expect: retry 3x, then alert admin
Test 5: Special characters in name → Expect: handled correctly

Level 2: Flow testing (test the complete workflow)

Run the entire automation end-to-end with different scenarios:

• Happy path: everything works perfectly
• Error path: a middle step fails, does recovery work?
• Edge case path: unusual but valid data
• Volume path: what happens with high volume?

Level 3: Integration testing (test with real systems)

Connect to actual (or staging) instances of each system and verify:

• Authentication works
• Data formats are correct
• Rate limits aren’t hit
• Permissions are sufficient
• Timing works as expected

Testing with Representative Data

Don’t test with perfect sample data. Create test data that represents real-world messiness:

Create test data for my automation that includes:

1. A "perfect" record (all fields filled correctly)
2. A record with missing optional fields
3. A record with special characters (O'Brien, Garcia-Lopez)
4. A record with maximum-length field values
5. A record with minimum values (empty strings, zero amounts)
6. A record that triggers every condition branch
7. A duplicate of record #1 (test duplicate handling)
8. A record with formatting variations (different date formats, phone formats)

Quick Check

Have you created test cases for every condition branch in your automation? If your automation has an “if/else” that sends VIP customers to one path and regular customers to another, you need at least one test case for each path. Untested paths are broken paths waiting to happen.

Staged Rollout

Don’t deploy to 100% of your target on day one. Use a staged rollout:

Stage 1: Shadow mode (1 week)

• Automation runs but doesn’t take real actions
• Logs what it WOULD do
• You compare against manual process results
• Goal: Verify logic matches expected behavior

Stage 2: Limited deployment (1-2 weeks)

• Run on 5-10% of records (one client, one department, one category)
• Real actions taken, but limited blast radius
• Monitor closely for errors
• Goal: Validate in production conditions

Stage 3: Expanded deployment (1 week)

• Scale to 50% of records
• Continue monitoring
• Address any issues found at 5-10%
• Goal: Confirm performance at scale

Stage 4: Full deployment

• 100% of records
• Monitoring in place
• Error handling validated
• Runbook documented for common issues

Why this works: If your automation has a bug that sends duplicate invoices, it’s much better to send 3 duplicate invoices (5% of 60 clients) than 60. The staged approach limits damage while you’re learning.

Post-Deployment Monitoring

Once your automation is live, you need to know when things go wrong – ideally before users notice.

The Monitoring Dashboard

Track these metrics:

Alert Rules

Set up alerts for:

Immediate alerts (fix now):

• Success rate drops below 90%
• Any step produces a critical error
• Execution time exceeds 5x normal
• Authentication failure (credentials expired)

Daily digest alerts (review today):

• Any errors in the last 24 hours
• Unusual patterns (spike or drop in volume)
• Warning-level issues from error handling

Weekly review (analyze trends):

• Success rate trend over time
• Most common errors
• Performance trends
• Volume trends

AI-Assisted Monitoring

Use AI to analyze your automation logs:

Here are the error logs from my automation this week:

[paste logs]

Analyze:
1. What are the most common errors?
2. Are there patterns (certain times, certain data types)?
3. Which errors are critical vs. informational?
4. What specific fixes would you recommend?
5. Are there any trending issues that might become
   critical if not addressed?

Optimization

Speed Optimization

If your automation is slow:

• Parallelize independent steps. Steps that don’t depend on each other can run simultaneously.
• Batch API calls. Instead of 100 individual API calls, batch them into 10 calls of 10 items.
• Cache repeated lookups. If you look up the same reference data multiple times, cache it.
• Move heavy processing to off-peak hours. Schedule resource-intensive automations during low-traffic times.

Reliability Optimization

If your automation fails too often:

• Add validation before processing. Check data quality at the start, not the middle.
• Improve retry logic. Are you retrying the right failures? Are timeouts appropriate?
• Add circuit breakers. If a dependency is consistently failing, stop hitting it and alert someone.
• Create fallback paths. If the primary method fails, is there an alternative?

Maintenance Optimization

If your automation is hard to maintain:

• Document everything. What it does, why each step exists, what data it uses, who owns it.
• Modularize. Break complex automations into smaller, reusable components.
• Version control. Track changes to your automation over time.
• Create a runbook. Document common issues and their fixes so anyone on your team can troubleshoot.

Quick Check

When was the last time you reviewed an existing automation? If it’s been more than a month, it’s time. Check the error logs, review performance metrics, and verify that the business process it supports hasn’t changed.

The Automation Runbook

Create a runbook for each automation:

Create a runbook for my [automation name] automation.

Include:

1. Overview
   - What it does (1-2 sentences)
   - When it runs
   - What systems it connects

2. Common issues and fixes
   - [Issue 1]: How to diagnose, how to fix
   - [Issue 2]: How to diagnose, how to fix
   - [Issue 3]: How to diagnose, how to fix

3. How to pause/restart
   - Emergency stop procedure
   - How to restart after fixing an issue
   - How to reprocess failed records

4. Escalation
   - Who to contact for different issue types
   - When to escalate vs. fix independently

5. Maintenance schedule
   - What to check monthly
   - What to update quarterly
   - Annual review items

The Continuous Improvement Cycle

Automations aren’t “set and forget.” Build a regular improvement cycle:

Monthly:

• Review error logs and address recurring issues
• Check that all monitored metrics are within expected ranges
• Verify integrations still work (APIs change)

Quarterly:

• Assess if the business process has changed (does the automation still match reality?)
• Review and update test cases
• Optimize slow or unreliable steps
• Update documentation

Annually:

• Full review of all automations
• Retire automations that are no longer needed
• Evaluate new tools and capabilities
• Assess cumulative time savings and ROI

Exercise: Create a Testing and Monitoring Plan

For one of the automations you’ve designed in this course:

1. Write 5 test cases covering happy path, error path, and edge cases
2. Define your staged rollout plan (4 stages with criteria for advancing)
3. List the 5 metrics you’ll monitor post-deployment
4. Set alert thresholds for each metric
5. Draft the “Common Issues” section of the runbook

Key Takeaways

• Test at three levels: individual steps, complete flow, and integrated systems
• Use representative (messy) data for testing, not perfect samples
• Deploy in stages: shadow mode, limited deployment, expanded, then full
• Monitor success rate, execution time, error types, and volume continuously
• Set immediate alerts for critical failures, daily digests for warnings, weekly reviews for trends
• Build runbooks so anyone can troubleshoot common issues
• Schedule regular reviews: monthly for errors, quarterly for relevance, annually for full assessment

Next lesson: the capstone. You’ll build a portfolio of three complete automations.