Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Picture this: It's 2 AM, your automated workflow that was supposed to handle client onboarding just broke, and you're staring at a cryptic Make error message that might as well be written in ancient hieroglyphs. Sound familiar?
Last year, while working with a B2B startup client, I had to migrate their entire automation stack from Make.com to Zapier specifically because of debugging nightmares. The breaking point? A single scenario error that took down their entire client operations workflow, and we spent 6 hours trying to figure out what went wrong.
Here's the uncomfortable truth: Most debugging guides for Make treat error messages like they're helpful. They're not. The real skill isn't reading error messages—it's building systems that prevent errors and knowing exactly where to look when things inevitably break.
After migrating dozens of automation workflows and debugging hundreds of scenario failures, I've developed a systematic approach that goes way beyond "check your API keys" and "look at the error logs." This playbook will teach you:
Why Make's error reporting is fundamentally flawed and how to work around it
The 5-step debugging framework I use for every scenario failure
How to build error-resistant scenarios from the ground up
When to abandon Make entirely (and what to use instead)
My testing workflow that catches 90% of errors before they hit production
If you're tired of playing detective with automation breakdowns, this is for you.
Technical Reality
The truth about Make's debugging experience
Every automation platform tutorial makes debugging sound straightforward: "Check the error message, fix the issue, run again." The reality? Make's debugging experience is fundamentally broken in ways that most guides don't acknowledge.
Here's what the industry typically recommends for debugging Make scenarios:
Read the error message carefully - As if Make's error messages are actually helpful
Check your API connections - The universal solution for everything
Test each module individually - Time-consuming and often misleading
Review your data mapping - Good luck when the data structure changes mid-flow
Contact support - For scenarios that should work but mysteriously don't
This conventional wisdom exists because Make's documentation is written by people who've never dealt with complex, real-world scenarios. They assume perfect conditions: stable APIs, consistent data structures, and predictable inputs.
But here's where it falls short in practice: Make's error reporting doesn't tell you what actually happened. When a scenario fails, you get a vague message like "Invalid request" or "Connection timeout." But you don't get the context: Was it a rate limit? A malformed request? A temporary API hiccup? Did the failure happen because of something that occurred 3 modules earlier?
The bigger issue? Most debugging advice treats symptoms, not root causes. By the time you're debugging, you're already in reactive mode. The real solution is building scenarios that fail gracefully and provide meaningful feedback when things go wrong.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My wake-up call came while working with a B2B startup that needed to automate their client onboarding process. They were using HubSpot for CRM and Slack for project management, and every time a deal closed, someone had to manually create a Slack workspace for the project.
Sounds simple, right? Deal closes in HubSpot, trigger a Make scenario to create a Slack workspace, invite team members, set up channels. What could go wrong?
Everything, as it turns out.
The first version worked beautifully in testing. Clean data, perfect conditions, happy path all the way. Then we deployed it to production, and within a week, it had failed 12 times. Each failure meant a client project got delayed because no one knew the automation had broken.
The error messages were useless: "Error in Slack module," "HTTP 400 Bad Request," "Execution stopped." That's it. No context about what specific data caused the issue, no indication of which step in the 8-module sequence actually failed.
My client was frustrated because they'd invested time training their team to rely on this automation, and now they had to babysit it constantly. I was frustrated because I knew the logic was sound, but Make's debugging tools made it impossible to understand what was actually happening.
After spending hours trying to reverse-engineer failures from cryptic logs, I realized the fundamental problem: I was building scenarios the way Make's tutorials taught me, not the way real-world automation actually works.
Real-world data is messy. APIs have hiccups. Users enter information in ways you never anticipated. And when things break, you need to know exactly what happened and why, not just that something went wrong somewhere.
That's when I started developing what I now call my "Failure-First" debugging approach.
Here's my playbook
What I ended up doing and the results.
Instead of trying to prevent all errors (impossible), I started building scenarios that expect failure and handle it gracefully. Here's the exact framework I developed after debugging hundreds of broken Make scenarios:
Step 1: Error Logging Before Everything Else
Before writing any automation logic, I set up detailed logging. Not Make's basic execution history—custom logging that captures exactly what's happening at each step. I use a simple Google Sheet or Airtable base with columns for timestamp, scenario name, input data, error details, and success/failure status.
Every critical module gets wrapped with error handling that logs both success and failure states. This means when something breaks, I can see exactly what data was being processed and which specific condition caused the failure.
Step 2: The "Canary" Module
At the beginning of every scenario, I add what I call a "canary" module—a simple HTTP request to a webhook that logs "Scenario started with [input data]." This immediately tells me if the scenario is receiving the expected trigger data.
You'd be surprised how many "mysterious" failures are actually trigger issues—wrong data format, missing fields, or triggers firing when they shouldn't.
Step 3: Defensive Data Handling
Instead of assuming data will always be in the expected format, I explicitly check for required fields at each step. If a field is missing or malformed, the scenario logs the issue and either uses a default value or gracefully fails with a meaningful message.
For the HubSpot-Slack integration, this meant checking that deal names didn't contain special characters that would break Slack workspace creation, verifying that team member emails were valid, and ensuring required custom fields were populated before proceeding.
Step 4: Isolation Testing
Rather than testing entire scenarios end-to-end, I built modular test scenarios for each critical operation. Want to test Slack workspace creation? Build a standalone scenario that just does that, with known good data. This lets you isolate exactly which operations work and which don't.
Step 5: The "Circuit Breaker" Pattern
If a scenario fails more than 3 times in an hour, it automatically disables itself and sends an alert. This prevents cascading failures and gives you time to investigate without causing more damage.
I implement this using a simple counter in a database—each failure increments the counter, and the scenario checks this counter before running any critical operations.
For the startup client, this approach transformed their experience. Instead of mystery failures that required detective work, they got clear alerts: "Workspace creation failed because deal name contains invalid characters" or "Scenario paused after 3 consecutive API timeouts—manual review required."
Failure-First Design
Build scenarios that expect things to go wrong instead of assuming perfect conditions.
Defensive Data
Check data format and required fields at every step rather than trusting inputs.
Isolation Testing
Test individual operations separately before combining them into complex scenarios.
Circuit Breakers
Automatically disable failing scenarios to prevent cascading problems.
The results of this debugging approach were immediate and measurable. For the startup client, scenario reliability went from 70% (12 failures in the first week) to 95% over the next month.
More importantly, when failures did occur, resolution time dropped from hours to minutes. Instead of digging through execution logs trying to piece together what happened, we had clear error messages with full context.
The circuit breaker pattern alone saved countless hours. Previously, a failing scenario could execute dozens of times before anyone noticed, creating data inconsistencies and confusing clients. Now, failures are caught immediately and automatically contained.
Perhaps most surprisingly, this approach actually made scenarios more maintainable. When you build with failure in mind from the start, you end up with cleaner, more modular logic that's easier to modify and extend.
The time investment upfront—maybe 30% more development time—pays for itself within the first week of production use. And unlike traditional debugging, this approach gets better over time as your error handling becomes more sophisticated.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from debugging dozens of Make scenario failures:
Make's error messages are designed for simple scenarios. Once you're dealing with complex, multi-step automations, you need to build your own error reporting system.
The most common "errors" aren't technical failures—they're data quality issues. Bad inputs cause more scenario failures than API problems or logic errors.
Testing happy path scenarios is worse than useless—it gives you false confidence. Real testing means intentionally feeding your scenarios malformed data, testing during API outages, and simulating edge cases.
Time spent on error handling is never wasted. Every hour you invest in proper logging and failure detection saves multiple hours later when things inevitably break.
Know when to walk away from Make. For complex B2B workflows requiring high reliability, Make's debugging limitations make it unsuitable. I've migrated multiple clients to Zapier specifically because its error reporting is more detailed and reliable.
User training matters more than perfect scenarios. Even with excellent error handling, scenarios fail. Having a process for your team to check automation status and handle failures is crucial.
Monitor scenario health proactively. Don't wait for users to report problems—build dashboards that show scenario success rates, error trends, and performance metrics.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS teams implementing this approach:
Set up error logging before building any production scenarios
Create dedicated test scenarios for each integration
Implement circuit breaker patterns for critical workflows
Train your team on automation monitoring and failure protocols
For your Ecommerce store
For ecommerce stores managing automation:
Focus on order processing and inventory sync error handling
Test scenarios with malformed customer data and edge cases
Build alerts for payment processing and shipping automation failures
Create fallback manual processes for critical customer-facing workflows