1. Control Your Triggers (Don’t Trust Them Blindly)
The mistake:
Workflows start more times than you expect.
Why it happens:
- Webhooks firing twice
- Schedules overlapping
- Manual triggers left enabled in production
How to avoid it:
- Use only one trigger per production workflow
- Disable Manual Trigger before going live
- For webhooks:
- Add a unique request ID check
- Log incoming payloads
- For schedules:
- Avoid overlapping intervals
- Add a Set node with execution metadata (timestamp, run ID)
Rule:
If you don’t know exactly why a workflow starts, it’s unsafe.
2. Lock Your Data Shape Early
The mistake:
Data changes form mid-workflow.
Why it happens:
- APIs return different structures
- Arrays become objects
- Missing fields break later nodes
How to avoid it:
- Use a Set node immediately after the trigger
- Define:
- Field names
- Data types
- Defaults for missing values
- Never pass raw API output deep into a workflow
Best practice:
- One “source of truth” Set node
- Every downstream node uses that structure only
Rule:
If data isn’t shaped, it isn’t safe.
3. Make Workflows Understandable (Even to Future You)
The mistake:
Logic lives in your head, not in the workflow.
Why it hurts:
- You forget why something exists
- Small changes cause big breaks
- Debugging becomes guesswork
How to avoid it:
- Rename nodes clearly ❌ “Set” ✅ “Normalize User Data”
- Add sticky notes explaining why, not what
- Group related logic visually
- Color-code sections if helpful
Rule:
If someone else can’t understand it, it’s not production-ready.
4. Use AI Only Where It Makes Sense
The mistake:
Letting AI touch critical logic.
Where AI should NOT be used:
- IDs
- Conditions
- Calculations
- Routing decisions
- Core data structure
Where AI works best:
- Text generation
- Summarization
- Classification
- Content formatting
How to stay safe:
- Keep AI at the edges, not the core
- Use Code or Set nodes for predictable logic
- Validate AI output before using it downstream
Rule:
If the output must be exact, don’t use AI.
5. Build Error Handling from Day One
The mistake:
Assuming you’ll “notice” when something breaks.
Reality:
You won’t.
How to avoid it:
- Create a dedicated Error Trigger workflow
- Send alerts to:
- Slack
- Logs or dashboards
- Include:
- Workflow name
- Node name
- Error message
- Timestamp
Bonus tip:
- Store failed payloads for replay
- Add retry logic where safe
Rule:
If a workflow can fail, it must report it.
6. Use Execution History as a Debugging Tool
The mistake:
Guessing what went wrong.
How to debug properly:
- Open the Executions tab
- Inspect the exact run that failed
- Check data at each node
- Use “Copy to editor” to recreate issues
Why this matters:
- You fix the real problem
- Not symptoms
- Not assumptions
Rule:
Never debug blind.
7. Organize Workflows Like a System
The mistake:
Everything in one place, no structure.
How to avoid it:
- Use Projects:
- By client
- By product
- By purpose
- Separate:
- Production
- Testing
- Experiments
- Limit access if working with teams
Rule:
Messy structure leads to messy failures.
Final Checklist Before Going Live
Before activating any workflow, ask:
- Do I know exactly how it starts?
- Is my data structured early?
- Can someone else understand this?
- Is AI used safely?
- Will I get alerted if it fails?
- Can I debug it quickly?
- Is it organized properly?
If any answer is “no” → fix it first.
