Most automation workflows don’t fail when you build them.
They fail later — in production.
After a few days, or under real usage.
Common issues I keep seeing:
- APIs returning 200 but nothing happens downstream
- silent failures (no retries, no alerts, no logs)
- workflows that work once, but break under repeated execution
- unexpected cost growth due to retries, loops or AI calls
- partial failures across integrations leaving inconsistent state
From the outside everything looks fine.
But something is already broken.
I’ve been working on analyzing these failure modes across automation setups (n8n, custom integrations, AI pipelines, API chains) and identifying where things break before they scale.
What I can help with:
- diagnosing unreliable workflows
- identifying missing error handling / retry logic
- analyzing API / AI cost exposure
- detecting risky patterns (loops, chains, integrations)
- improving production reliability
If you’re building something with workflows, APIs or AI and things feel “fragile”, I’m happy to take a look.