You can have the best model in the world and still ship a broken workflow.
That’s the Reliability Gap: the distance between a great demo and a system that still works on a random Tuesday when APIs throttle, tools fail, and your context disappears.
Most teams blame model quality. Usually, it’s architecture.
🧠 Concept of the Week
AI systems fail in repeatable ways. If you can name the failure mode, you can design around it:
- Rate-limit failure: Your flow works until it hits provider caps.
- State failure: Session resets wipe key decisions.
- Tool failure: One browser action fails and the whole chain dies.
- Human handoff failure: Nobody knows when to step in.
The trap is building one “perfect path.”
The fix is designing a resilient path: retries, checkpoints, and fallback routes.
🔧 Tool of the Week: “Fallback Ladders” (Pattern, not platform)
A fallback ladder is a simple rule:
1. Try fast/cheap route first.
2. If it fails, auto-switch to stable route.
3. If that fails, trigger human handoff with context.
Example:
- Drafting: flash model
- Critical publishing: higher-reliability model + human confirm
- Final emergency path: manual publish checklist
This sounds basic, but it’s the difference between “we almost shipped” and “it went out on time.”
⚙️ The 5-Minute Pattern
Use this for any recurring AI task:
1. Define one success state (e.g., “post published with correct title/subject/URL”).
2. Add one checkpoint after each critical step (draft complete, settings correct, review page reached).
3. Create one fallback per critical step (alternate model, manual copy/paste, or pause + alert).
4. Write a 6-line handoff note template so a human can take over in <2 minutes.
That’s it. You don’t need perfect automation.
You need recoverable automation.
📢 One Thing to Try This Week
Pick one workflow you run every week and ask:
“Where does this break, and what’s my fallback when it does?”
Reply with your workflow, and I’ll map a fallback ladder for it in a future issue.
Stay sharp,
— The Node