Enterprises Don't Need More Autonomous AI Agents—They Need to Fix Exception Handling

Puzzled-Effort-9319 · reddit · 2026-10-26

Core Point

Most enterprises don't need more autonomous AI agents—they need less broken processes. More precisely: exception handling is where things break down.

A Real Case

A distribution client asked for an end-to-end autonomous order-processing agent. The author spent a day sitting next to actual operators and found:

About 90% of orders flow straight from Shopify to the ERP with no intelligence needed at all
Operators spend most of their day on the remaining 10% of exceptions: missing PO numbers, address mismatches, contract prices the ERP doesn't recognize, etc.

What They Did

Use simple automation for the normal flow; the agent handles only exceptions: pulling context from both systems, diagnosing the problem, drafting a fix, and the operator approves with one click
Result: exception handling dropped from most of the day to about 1 hour

Three Lessons

Don't rush to standardize: First ask everyone why they do things their own way. If the reason involves customers or risk (e.g., the contract allows split shipments), keep it and write it into the rules; only standardize when the answer is just "that's how I was taught"
Don't rush to remove workarounds: A sticky note listing 6 customers requiring manual checks traces back to an incident two years ago—wrong pallet size nearly lost a contract. An "autonomous" version would skip that check entirely
Distinguish two types of workarounds: Those that just move data around are mostly waste and can be automated; those that perform checks are mostly safety nets, and each has a story behind it—listen to the story before cutting it

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