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Workflow Automation
Read mini classes on this topic together, starting with the latest lessons for context.
10
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Set guardrails before automation
The hard part of automation is not wiring tools together; it is deciding what AI may read, change, when it must stop, and how the team compensates after failure.
Told to be thorough, AI research keeps growing and never delivers. It's done when it can support a specific decision. Write that decision, a cap on options, the required evidence, and stop rules; park the rest.
When AI cleans a shared drive, delete can mean trash or permanent removal. Judge it by recovery: AI lists candidates, a person approves a quarantine, a sample restore proves it, and deletion is decided later.
AWS FinOps Agent traces cloud cost spikes to the change behind them, but its named owner is only as good as your account-to-owner map. Write the map first, then route findings.
Apple raised Mac and iPad prices as AI data centers soak up memory. Your needs didn't change; upgrading for future AI just got pricier. Start from the work your device blocks each week.
When one event wakes several AI modules that overwrite each other, teams reach for arbitration rules. Better to fix the design: one entry point per event, one writer per field, and everything else only suggests.
A workflow that fails after creating records or sending notices can do the same thing twice if rerun. Log IDs for each outward step, plan how to fix failures, put irreversible steps last, and stop when unsure.
In Anthropic's internal case, the same Claude went from under 21% to over 95% accuracy on company numbers once sources and definitions were curated, then fell to about 65% after a month unmaintained.
Meta's AI support was talked into changing strangers' emails, and over 20,000 Instagram accounts were stolen. The fix: before AI handles accounts, identity and permissions must come from the system, not the conversation.
An AI outage may be short, but workflows often stall longer. Before relying on AI, define the minimum deliverable without it, keep source data outside AI, and name who decides to switch.
Asana bought no-code agent builder StackAI; its AI Teammates read and write across 10+ apps. Connecting a tool doesn't mean AI should do everything in it. Assign permissions per action, then test failures.