Published 2025-12-19 09-35
Summary
We bolted AI onto old workflows and called it progress. Real change means designing processes where multiple specialized AI agents own tasks, use tools, and actually run the show—not just autocomplete your anxiety.
The story
Before: We treated AI like a fancy autocomplete bolted onto a 2007 workflow.
One LLM call here. A “smart” template there. Then we wonder why the process still feels like a Rube Goldberg machine held together by Slack pings and human anxiety.
That model made sense when work was linear: Step 1, Step 2, Step 3, please do not breathe on the spreadsheet.
But AI is rendering a lot of those old workflows… not “bad,” just obsolete. The moment you expect AI to run a real business process end-to-end, a single prompt is like trying to run payroll with a sticky note.
After: I’ve been thinking in terms of agentic workflows, like AgentFlow’s approach: multiple specialized AI “workers” that each own a domain task, then get orchestrated into a higher-order flow that looks like an actual business process.
Data enrichment agent. Decisioning agent. Content generation agent. QA agent. Modular, reusable, swappable. Like refactoring a monolith into services, except the services talk like nerds and never ask for PTO.
The real unlock is tool use and integrations: agents can call APIs, databases, internal systems; they can act, not just chat. And because this is production, observability and controllability matter: logging, tracing, monitoring. If it can’t be debugged, it can’t be trusted.
So here’s my question: what process outcome do you want… if you stopped “sprinkling AI” and started redesigning the workflow around automation from the ground up?
For more about making the most of AI, visit
https://linkedin.com/in/scottermonkey.
[This post is generated by Creative Robot]. Designed and built by Scott Howard Swain.
Keywords: #BusinessProcessReengineering, AI workflow transformation, multi-agent systems, autonomous process design
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