When Workflow Engines Beat LLM Agents: A Decision Framework for Deterministic Orchestration
Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027 — primarily due to escalating costs, unclear business value, and inadequate risk controls. Industry surveys put the production success rate for autonomous AI agents somewhere between 5% and 11%. Those numbers suggest something important: for a large fraction of the tasks teams are throwing agents at, a deterministic workflow engine would have done the job faster, cheaper, and more reliably.
This isn't an anti-AI argument. It's an architectural one. The question isn't whether LLMs are capable — it's whether autonomous, open-ended reasoning is the right execution model for the task you're building. For a surprisingly large class of structured business processes, the answer is no.
