01
Three different tools for three different problems
Traditional automation follows known rules over structured inputs. AI assistance handles ambiguity but leaves the decision with a person. An agent can plan or select tools across several steps within a defined boundary.
Treating these as a maturity ladder is a mistake. A deterministic integration is often the safest and least expensive answer, even inside an advanced AI product.
02
Use rules when correctness can be expressed
Calculations, validation, permissions, state transitions, and exact routing belong in code when their logic is knowable. Rules are testable, fast, and easier to audit. Adding a model to a deterministic decision usually creates variability without creating value.
03
Use AI assistance for interpretation
Documents, conversation, images, and loosely structured requests contain meaning that is difficult to encode as rules. A model can classify, extract, summarize, draft, or recommend while an interface shows evidence and gives a person control.
04
Use agents for bounded multi-step work
An agent becomes relevant when the sequence cannot be fully fixed in advance: it may need to research, choose a tool, inspect a result, and adapt. The boundary still needs approved tools, permissions, budgets, stop conditions, and escalation.
The more consequential the action, the more important reversibility and explicit approval become. “Autonomous” should never mean invisible or unaccountable.
05
Combine them into one reliable workflow
A durable system often uses all three: AI interprets an incoming document, deterministic code validates the extracted fields, an agent gathers missing context, and a person approves the final action. Architecture should assign each part of the job to the mechanism best suited to it.