Rules Engines
Our rules layer encodes business conditions that should remain explicit, inspectable, and deterministic.
What rules govern
Rules can govern thresholds, permissions, routing, exceptions, business policies, escalation, and execution boundaries. They provide a predictable control plane around model-driven interpretation and automated workflows.
Rule evaluation
A rule can be simple:
- Invoice days outstanding > threshold→Route to collections review
or depend on multiple structured conditions. The important property is that the logic is explicit enough to test independently of a language model. Rules engines are particularly useful where the company already knows the policy it wants enforced.
Rules and models
Rules do not compete with models. They solve a different problem.
A model may interpret whether a communication constitutes a complaint. A rules engine can then determine which severity levels require manager review, which customers require special handling, and which actions are prohibited without approval. This creates a boundary:
- Ambiguous interpretation→Model
- Known policy→Rules
- State transition→Workflow
- Consequential exception→Approval / escalation
Maintenance
Rules create their own lifecycle. Thresholds can become stale. Policies change. Exceptions accumulate. Poorly managed rule sets can become contradictory or difficult to reason about.
We therefore treat important rules as versioned operating logic rather than scattering them through prompts or application code where changes are difficult to inspect.
Why explicit rules matter
Encoding known business logic inside a model prompt can make deterministic policy appear flexible while actually making it less testable. Where the company knows the rule, we prefer to preserve it as software.
That allows the intelligence layer to focus on the parts of the problem where interpretation is genuinely necessary while keeping policy, permissions, and exact routing behavior under deterministic control.
Rules versus thresholds learned from data
A rule can originate from business policy or from an empirically selected operating threshold, but those are not the same thing. A contractual approval limit may be fixed by policy. A customer-risk threshold may be chosen from historical evaluation and may need recalibration as the data distribution changes.
We preserve that distinction because it changes how the rule should be governed. Policy changes require authorization. Empirical thresholds require measurement. Both can be deterministic at runtime while having different maintenance processes.
Rules engines are therefore useful not only because they are predictable, but because they make the source of operating authority explicit instead of burying policy inside model behavior.