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DMN-Style Decision Tables

Nested AC_if_var chains get unreadable fast. A decision table externalizes branching into rows of conditions -> outputs evaluated by a hit policy — the DMN way to keep business rules data-driven and reviewable.

Each cell condition is a wildcard (None / "-" / "*"), a literal (equality), or {"op": "ge", "value": 18} using the project's standard comparators (eq/ne/lt/le/gt/ge/contains/startswith/endswith — reused from the executor, not duplicated). Pure standard library; imports no PySide6.

Hit policies

Policy Behavior
UNIQUE Exactly one rule may match (raises if more do).
FIRST The first matching rule wins.
PRIORITY Same as FIRST — first match in rule order.
COLLECT All matching rules' outputs (a list).

Headless API

from je_auto_control import evaluate_table

spec = {
    "inputs": ["age", "country"],
    "hit_policy": "FIRST",
    "rules": [
        {"conditions": {"age": {"op": "lt", "value": 18}},
         "outputs": {"tier": "minor"}},
        {"conditions": {"age": {"op": "ge", "value": 18}, "country": "US"},
         "outputs": {"tier": "us-adult"}},
        {"conditions": {"age": {"op": "ge", "value": 18}},
         "outputs": {"tier": "adult"}},
    ],
}
evaluate_table(spec, {"age": 30, "country": "DE"})   # -> {"tier": "adult"}

evaluate_table returns the matched outputs dict (or {} if none) for single-hit policies, and a list for COLLECT. The DecisionTable class (from_dict / evaluate) is available for reuse.

Executor command

AC_decision_table takes spec and context (each a dict or JSON string) and returns {result}. The same operation is exposed as the MCP tool ac_decision_table and as a Script Builder command under Flow.