Policy

/semantica:policy

Policy governance over recorded decisions. Usage: /semantica:policy <task> [args]

PolicyEngine lives in semantica.context. For most cases the two policy methods on ContextGraph itself are enough.

check <decision> — the simple path

No policy store needed; rules default to a built-in policy set.

from semantica.context import ContextGraph

graph = ContextGraph()
result = graph.check_decision_rules({
    "category": "vendor_selection",
    "outcome": "approved",
    "confidence": 0.93,
    "decision_maker": "gyro",
})
# {'compliant': bool, 'violations': [...], 'warnings': [...], 'policy_rules': {...}}

Default rules: min_confidence=0.7, required_outcomes=['approved','rejected','flagged'], required_metadata=['decision_maker'], max_reasoning_length=1000. Override by passing your own rules= dict.

enforce <decision> [--rules <dict>]

verdict = graph.enforce_decision_policy(decision_data, policy_rules=None)

Managed policies — the full path

PolicyEngine requires a graph store and versioned Policy objects.

from semantica.context import PolicyEngine
from semantica.context.decision_models import Policy

engine = PolicyEngine(graph_store)

policy_id = engine.add_policy(Policy(...))
policies  = engine.get_applicable_policies(category="vendor_selection", entities=[...])
ok        = engine.check_compliance(decision, policy_id)
history   = engine.get_policy_history(policy_id)

engine.update_policy(policy_id, rules={...}, change_reason="tightened threshold")
engine.record_exception(decision_id, policy_id, reason="...", approver="...")
impact    = engine.analyze_policy_impact(policy_id, proposed_rules={...})
affected  = engine.get_affected_decisions(policy_id, from_version, to_version)

Note check_compliance takes a Decision object, not a dict — fetch it from the graph rather than constructing one by hand.