2.3 KiB
Boundary Validation
Status: implementation-facing MVP for ADAPTIVE-WP-0004.
Purpose
This document describes the first explicit boundary engine now available in
adaptive_pricing_core.boundary_engine.
The engine turns pricing-policy intent into inspectable validation outcomes instead of leaving viability checks implicit in dashboard review.
Inputs
The validator accepts:
- a canonical
PricingModel - a
PricingConfigurationdescribing expected usage, fee assumptions, cost allocation, optional price overrides, and commitment terms - a
BoundaryPolicydefining hard and soft limits
Constraint Types
Current MVP constraints cover:
- segment eligibility
- expected usage variance limit
- payment fee ceiling
- cost-floor coverage
- minimum margin
- target-margin approval threshold
- discount exposure ceiling
- discount approval threshold
- commitment-backed concession enforcement
Hard constraints reject a configuration. Soft constraints mark it as valid only with approval.
Commitment Logic
The engine treats a concession as any configuration that weakens seller economics relative to the model baseline under the same scenario assumptions.
A concession is considered meaningfully backed only when at least one of these protections is present:
- minimum monthly turnover at or above modeled monthly revenue
- prepayment covering at least one modeled month
- guaranteed platform fee at or above modeled monthly revenue
- customer-funded onboarding that neutralizes onboarding cost
- materially longer contract duration
- reduced cancellation flexibility
Outputs
Validation returns a ValidationResult with:
decision:accepted,requires_approval, orrejectedvalidandrequires_approval- a human-readable summary
- machine-readable configuration snapshot
- machine-readable economics metrics
- per-constraint results with reasons, thresholds, and suggested actions
Coulomb Adapter
The Coulomb observatory exposes this engine through
observatory.boundary.build_boundary_validation().
That adapter currently uses:
- observed per-period payment fee rate
- observed AI usage cost
- observed per-member infrastructure cost allocation
- conservative default policy thresholds
This is intentionally an MVP policy surface. Later milestones can replace these defaults with seller-managed governance data and richer LTV-aware constraints.