ConstraintDSL | Constraint Definition Language for Decision Systems v3.2

Product attributes

Canonical product name: ConstraintDSL

Module type: Constraint expression language and rule modeling module

Primary category: Decision intelligence

Secondary categories: Constraint modeling, rule engineering, optimization guardrails, action validation

Intended users: Decision system engineers, optimization engineers, platform architects, AI product teams

Applicable lifecycle stage: Decision modeling, optimization setup, rule structuring, action validation, approval guardrail design

Typical inputs: Business rules, operational limits, thresholds, eligibility conditions, safety boundaries, scenario parameters, constraint configuration

Typical outputs: Structured constraint objects, rule mappings, validation results, solver compatible constraint definitions, action guardrail outputs

Supported delivery format: ZIP package delivered automatically by email after purchase

Expected package contents: Source files, examples, DSL templates, constraint configuration samples, documentation, tests

Runtime environment: Python oriented development environment

Integration mode: Decision engine component, optimization preprocessing layer, policy guardrail layer, action validation module

Recommended skill level: Advanced

Commercial rights: Full commercial use is permitted

Modification rights: Modification, custom rule design, internal adaptation, and proprietary integration are permitted

Open source policy: Public open sourcing is prohibited

Redistribution policy: Resale, redistribution, sublicensing, or repackaging as a standalone module is prohibited

Production readiness note: Requires domain rule review, legal or compliance review where applicable, solver compatibility validation, and action safety testing

Validation standard: The module is considered valid when sample rules can be expressed, parsed, validated, and applied to accept or reject example actions

Description

ConstraintDSL provides a formal structure for expressing rules and limits that decision systems must respect. In advanced AI systems, especially those that generate actions, recommendations, schedules, dispatch plans, or optimization outputs, it is not enough to produce a high value answer. The answer must also be legal, feasible, safe, explainable, and consistent with operational boundaries. ConstraintDSL helps convert business rules, equipment limits, eligibility requirements, numerical thresholds, approval conditions, and scenario specific restrictions into structured definitions that other modules can read. It can sit between strategy generation and optimization, or between optimization output and execution approval. For example, a decision engine may want to maximize revenue, but it must also respect resource limits, capacity boundaries, minimum units, contractual obligations, safety thresholds, and manual approval rules. This module gives teams a reusable way to define such constraints rather than hard coding them into scattered scripts. The module does not contain every industry rule by default. Users must still define and validate their own rule library. For production systems, each constraint should have an owner, version, source, test case, and business explanation. This is especially important when the generated action affects money, safety, compliance, or customer operations.