Design a Scalable Expense Rules Engine

Context

You are tasked with designing a scalable rules engine for corporate credit-card expense review. This engine will be part of a multi-tenant expenses API, serving numerous companies. Its primary function is to evaluate submitted expenses against a set of rules and flag items that require manager review.

Expenses are provided as a list of dictionary objects, where all keys and values are strings. Each expense is guaranteed to have identifiers and basic fields such as amount, currency, expense_type, vendor_type, merchant, and transaction_date. A trip_id field may optionally be present.

Core Requirements

The engine must initially support the following base rules:

• No restaurant expense over $75.
• No airfare expenses.
• No entertainment expenses.
• No expense over $250.

Inputs

The core function will accept:

• expenses: A list of dictionaries, where each dictionary represents an expense. All fields within these dictionaries are string-typed and must be parsed and validated by the engine.
• rules: A list of rule definitions. The candidate is responsible for defining the structure and semantics of these Rule types.

Function Signature

You are to propose the design for a function with the following signature: evaluateRules(rules: list<Rule>, expenses: list<Expense>) -> ReturnType

The ReturnType must be stable and backward-compatible for clients across many companies.

Design Goals

The system must meet the following design objectives:

• Rule Management: Support 100+ rules selected per manager/team. Allow new rules to be added and created via an API without breaking existing clients.
• Evaluation Semantics: Clearly specify how rules are evaluated, including:
  • Priority and ordering of rules.
  • Support for logical combinators (AND/OR/NOT) within rules.
  • Decision on short-circuiting versus exhaustive evaluation.
  • Strategy for conflict resolution across multiple rules.
• Explainability & Auditability: For every violation, provide a clear reason, associated rule metadata, timestamps, and information about which policy or entity triggered the violation.
• System Qualities: Address multi-tenant isolation, authorization, observability, idempotency, deterministic ordering, and resilience to partial failures.
• Data Models & Validation: Define core data models (e.g., Expense, Trip, Rule, Policy, RuleResult, EvaluationSummary). Describe the strategy for parsing and validating string-typed inputs like amounts, categories, and vendor types.

Extension: Aggregate (Group-By) Rules

Extend the engine to support rules that operate on aggregated data, such as trip-level totals. Examples include:

• A trip's total expenses must not exceed $2000.
• Total meal expenses per trip must not exceed $200.

The engine should evaluate both per-entity (expense) and aggregate (trip) rules in a single pass. Describe your aggregation strategy, including considerations for caching and incremental updates.

The ReturnType must be backward-compatible and capable of surfacing both expense-level and trip-level violations. For each violation, it should include:

• Entity type (expense or trip) and its identifiers (e.g., expense_id, trip_id).
• Violated rule codes, severity, and human-readable messages.
• Machine-readable fields (e.g., observed_value, threshold, currency).
• Support for pagination (cursor-based) for large result sets.