Leave Request Approval with Balance and Policy Enforcement

The problem

Leave requests submitted through HRIS are evaluated by Corules before manager notification. Accrual balance, blackout periods, consecutive day limits, and team coverage minimums are checked. Policy-compliant requests notify the manager for awareness. Requests violating coverage minimums escalate. Blackout-period requests block with explanation.

Without deterministic enforcement, AI agents either block every edge case (adding manual overhead) or silently approve decisions that violate policy — with no audit trail to show auditors or regulators.

How Corules solves it

Corules sits between your AI agent and the action it wants to take. When the agent proposes a decision, Corules evaluates the full context against your compiled policy set in a single deterministic pass — no LLM, no ambiguity.

The result is a structured outcome: BLOCK — Request overlaps Q4 blackout period (Nov 25 – Jan 3). Policy prohibits leave during this period.

Policy example

Corules policies are written in CEL (Common Expression Language). They are compiled once at publish time and evaluated deterministically at request time — no LLM, no variability.

// Leave policy (CEL)
context.requested_days <= context.available_balance
  && !context.overlaps_blackout_period
  && context.team_coverage_remaining >= params.min_team_coverage_pct
  && context.consecutive_days <= params.max_consecutive_leave_days

This expression is evaluated against the structured context your agent sends in the /v1/validate request.

Integration options

Corules integrates with the tools your teams already use. All integrations call the same REST API or MCP server — your policy logic stays in one place.

Frequently Asked Questions