AI Workflow Foundations: Business rules and exception handling

Business Rules and Exception Handling: Study Guide

What This Is

Business rules are explicit, reusable logic that define how decisions are made in workflows (e.g., "If a customer’s order exceeds $1,000, require manager approval"). Exception handling ensures workflows gracefully manage deviations (e.g., "If the manager’s approval times out, escalate to the next-level approver"). These concepts matter because they automate consistency, reduce errors, and keep operations running when edge cases arise. Example: A bank’s fraud detection system flags transactions over $10K (business rule) but routes borderline cases to a human reviewer (exception handling) instead of rejecting them outright.

Key Facts & Principles

• Business Rule: A declarative statement that enforces a policy or constraint. Example: "Discounts >20% require VP approval" is a rule; the code if discount > 0.2: require_approval() implements it.
• Rule Engine: A tool (e.g., Drools, IBM ODM) that executes business rules separately from application code, making them easier to update. Example: Updating a shipping fee rule in a rule engine doesn’t require redeploying the e-commerce app.
• Exception: A deviation from the expected workflow path. Example: A missing document in an onboarding process triggers an exception, pausing the workflow until resolved.
• Exception Handling: The process of detecting, logging, and resolving exceptions. Example: If a payment fails, the system retries twice, then emails the customer with a link to update their card.
• Idempotency: Ensuring repeated actions (e.g., retries) don’t cause unintended side effects. Example: A "refund" API call should return the same result whether called once or five times.
• Dead Letter Queue (DLQ): A storage system for exceptions that can’t be processed automatically, allowing manual review. Example: Failed insurance claims are sent to a DLQ for underwriters to investigate.
• Rule Chaining: Linking rules so the output of one triggers another. Example: "If customer is VIP-apply 15% discount-notify loyalty team."
• Overlap/Conflict: When rules contradict each other. Example: Rule A says "Free shipping for orders >$50," but Rule B says "No free shipping for Alaska." Resolve with priority or hierarchy.
• Test Case Coverage: Rules should be tested for normal cases, edge cases, and exceptions. Example: Test a loan approval rule with credit scores of 750 (approve), 600 (deny), and 650 (manual review).
• Audit Trail: A log of all rule executions and exceptions for compliance. Example: A healthcare system logs every time a "HIPAA compliance" rule is triggered or bypassed.

Step-by-Step Application

1. Define the Workflow

2. Map the "happy path" (normal flow) and identify decision points. Example: For an expense report, the happy path is "submit-auto-approve if <$500-reimburse." Decision points: approval thresholds, receipt requirements.

3. Extract Business Rules

4. List rules as "IF [condition] THEN [action]." Example:

   • IF expense > $500 THEN require manager approval.
   • IF receipt missing THEN reject and notify submitter.

5. Design Exception Paths

6. For each rule, ask: "What if this fails?" Example:

   • IF manager doesn’t approve in 48 hours THEN escalate to director.
   • IF receipt is unreadable THEN route to finance for manual review.

7. Implement Rules and Exceptions

8. Use a rule engine (for complex logic) or code (for simple cases). Example:

   • Rule engine: Deploy rules in Drools for dynamic updates.
   • Code: Write a Python function with try/except blocks for exceptions.

9. Test Edge Cases

10. Create test scenarios for:

    • Rule conflicts (e.g., VIP customer + Alaska shipping).
    • System failures (e.g., database timeout during approval).
    • Invalid inputs (e.g., negative expense amount).

11. Monitor and Iterate

12. Track exceptions in a dashboard (e.g., Grafana) and refine rules. Example: If 30% of expense rejections are due to missing receipts, add a pre-submission receipt check.

Common Mistakes

• Mistake: Hardcoding rules in application logic. Correction: Use a rule engine or configuration file. Why: Hardcoded rules require developer time to update (e.g., changing a discount threshold from 10% to 15%).

• Mistake: Ignoring idempotency in exception retries. Correction: Design retries to be idempotent (e.g., use unique IDs for transactions). Why: Retrying a failed payment could double-charge the customer.

• Mistake: Treating all exceptions the same. Correction: Categorize exceptions (e.g., "retryable" vs. "fatal") and handle them differently. Why: A network timeout (retryable) shouldn’t be treated like an invalid credit card (fatal).

• Mistake: Not logging exceptions. Correction: Log exceptions with context (e.g., timestamp, input data, rule triggered). Why: Without logs, debugging "why did this order fail?" becomes guesswork.

• Mistake: Over-engineering rules for rare edge cases. Correction: Start with the 80% of cases, then add exceptions. Why: A rule like "IF customer is a celebrity THEN skip fraud check" adds complexity for minimal gain.

Practical Tips

• Start with a "Rule Inventory": Document all existing rules (even informal ones) in a spreadsheet. Example: List rules like "New customers get 10% off first order" and "Returns accepted within 30 days."
• Use Decision Tables: For complex rules, create a table with conditions (rows) and actions (columns). Example: | Order Value | Customer Tier | Action | |-------------|---------------|-----------------| | >$1K | Gold | Free shipping | | >$1K | Silver | 50% off shipping|
• Automate Exception Alerts: Set up Slack/email alerts for high-priority exceptions (e.g., "Payment failed for order >$10K"). Example: Use Zapier to notify the finance team when a refund exception occurs.
• Review Rules Quarterly: Business policies change; audit rules for relevance. Example: If a "COVID-19 discount" rule is still active, remove it.

Quick Practice Scenario

Scenario: A SaaS company’s subscription renewal workflow automatically charges customers on the renewal date. A customer’s card declines, triggering an exception. The system retries the charge after 3 days, but the card fails again. Question: What’s the next step in the exception handling flow? Answer: Send the customer an email with a link to update their payment method, and pause their service after 7 days if unresolved. Explanation: This balances automation (retries) with human intervention (email) to avoid service disruption.

Last-Minute Cram Sheet

1. Business rule = IF [condition] THEN [action]; keep it separate from code.
2. Exception handling = Detect-Log-Resolve (automatically or manually).
3. Rule engine (e.g., Drools) > hardcoding for dynamic rules.
4. Idempotency = Same action, same result, no side effects. Retries can cause duplicates.
5. Dead Letter Queue (DLQ) = Park unprocessable exceptions for manual review.
6. Test rule conflicts (e.g., VIP + Alaska shipping) before deployment.
7. Audit trails = Log every rule execution for compliance. No logs = no debugging.
8. Start simple: Handle 80% of cases first, then add exceptions.
9. Overlap/conflict = Resolve with priority (e.g., "VIP rules override standard rules").
10. Monitor exceptions in real time (e.g., Grafana dashboard). Silent failures = unhappy customers.