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Architecture

Hybrid Automation Architecture

45 min
5 lessons

Learn why pure AI automation fails in production, and how to build reliable systems by combining AI intelligence with deterministic code triggers.

1 The Problem with Pure AI Automation 10 min

The Salesforce Case Study

In September 2025, Salesforce launched Agentforce - autonomous AI agents that could handle customer interactions, grant discounts, and modify orders without human oversight.

116 days later, they reversed course.

What Went Wrong

  • October 2025: Customers discovered they could get 40% discounts by rephrasing requests. The AI interpreted "I'm a loyal customer" as sufficient justification.
  • November 2025: A customer used an AI-granted discount as legal proof. Salesforce's lawyers discovered AI decisions are contractually binding.
  • December 2025: Mandatory pivot to "Human-in-the-Loop" for all financially impactful actions.

Key Insight

AI decisions are legally binding. When an AI says "I'll give you 30% off", that creates an implicit contract your company must honor.

2 Deterministic vs Probabilistic 8 min

Understanding this distinction is fundamental to building reliable automations.

Deterministic Automation (Code/Triggers)

  • Based on if/then rules written in code
  • ALWAYS produces the same result for the same input
  • Example: if cart > $100 AND loyalCustomer then apply 10% discount
  • Testable, auditable, predictable

Probabilistic AI (LLM)

  • Based on next-token prediction with statistical likelihood
  • Can produce different outputs for similar inputs
  • Affected by context, phrasing, temperature settings
  • Creative, flexible, but unpredictable

The Golden Rule

AI to understand intent. Code to execute action. Never the reverse.

3 The 80/20 Trap 8 min

The "Hello World" Fallacy

AI demos are impressive. They handle "Where's my order?" beautifully. But demos show the 80% - the routine cases AI handles well.

The 20% edge cases represent 100% of your risk.

Routine vs Edge Cases

Routine (80%) Edge Case (20%)
"Where's my order?" Lost order + VIP + wedding gift + overseas
"What's the price?" B2B negotiated + promo stacking + currency conversion
"Return this product" Personalized item + past deadline + emotional reason
Standard FAQ Legal request + cultural context + medical emergency

Testing Strategy

Test the 20%, not the 80%. Your QA should focus on edge cases, not the scenarios that already work in demos.

4 The Three-Layer Architecture 12 min

Hybrid architecture separates concerns into three distinct layers, each with clear responsibilities.

Layer 1: Intent Layer (AI)

AI excels at understanding what users want.

  • Request classification (support, sales, complaint...)
  • Entity extraction (product, amount, urgency, sentiment)
  • Context understanding (history, tone, cultural nuances)
  • Suggestion of possible actions

Example

User: "I've been a customer for 5 years and this is unacceptable!"

AI Output: { intent: "complaint", sentiment: "angry", vip_likely: true, escalation_recommended: true }

Layer 2: Action Layer (Deterministic)

Code executes actions within strict, auditable boundaries.

  • Hard-coded business rules
  • Non-negotiable limits (max discount: 15%)
  • Pre-execution validation
  • Automatic escalation when out of scope

Example

Rule: if discount_requested > MAX_DISCOUNT then escalate_to_human()

No AI can override this. It's code.

Layer 3: Override Layer (Human)

Humans monitor, correct, and handle exceptions.

  • Real-time dashboard of AI decisions
  • Alerts on detected edge cases
  • Veto capability for any action
  • Feedback loop for continuous improvement
5 Practical Implementation 7 min

Cart Abandonment Example

Let's compare pure AI vs hybrid architecture for cart abandonment emails.

Pure AI Approach (Risky)

"AI decides if it sends an email, what content, what discount to offer, and when."

Problem: AI might offer 50% to a $20 cart. Or nothing to a VIP.

Hybrid Approach (Reliable)

Step 1: Deterministic Trigger

Cart abandoned for 1 hour → trigger sequence (code-based, predictable)

Step 2: AI Personalization

Analyze customer history → suggest tone and complementary products

Step 3: Business Rules

if cart > $50 AND first_reminder then max_discount = 10%

Step 4: Human Escalation

If VIP detected → notify sales team for personal outreach

The Five Rules

  1. AI proposes, code disposes. AI can suggest. Only code applies with limits.
  2. Test the 20%, not the 80%. Focus QA on edge cases.
  3. Financial impact = validation. Discounts, refunds, credits need rules or humans.
  4. Escalation by default. When in doubt, hand off to human.
  5. Complete audit trail. Every AI decision must be traceable.

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