KYC Routing Is a Rules Problem, Not an AI Problem
Risk tiers, document requirements, verification paths — KYC routing is a set of explicit business rules. AI helps extract data, but the routing decision should be versioned, tested, and auditable.
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Business rules, decision automation, and the TIATON runtime.
#ai-agents #architecture #business-rules #compliance #explainability #failure-recovery #fintech #governance #industry-cases #observability #orchestration
The Extraction Pattern: Using LLMs as Data Operators, Not Decision Makers
LLMs extract structured data from unstructured input. Rules decide what to do with it. A typed contract between the two layers makes the system testable, auditable, and reproducible.
The Rollback Problem: What Happens When Step 4 Fails?
Distributed workflows can't rely on global transactions. AI agents make this worse — they act fast, across many systems, with no built-in undo. The Saga pattern offers design principles that apply.
Your Business Rules Are Technical Debt You Don't Track
Pricing logic, approval thresholds, routing conditions — scattered across code, spreadsheets, and institutional memory. The debt that no dashboard shows.
Explainability Is an Architecture Decision, Not a Feature
Intrinsic vs. post-hoc explainability is determined at design time. EU AI Act Article 86, GDPR, and CJEU rulings are making this an engineering constraint, not a policy discussion.
AI Under Rules vs. AI Under Hope: Two Architectures for Production Agents
The LLM decides everything or the LLM extracts and rules decide. Two architectures, different audit, compliance, and failure recovery outcomes.
Why 95% of Enterprise AI Pilots Fail — and It's Not the Model's Fault
MIT, Gartner, and S&P Global data point to the same pattern: the gap between demo and production kills AI projects. What the 5% that succeed do differently.