Automation view
AI Support Automation Architecture
A controlled automation model connecting intent detection, knowledge retrieval, workflow execution, customer context, agent assistance, and human escalation.
Support automation should reduce repetitive work and improve decision quality without hiding uncertainty or removing appropriate human control.
Disconnected bots and scripts often fail because they lack trusted knowledge, live customer context, action controls, error handling, and clear escalation rules.
Capability layers and operating flow
This diagram is a conceptual portfolio artifact. Specific products, integrations, controls, and ownership would be confirmed during discovery.
Understand
Channel input
Intent detection
Authentication
Risk classification
Retrieve
Approved knowledge
Customer context
Open cases
Product status
Act
Guided self-service
Workflow execution
Case or task creation
Notifications
Assist
Recommended answers
Next best action
Summaries
Agent guidance
Govern
Confidence rules
Human approval
Audit trail
Fallback and escalation
Rules that shape the architecture
Retrieval before generation
Answers are grounded in approved, attributable support knowledge.
Actions are controlled
Automation executes only permitted workflows with validation and auditability.
Uncertainty is visible
Low-confidence or high-risk work moves to a human with the context already collected.
ADR 004 — Use AI as a governed assistance and workflow layer
Decision
Combine retrieval, deterministic workflows, and human escalation rather than relying on unrestricted generative responses.
Reason
A controlled model improves reliability, traceability, and operational adoption while still reducing repetitive work.
Expected impact
- More consistent self-service
- Faster agent resolution
- Safer automation and clearer accountability
This is a conceptual architecture decision for portfolio demonstration. Outcomes are design objectives, not claims of measured results for a specific organization.