The AI Continuous Improvement Cycle
A governance framework for AI systems based on the Plan-Do-Check-Act (PDCA) model. This ensures that AI operations are managed, monitored, and systematically improved over time.
Facilitator / Leadership
PLAN & ACT
Auditor Agent
CHECK
CAPA Manager
ACT
Production Workflow
(Progenitors, Synthesizer, Validator)
DO
Instrumentation Layer
MONITOR
Artifact & Prompt
Repository
The PDCA Cycle in Action
1. Plan & Do
The Facilitator (Leadership) defines goals and deploys the Production Workflow. The workflow executes its tasks (the "Do" phase), generating outputs and operational data.
2. Check
The Instrumentation Layer monitors the workflow. The Auditor Agent analyzes this data, comparing performance against the initial plan and submitting its findings to leadership.
3. Act (Corrective)
If the workflow produces a non-conforming result, the CAPA Manager is triggered. It manages the immediate corrective action and reports back to leadership, addressing the root cause of the failure.
4. Act (Preventive)
Based on audit reports and CAPA data, the Facilitator issues improvement directives. This updates the central Repository with better prompts or workflow logic, starting the cycle anew with a more robust system.