TL;DR

• AI autonomy isn't linear: Real agentic AI requires balancing three independent dimensions: Scope of Authority (what it can decide), Human Interaction Patterns (who makes decisions), and Error Handling & Recovery (how it responds to problems)

• Five practical levels emerge: From Level 1 (Supervised Task Automation with human approval and fail-stop recovery) to Level 5 (Autonomous General Systems with resilient error handling across all domains)

• Start narrow, scale gradually: Organizations should begin with limited scope and approval-based systems, then incrementally expand authority, reduce human oversight, and implement more sophisticated error handling as capabilities mature

• Error handling is critical: Higher autonomy levels require progressively more sophisticated error recovery, from simple fail-stop mechanisms to resilient systems that continue operating through problems

• Each dimension creates different risks: A narrow-scope system might operate autonomously, while a broad-scope system might need constant human oversight - the framework helps match capabilities to organizational risk tolerance

• Implementation requires systematic planning: Success depends on comprehensive assessment of organizational readiness, incremental development strategies, robust governance structures, and ongoing monitoring with continuous improvement processes