AI agent autonomy exists on a spectrum from simple autocomplete to fully autonomous systems. Level 0 (Suggestion): AI generates suggestions, human decides. Example: GitHub Copilot inline suggestions. Zero risk but minimal productivity gain. Level 1 (Assisted): AI performs discrete tasks on request, human reviews output. Example: AI drafts email, human edits and sends. Level 2 (Supervised Automation): AI completes multi-step workflows with human checkpoints at key decisions. Example: AI processes customer support tickets, humans review and approve responses. Level 3 (Monitored Autonomy): AI runs independently within defined boundaries, humans review exceptions and periodically audit. Example: AI-powered trading within risk limits, human reviews daily P&L. Level 4 (Conditional Autonomy): AI operates fully autonomously for routine cases, escalates edge cases. Example: AI handles 80% of IT tickets without human involvement. Level 5 (Full Autonomy): AI operates completely independently with self-monitoring. Very rare in practice, high-stakes domains. Design principles: match autonomy level to risk tolerance and reversibility. Financial transactions: Level 2-3. Customer communications: Level 2-4 depending on stakes. Code deployment: Level 2-3 with testing gates. Data analysis: Level 3-4. Framework: increase autonomy incrementally, measure error rates at each level, establish clear escalation paths.