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Agentic Autonomy Levels
Managing agentic systems requires moving from simple prompting to hierarchical delegation, where a 'manager agent' verifies results before presenting them for human approval.
What: Addy Osmani outlines a tiered autonomy model for AI agents, arguing that high-autonomy agents are best for large-scale tasks like fleet-wide refactoring. The ideal architecture involves a manager agent that delegates sub-tasks to helpers and continuously validates their outputs against a set of constraints.
Why it matters: Scaling agentic systems is fundamentally an engineering problem of managing state and trust; hierarchies allow for high throughput without sacrificing the ability to halt execution.
Decoder
- Agentic engineering: The shift from using AI as a chatbot that generates snippets to treating AI agents as autonomous entities that operate within a system to achieve goals.
Original article
Agentic Autonomy Levels
In most conversations about agentic engineering, the action has changed from prompting to operating. Here's a frontier looking into the fog: software factories, goals, loops, background sessions,...
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