AI Agent Grading Framework
Exploring different levels of AI Agents and their capabilities from functionality and autonomy perspectives
Level 1: Simple Reflex Agent
Description：
Responds directly to the environment based on predefined rules or conditions, without complex reasoning.
Features：
- Perceives the environment and acts immediately.
- No memory, no planning, single behavior.
Examples：
- Automatic email reply rules (e.g., 'Reply with thanks upon receipt').
- Smart home thermostat (heat when temperature falls below X).
Comparison to Autonomous Driving：
Similar to L1 (like adaptive cruise control, only a single function automated).
Level 2: Model-Based Reflex Agent
Description：
Has a simple model of the environment, can adjust behavior based on context, but still primarily reactive.
Features：
- Has short-term memory or state awareness.
- Decisions based on rules or simple models, no long-term planning.
Examples：
- Smart customer service bots (selecting responses based on conversation history).
- Robot vacuum cleaners (adjusting path when encountering obstacles).
Comparison to Autonomous Driving：
Similar to L2 (like partial automation, requires human supervision).
Level 3: Goal-Based Agent
Description：
Can understand goals and plan actions, has some autonomy, but relies on clear instructions.
Features：
- Can break down tasks and perform multi-step operations.
- Decisions based on goals, but limited adaptability.
- Usually requires humans to provide specific goals or boundary conditions.
Examples：
- AutoGPT (autonomously calling tools to complete tasks after receiving them).
- Navigation software (planning optimal routes and adjusting in real-time).
Comparison to Autonomous Driving：
Similar to L3 (like conditional automation, can take over in specific scenarios but requires human readiness to intervene at any time).
Level 4: Utility-Based Agent
Description：
Optimizes multi-objective decisions in complex environments, with strong adaptability and autonomy.
Features：
- Can weigh different options and choose the optimal solution.
- Can handle ambiguous instructions or multi-variable tasks.
- Approaches human-level capability in specific domains.
Examples：
- Manus (autonomously completing complex tasks, such as screening resumes and generating reports).
- Advanced recommendation systems (integrating multiple factors including user preferences, time, inventory, etc.).
Comparison to Autonomous Driving：
Similar to L4 (like high automation, human takeover required only in extreme cases).
Level 5: Fully Autonomous Agent
Description：
Operates completely autonomously in open environments, without human intervention, approaching or exceeding human intelligence.
Features：
- Self-defines goals and optimizes over the long term.
- Domain-general capability, adapts to unknown environments.
- Possesses learning, reasoning, and creative abilities.