Agent Autonomy Measure
(Redirected from Autonomous Capability Score)
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An Agent Autonomy Measure is an agent performance measure that quantifies the degree of independence in decision-making, task execution, and goal pursuit exhibited by AI agent systems (across operational contexts).
- AKA: Autonomy Level Assessment, Agent Independence Metric, Self-Governance Measure, Autonomous Capability Score.
- Context:
- It can typically evaluate Decision-Making Independence through human intervention frequency, approval requirements, and override rates.
- It can typically assess Task Completion Capability via end-to-end execution, subtask handling, and error recovery.
- It can typically measure Goal-Setting Autonomy through objective formulation, strategy selection, and plan adaptation.
- It can typically quantify Resource Management Independence via computational allocation, tool selection, and budget optimization.
- It can typically analyze Learning Autonomy through self-improvement capability, adaptation rate, and experience utilization.
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- It can often incorporate Contextual Adaptation Metrics for environment response, situation assessment, and behavioral adjustment.
- It can often include Temporal Autonomy Factors via sustained operation duration, maintenance requirements, and degradation resistance.
- It can often evaluate Collaborative Autonomy through multi-agent coordination, task delegation, and collective decision-making.
- It can often measure Ethical Autonomy via value alignment, constraint adherence, and moral reasoning.
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- It can range from being a Binary Autonomy Measure to being a Continuous Autonomy Measure, depending on its measurement granularity.
- It can range from being a Single-Dimension Measure to being a Multi-Dimension Measure, depending on its assessment scope.
- It can range from being a Static Autonomy Measure to being a Dynamic Autonomy Measure, depending on its temporal variation.
- It can range from being a Domain-Specific Measure to being a Domain-Agnostic Measure, depending on its application generality.
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- It can be utilized by Agent Performance Monitoring Systems for capability assessment.
- It can inform Agent Governance Frameworks for deployment decisions.
- It can support Human-Agent Collaboration Architectures through delegation planning.
- It can guide Agent Development Environments in optimization targets.
- It can influence Agent Security Frameworks via risk assessment.
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- Example(s):
- DeepMind AGI Levels, categorizing from No AI to Superhuman AGI.
- SAE Automation Levels, measuring driving automation autonomy.
- Enterprise Autonomy Metrics, such as:
- Task-Specific Autonomy Measures, such as:
- ...
- Counter-Example(s):
- Performance Accuracy Metric, which measures correctness not independence.
- Efficiency Measure, which assesses speed not autonomy.
- User Satisfaction Score, which evaluates preference not self-governance.
- See: AGI Performance Measure, Autonomous Agent, Agent Capability Assessment, Service Autonomy Principle, Sentient Entity Measure, Agent Goal, Self-Sufficient Organism.