The AI ecosystem is increasingly focused on developing effective AI agents that offer economic advantages while posing security and reliability risks. Current AI agents embed general-purpose models allowing them to manipulate tools and perform complex actions beyond text. However, there is a lack of a comprehensive taxonomy to classify these tools, which could enhance communication among developers and users about capabilities and limitations. An AISIC workshop with 140 experts proposed approaches for structuring a taxonomy, focusing on functionality, access patterns, risk, reliability, modality, monitoring, and autonomy.
These diverse methods aim to clarify the actions AI tools enable and the constraints, such as permissions in trusted versus untrusted environments. By creating structured taxonomies, stakeholders can better assess AI agent capabilities, facilitating risk assessments and improving transparency in development and deployment. Engagement and feedback are encouraged to refine these frameworks and support the AI agent value chain.
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