AI Agent Tool Taxonomies Unveiled at CAISI NIST Workshop
CAISI and NIST hosted a workshop with 140 experts to develop taxonomies for AI agent tools, addressing functionality, risk, and access patterns.
January 2024 — Approximately 140 AI experts convened at a workshop hosted by the Consortium for AI Safety and Innovation (CAISI) and the National Institute of Standards and Technology (NIST) to address the growing need for standardized taxonomies of tools used in AI agent systems. The event, part of the AI Safety and Innovation Consortium (AISIC), aimed to create a shared vocabulary for developers, deployers, and researchers to improve transparency and risk management.
Key Takeaways from the Workshop
Participants identified multiple approaches to categorize AI agent tools, including:
- Functionality-focused: What actions does the tool enable?
- Access patterns: Can tools access external resources or be configured with write permissions?
- Risk-based: How critical is the tool to potential harms? Are actions reversible?
- Reliability: Can the tool be used consistently?
- Modality: Is the tool text-based, robotic, or multimodal?
- Monitoring: What level of observability does the tool provide?
- Autonomy: How much discretion does the agent have in tool use?
Workshop participants emphasized that no single taxonomy suffices. Instead, multidimensional frameworks combining these approaches may offer the most promise.
Proposed Taxonomies
1. Functionality-Oriented Taxonomy
| Purpose | Type | Examples |
|---|---|---|
| Perception | Sensors | Internet search, diagnostics, voice input |
| Reasoning | Planning | Task-decomposition models |
| Action | Physical extensions | Robotic arms, laboratory tools |
This taxonomy helps developers communicate capabilities and constraints, such as filtering risky search results.
2. Constrained Tool Access Patterns
| Tool Permissions/Environment | Read Only | Constrained Write | Write |
|---|---|---|---|
| Trusted Environments | RAG | Application-specific GUI | Coding agent in a trusted repo |
| Untrusted Environments | Deep research | Browser use | Computer use |
This framework complements risk assessments, aligning with resources like NIST AI 600-1.
Next Steps
CAISI and NIST encourage stakeholders to adapt these taxonomies and provide feedback via CAISI-agents@nist.gov. The effort underscores the importance of transparency in AI agent development as tools grow more sophisticated and pervasive.
"Tool taxonomies are one method to improve transparency on capabilities and deployments along the AI agent value chain," the organizers noted.
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About the Author
Michael Rodriguez
AI Technology Journalist
Veteran technology journalist with 12 years of focus on AI industry reporting. Former AI section editor at TechCrunch, now freelance writer contributing in-depth AI industry analysis to renowned media outlets like Wired and The Verge. Has keen insights into AI startups and emerging technology trends.