REVIEW 4 cited by
Characterizing AI Agents for Alignment and Governance
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The creation of effective governance mechanisms for AI agents requires a deeper understanding of their core properties and how these properties relate to questions surrounding the deployment and operation of agents in the world. This paper provides a characterization of AI agents that focuses on four dimensions: autonomy, efficacy, goal complexity, and generality. We propose different gradations for each dimension, and argue that each dimension raises unique questions about the design, operation, and governance of these systems. Moreover, we draw upon this framework to construct "agentic profiles" for different kinds of AI agents. These profiles help to illuminate cross-cutting technical and non-technical governance challenges posed by different classes of AI agents, ranging from narrow task-specific assistants to highly autonomous general-purpose systems. By mapping out key axes of variation and continuity, this framework provides developers, policymakers, and members of the public with the opportunity to develop governance approaches that better align with collective societal goals.
Forward citations
Cited by 4 Pith papers
-
Draining the Energy Commons: Self-Defeating Over-Appropriation as a Coordination Failure in Agentic LLM Collectives
LLM prosumers deplete a shared renewable reserve exactly when demand exceeds peak replacement, acting like impatient open-access users even when sustaining the reserve is feasible.
-
Simple Prompt Injection Attacks Can Leak Personal Data Observed by LLM Agents During Task Execution
Prompt injection can make LLM agents leak personal data they observed while executing tasks, with measured attack success rates around 15-20 percent and password leakage much rarer.
-
Embodied AI: Emerging Risks and Opportunities for Policy Action
A policy analysis arguing that embodied AI risks are real, under-covered by current US/EU/UK frameworks, and best handled through certification, benchmarks, clarified liability, and economic adaptation.
-
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
A qualitative taxonomy positions vibe coding and agentic coding as complementary paradigms rather than rivals in AI-assisted software development.
Discussion (0). Continue with ORCID to comment.