Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2424171795
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Andriy Podstavnychy (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Adoption Rate: 1 paper
- Regulatory Compliance: 1 paper
- Research Productivity: 1 paper
- Ai Safety And Ethics: 1 paper
- Firm Productivity: 1 paper
- Task Completion Time: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| AI agents’ risks hinge on their execution histories, so static prompts and access controls cannot reliably enforce path-dependent rules; firms must evaluate actions at runtime, a change that raises latency, engineering and compliance costs and reshapes markets for governance, insurance and enterprise adoption.arxiv | Andriy Podstavnychy provider id |
2026-03-17 | 11 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 11 cumulative citations. This is a coverage summary, not an author score or h-index.