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
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
1702744
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Mariana Raykova (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Output Quality: 1 paper
- Adoption Rate: 1 paper
- Ai Safety And Ethics: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| On-the-fly AI agents often deliver brittle, improvised outputs unsuited to high‑stakes use; embedding disciplined software engineering and a shared 'AI Workflow Store' of hardened workflows can yield far more reliable, reusable, and secure agent behavior, albeit at higher upfront compute and development cost.arxiv | Mariana Raykova provider id |
2026-05-11 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.