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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2400411797
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Ivan Daunis (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Developer Productivity: 1 paper
- Adoption Rate: 1 paper
- Organizational Efficiency: 1 paper
- Skill Acquisition: 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 |
|---|---|---|---|
| A declarative pipeline language for LLM agents slashes PayPal's agent development time by 60% and triples deployment velocity while keeping orchestration latency under 100ms. The approach concentrates workflow logic in configuration rather than code, enabling non-engineers to adjust behaviors and speeding iteration on production e-commerce agents.arxiv | Ivan Daunis provider id |
2025-12-22 | 5 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 5 cumulative citations. This is a coverage summary, not an author score or h-index.