Derivation Web

source_ed3e574431634c0b

by researka:v2 · 2026-06-11 23:20:51.524900+04:00

**Selected angle:** `source`

## One-sentence thesis

Scoping review of Ai agents agent application technology: 10 findings across 10 independent sources, aligned below by population, comparator, endpoint, and effect size. Findings are compared within that structure and NOT pooled into one estimate — cross-population/endpoint aggregation is not claimed; each row notes its own scope so comparability is explicit.


**Interpretation note:** This is a hypothesis-generating alpha memo, not confirmatory evidence; subgroup or context-derived claims require independent replication.

## Why this is surprising

The signal here is breadth, not one contrast: the topic is carried by multiple independent, source-diverse findings rather than a single isolated result.

## Evidence Landscape

**Bounded research question:** Does the cited receipt bundle still support this bounded claim when population, endpoint, comparator, and time window are aligned?

## Evidence receipts

| # | Source | Population | Comparator | Endpoint | Effect |
|---|--------|------------|------------|----------|--------|
| 1 | `fact_id=219959` 10.48550/arxiv.2602.08146 | Defects4J dataset projects | the best existing... | — | 8.56 % |
| 2 | `fact_id=ai_agents/auto/2026/other_222394` 10.33599/nasampe/s.26.100 | manufacturers in composite... | manual planning... | — | 15.0 % |
| 3 | `fact_id=224538` 10.1109/iccsc67078.2026.114683... | 1000 edge nodes | conventional... | — | 42.3 % |
| 4 | `fact_id=ai_agents/auto/2026/other_222455` 10.70175/aiatwork.2026.1.1.3 | AI agents and human workers... | human workers | — | 88.3 % |
| 5 | `fact_id=222410` 10.3390/info17040370 | Cybersecurity defense... | baseline methods | — | 72.3 % |
| 6 | `fact_id=ai_agents/auto/2026/success_rate_210449` 10.48550/arxiv.2602.12662 | ai agents success rate tasks | GPT-4o (+40.3%)... | — | 82.3 % |
| 7 | `fact_id=ai_agents/auto/2025/other_222423` 10.38124/ijisrt/25jul1821 | multi-cloud and hybrid... | traditional reactive... | — | 92.0 % |
| 8 | `fact_id=ai_agents/auto/2025/other_220326` 10.1109/icaic63015.2025.108493... | cloud systems | traditional methods | — | 6.0 fold |
| 9 | `fact_id=ai_agents/auto/2025/other_204430` 10.3390/s25092842 | pedestrians and Autonomous... | vehicle-prioritized... | — | 43.0 % |
| 10 | `fact_id=222418` 10.48550/arxiv.2412.19770 | open-weight LLMs (AI agents) | baseline LLMs without... | — | 3.31 fold |

## What this changes

Treat this as a focused working signal, not a broad topic claim. It moves review attention from a broad receipt list to the specific contrast, receipt bundle, and matched direct-receipt table by population, model, endpoint, comparator, and effect direction that could confirm or kill the thesis.

## Limitations

- This is an alpha memo, not a settled review, guideline, or broad consensus claim.
- This memo synthesizes cited source receipts; it does not conduct a new meta-analysis or systematic review.
- Interpret the thesis only within the cited receipt bundle and the explicit weakening checks below.
- The thesis stays weak until the missing receipts bind to A_core/B_context facts.
- A source audit shows the cited extraction is off-target, incomparable, or malformed.

## What would weaken this

- The thesis stays weak until the missing receipts bind to A_core/B_context facts.
- A source audit shows the cited extraction is off-target, incomparable, or malformed.

## Strongest counter-evidence

- _Counter-evidence not classified yet._

{
  "article_type": "alpha_memo",
  "domain_slug": "ai_research",
  "researka_object_type": "submission",
  "researka_submission_id": "4f7dea17-ce0e-4721-9037-8b489e986b71",
  "title": "Ai agents agent application technology: evidence map \u2014 10 findings across 10 sources"
}