{
  "id": 3336251,
  "title": "ASAREE: An Analytical Sandbox for Agentic AI Research, Engineering, and Experimentation",
  "url": "https://urgent.news/2026/08/25/asaree-an-analytical-sandbox-for-agentic-ai-research-engineering-and",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-25T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.20.746074v1?rss=1"
  },
  "original_language": "en",
  "account": "Agentic AI platforms allow for the creation of autonomous workflows, yet they lack the ability to conduct experimentation and hypothesis testing. To bridge this gap, ASAREE (Analytical Sandbox for Agentic AI Research, Engineering, and Experimentation) has been developed as an open-source platform. ASAREE enables the creation of agents, connection to MCP servers and tools, and the design of factorial experiments via a visual interface or Python SDK. It meticulously records a full provenance trace for every run and routes all model calls through a provider-agnostic bridge, ensuring compatibility with local deployments and upholding data privacy.\n\nIn a practical application, ASAREE was employed to assess crucial design choices within a multi-agent machine learning pipeline. A 2 x 2 x 2 factorial design was used, examining the impact of more advanced models, increased reasoning effort, and the utilization of a critic agent on key performance indicators. The findings revealed that while more advanced models, greater reasoning effort, and the use of a critic agent led to significant increases in compute time, token usage, cost, and feature count, they did not result in enhanced predictive performance.\n\nAmong the tested models, the most cost-effective baseline emerged as Claude Sonnet 5 with medium effort and no critic agent. This model achieved the highest mean PR AUC, indicating superior predictive performance. In contrast, using the more sophisticated Claude Opus 5 with extra high effort and a critic agent resulted in a 15.5x increase in cost and a 13.1x longer runtime, while simultaneously performing worse on average. These results underscore ASAREE's capability to serve as a robust framework for evaluating the performance and resource efficiency of agentic systems.",
  "summary": "Agentic AI platforms enable the engineering of autonomous workflows but are not designed for experimentation and hypothesis testing. ASAREE (Analytical Sandbox for Agentic AI Research, Engineering, and Experimentation), is an open-source platform to address this gap. ASAREE creates agents, connects to MCP servers and tools, and designs factorial experiments through a visual interface or…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}