Knowledge Resource
Research Summary: Research with AI Agents: How Agentic Systems Are Changing Scientific Work
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 28 September 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
Research indicates that agentic AI systems are transforming scientific work by automating complex digital research tasks such as literature search, data analysis, and programming. These systems decompose tasks, access various information sources, and execute code, leading to significant speed advantages. The primary shift observed is from human execution of tasks to steering and reviewing AI outputs, with the greatest efficiency gains realized when task behaviors are predictable and verifiable.
Why it matters
The emergence of agentic AI systems fundamentally alters the landscape of knowledge work, enabling unprecedented acceleration and automation of digital research processes. This necessitates strategic re-evaluation of resource allocation, skill development, and competitive advantages across sectors heavily reliant on research and development.
Key insights
- Agentic AI systems can autonomously decompose complex research tasks into subtasks.
- These systems are capable of searching the web, accessing databases, and executing code to perform digital research tasks.
- The use of agentic AI systems enables high-speed execution of digital research tasks.
- The human role in research is shifting from direct execution to steering and reviewing the work performed by AI agents.
- Efficiency gains from agentic systems are maximized when expected behaviors can be formalized and automatically tested.
Source
arXiv — Computers and Society — https://arxiv.org/abs/2609.31219
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- Verification ID
- ASA-EXE-2026-00959
- Version
- v1.0 · r0
- Issued
- 28 September 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- Research with AI Agents: How Agentic Systems Are Changing Scientific Work
- Original authors
- Attribution requires verification
- Original source
- arXiv — Computers and Society
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
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