The article reviews the emergence of Agentic AI, covering its evolution, theoretical foundations, working principles, and architectural aspects. It surveys recent scholarly contributions across various domains, highlighting real‑world applications, current research findings, and existing challenges. The review also proposes a framework for stakeholder adoption and outlines future research directions to guide researchers and practitioners.
By AKM Bahalul Haque, Al Amin Islam Ridoy, Mohammad Rayhan, Ivan Porres
A minimal OpenAI Agents SDK implementation where retrieval becomes a search-read-decide loop The post Agentic RAG: Let the Agent Search appeared first on Towards Data Science .
By Shuai Guo
Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure.
By Baolin Peng, Wenlin Yao, Qianhui Wu, Hao Cheng, Jianfeng Gao
The article "Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks" surveys the lack of a standard definition for AI agents and organizes this ambiguity into five dimensions: environmental interaction, learning and adaptation, autonomy, goal‑directed behavior, and temporal coherence. It reviews how each dimension has been conceptualized in prior work and compiles the metrics, benchmarks, and evaluation frameworks used to assess them. The authors also introduce the Agent Compendium, a public digital resource that extends these evaluation methods, aiming to provide a common structure for evaluating and comparing agent capabilities across AI systems.
By Mia Lassiter, Brinnae Bent
arXiv:2607. 28229v1 Announce Type: cross Abstract: The web is increasingly accessed by AI agents rather than humans.
By Luigi Sigillo, Matteo Silvestri, Francesco Tabaro, Rajat Bhatnagar, Syed Irtaza Mubashar, Matt Jeffryes, Daljit Nijjer, Vittorio Perera, Ola Spjuth, Julio Saez-Rodriguez, Melissa Harrison, Fabio Petroni
Computer scientist Phillip Isola cuts through the hype to explain how AI agents work and what the future might hold for this rapidly advancing technology.
By Adam Zewe | MIT News
The article titled "Is Agentic AI Just Automation?" argues that many so‑called agents are merely flowcharts in disguise. It explains why this misconception exists and suggests what kinds of systems should be built instead to achieve true agentic AI.
By Prashant Mudgal
“SceneSmith” system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores.
By Alex Shipps | MIT CSAIL
Learn how enterprises can manage AI investments in the agentic era by measuring useful work per dollar, improving efficiency, and scaling high-value workflows.
The paper introduces ARC, a lightweight hierarchical policy that learns to configure LLM‑based agent systems on a per‑query basis by treating each configuration as a temporally extended option in a semi‑Markov decision process. Unlike fixed templates or hand‑tuned heuristics, ARC dynamically selects workflows, tools, token budgets, and prompts tailored to the difficulty of each query. Experiments on reasoning, tool‑use, and agentic benchmarks show that ARC outperforms budget‑matched tool‑augmented LLMs, boosting reasoning accuracy by 31.3%, tool‑use accuracy by 13.95%, and doubling success on the τ‑Bench Airline Pass task from 9.0% to 18.0%.
By Aditya Taparia, Som Sagar, Ransalu Senanayake
We introduce PaperBench, a benchmark evaluating the ability of AI agents to replicate state-of-the-art AI research.