Your AI Strategy Needs A Rebuild Before Agents Break It #AI agents are moving from pilot projects into real business roles, but many companies are discovering that their #cloud #infrastructure, #security models, and #workflows were built for people, not #autonomous systems.
AGENTS
-
GPT with LangGraph: Multi-Agent Systems and Advanced AI Applications
By
–
GPT with LangGraph! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode The rapid advancement of large model technology is leading to an increasing application of agent technology across various fields and industries significantly transforming how people work and live. In complex and dynamic environments, multi-agent systems are able to tackle intricate tasks that would be challenging for a single agent, thanks to their collaborative and division-of-labor approaches. The following stack of research papers and hands on tutorials highlight the integrated use of GPT with LangGraph and CrewAI. LangGraph enhances information transmission efficiency through its graph-based structure, while CrewAI boosts team collaboration and system performance via intelligent task allocation and resource management. The key areas of this research include: The design of agent architectures based on LangGraph for precise control . The enhancement of agent capabilities through CrewAI to tackle a range of tasks. The goal of this study is to explore the combined potential of GPT and LangGraph and CrewAI in multi-agent systems, offering fresh insights for the ongoing evolution of agent technology and fostering innovation in the application of large model intelligent agents. References Duan, Z., & Wang, J. (2024, November 27). Exploration of LLM multi-agent application implementation based on LangGraph+CrewAI. arXiv. Retrieved March 9, 2025, from arxiv.org/abs/2411.18241 Horsey, J. (2025, March 9). Build a powerful Python chatbot in minutes with LangGraph. Geeky Gadgets. Retrieved March 9, 2025, from geeky-gadgets.com/build-a-po… Ong, R. (2024, July 10). GPT-4o and LangGraph tutorial: Build a TNT-LLM application. DataCamp. Retrieved March 9, 2025, from datacamp.com/tutorial/gpt-4o… Sivan, V. (2024). Building AI agent systems with LangGraph. Medium. Retrieved March 9, 2025, from medium.com/pythoneers/buildi… Wang, J., & Duan, Z. (2024, December 2). Intelligent Spark agents: A modular LangGraph framework for scalable, visualized, and enhanced big data machine learning workflows. arXiv. Retrieved March 9, 2025, from arxiv.org/abs/2412.01490
→ View original post on X — @gp_pulipaka, 2026-04-14 04:57 UTC
-
Manus: Gen AI Agent for Big Data Analytics and Machine Learning
By
–
Manus: Gen AI Agent! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Manus-AI-Agent -
Multiagent Learning Algorithms with Large Language Models
By
–
Multiagent Learning Algorithms with Large Language Model! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/MultiAgent-LLM
→ View original post on X — @gp_pulipaka, 2026-04-14 04:00 UTC
-
Enterprise Search: Deploying NVIDIA AI-Q for Secure Production
By
–
Moving enterprise search from a prototype to a secure, full-scale production environment is a complex hurdle. 🔍 There is a great step-by-step blueprint available for deploying the open-source NVIDIA AI-Q template to build scalable, "agentic" search stacks integrating private data. Read the Blog: developer.nvidia.com/blog/ho… #EnterpriseAI #LangChain @nvidia
→ View original post on X — @haroldsinnott, 2026-04-14 04:00 UTC
-
AI coding agents viable after model threshold crossed
By
–
It is going to be like what happened in coding: as soon as models crossed a certain threshold (Opus 4.5, GPT-5.2, Gemini 3), suddenly Claude Code & Codex were viable. Before that, it was all about coding assistance, afterwards it was all about agents from relatively small gains.
-
Building AI Agents: Rethinking the Foundational Approach
By
–
Why "Build an #AIAgent" Is the Wrong Starting Point for #AI Systems
by Trillo AI @hackernoon Learn more: https://
bit.ly/4vmWouU #GenerativeAI #ArtificialIntelligence #MachineLearning -
AI Scientist Paper Published in Nature, Interview with Yutaro Yamada
By
–
An interview article with Yutaro Yamada of Sakana AI, one of the lead authors of the AI Scientist paper recently published in @Nature magazine, has been published in Nikkei Digital Governance. nikkei.com/prime/digital-gov… In this article, Mr. Yamada provides a comprehensive explanation of the current state of AI-driven scientific research, covering both the achievements of AI Scientist and its challenges. As mentioned in the article, one of the important findings from this research is that we experimentally demonstrated that the quality of generated papers can improve as the performance of the underlying AI model improves. "Being published in a top-tier scientific journal means that Sakana AI's paper-creation system has been recognized as having 'passed' the scientific version of the Turing test." The article also accurately addresses the current limitations of AI Scientist. What matters to us is not just what today's models can do. The Nature paper's achievement is that we quantitatively tracked the relationship between model performance and research quality, thereby revealing what could be called "scaling laws for scientific research." We believe this provides valuable insights for broader discussions about the future of science. "In the paper published in Nature this time, we demonstrated that if the performance of the AI model underlying the system improves, there is potential to enhance the quality of generated papers, and future developments are expected. Yamada of Sakana AI states: 'I want to improve the quality of automated research and help human researchers achieve surprising results.'" The article also mentions that AI-generated papers are watermarked and that the experiments were conducted under the approval of ethics committees and academic societies. In an era when AI is significantly transforming the nature of science, Sakana AI will continue to explore the possibilities of AI in science through ongoing dialogue with the scientific community. Blog: sakana.ai/ai-scientist-natur… [Translated from EN to English]
-
DeepMind hires philosopher to investigate AI consciousness implications
By
–
DeepMind has hired a philosopher to study AI consciousness. I'm really curious about what exactly they've observed internally.
-
Best Edge AI Award: Local VLM Multi-Rover Mars Explorer
By
–
Won best edge AI at the @ycombinator and @innate_bot hackathon! We built a local VLM multi-rover orchestrator for Mars exploration. On-device navigation and automated fault detection & recovery across odometry, stereo vision, and lidar. Thanks for hosting, @ax_pey!
→ View original post on X — @scobleizer, 2026-04-14 02:00 UTC