Articles from Source: The-New-Stack

Companies are rapidly adopting agentic AI, but nearly 50% of deployments fail due to poor ROI. Instead of creating a single 'super agent,' businesses should develop specialized agents for specific tasks, like data entry or information retrieval. This approach improves efficiency and allows for better management and transparency. Using multiple agents can enhance collaboration and productivity, as seen with AIG's use of 80 agents for underwriting. #AI #Automation #BusinessStrategy #TechTrends

The concept of "vibe coding" suggests that you can describe software needs in plain English and AI will generate the code. However, experts like James Gosling and Simon Ritter highlight significant challenges. Firstly, many AI coding tools rely on existing code, which often lacks quality. Training AI on mediocre code leads to mediocre results. Secondly, natural language is inherently ambiguous, making it difficult for AI to interpret instructions accurately. This ambiguity is why programming...

In the era of digital transformation, AI agents are changing how businesses operate. They allow companies to enhance speed while maintaining human connection. 🌐 A strategic approach is essential for building effective AI agents. The article outlines a five-step process to guide businesses in defining their agents' roles, equipping them with relevant data, and ensuring they meet specific goals. 📈 By understanding customer needs and journey, companies can leverage AI to improve customer support...

Discover how to leverage local AI in the Zed IDE! 🌟 Zed is a cross-platform code editor designed for collaboration with AI. Currently available for Linux and macOS, it offers both free and Pro versions. The Pro plan enhances AI support significantly. Key features include GPU rendering, Git support, and real-time collaboration. For AI integration, users can connect Zed with local language models like gpt-oss via Ollama. Ready to enhance your coding experience? #AI #ZedIDE #Programming...

The Model Context Protocol (MCP) is gaining traction as a standard for connecting AI agents with external data and services. Since its launch by Anthropic, over 16,000 MCP servers are now listed. MCP excels in complex environments, enhancing scalable, multitenant platforms. However, it's not ideal for every use case; static contexts and strict security often favor traditional API calls. Experts emphasize that while MCP offers advantages for agentic workflows, caution is needed due to...

Understanding the difference between AI Agents and Agentic AI is crucial for developers in Kubernetes environments. AI Agents are individual software entities that perform specific tasks, similar to microservices. They operate on a request-response basis and often rely on external tools for enhanced functionality. In contrast, Agentic AI involves multiple AI agents collaborating through orchestration, adding complexity and capability. This comparison helps clarify architectural patterns for...

Introducing Qodo Aware, a tool designed to enhance AI coding agents with improved context awareness. While most AI coding agents rely on large language models, Qodo Aware stands out with its context engineering capabilities. It integrates information from various platforms like GitHub and Bitbucket to provide a comprehensive understanding of codebases. The tool features three main agents: 1. **Context Retrieval** - Uses semantic search to find relevant code snippets. 2. **Deep Research** -...

🚀 Clockwork has launched FleetIQ, targeting the network bottlenecks in AI workloads. As GPU speeds increase, network efficiency becomes crucial. 🌐 FleetIQ provides visibility into GPU clusters, helping identify issues across systems. This can save valuable time during training processes disrupted by network errors. 🔧 The platform also offers stateful fault-tolerance and automatic performance optimizations to enhance uptime and reduce congestion. #AI #Networking #TechInnovation #CloudComputing...

🚀 JFrog is enhancing AI tooling and governance to streamline software delivery. At their swampUP 2025 conference, they introduced key initiatives aimed at simplifying AI-infused software supply chains. Key highlights include: 🔹 **DevGovOps**: A focus on evidence-based controls for software releases. 🔹 **AI Catalog**: A governed marketplace for AI and ML models. 🔹 **Agentic Code Remediation**: Automated fixing of vulnerabilities. 🔹 **JFrog Fly**: A new approach to versioning and release...

The article discusses the varied adoption of AI-coding tools among software developers. Some report increased productivity, while others question their value and express concerns about tech debt. Key issues include lack of context, a steep learning curve, and loss of tribal knowledge within enterprises. As remote AI-coding environments gain traction, the need for improved developer-AI collaboration becomes clear. #AICoding #SoftwareDevelopment #TechTrends #EnterpriseTech #Collaboration 🤖💻🔍

As AI transforms industries, IT leaders face new challenges with the rise of large language models (LLMs). 📈 Decentralized innovation is speeding up AI adoption, but it also leads to risks like data exposure and uncontrolled spending. Enterprises need a solution to manage LLM usage effectively. 💼 An AI gateway can provide centralized governance and control, ensuring secure, cost-effective access to AI services. This tool helps track usage and enforce policies without stifling innovation. 🔒...

The software development landscape is evolving with the rise of "vibe coding," a term coined by AI researcher Andrej Karpathy. This approach shifts developers from traditional coding to orchestrating AI-driven workflows. While early adopters report significant productivity increases, a key challenge arises: many organizations struggle to understand what they’re deploying. Recent data from Stack Overflow shows that 76% of developers use AI tools, yet there's a disconnect in measuring...

AI agents are transforming various industries like finance, healthcare, and retail. These systems go beyond traditional software by continuously learning and adapting to improve performance. 🤖 They operate in a cycle of data gathering, analysis, decision-making, and action execution. Different types include reactive, deliberative, learning, and hybrid agents, each tailored for specific tasks. 📊 As AI agents become integral to enterprise operations, they introduce new complexities that need...

AI co-pilots are transforming how we work, offering tools for coding, writing, and design. However, many of these systems operate in isolated silos, limiting their effectiveness. To unlock greater value, AI agents need access to broader organizational data, enabling them to provide deeper insights and recommendations. The challenge lies in ensuring these agents work seamlessly across platforms, enhancing collaboration and decision-making. #AI #DataSilos #TechInnovation #Collaboration...

AI development is facing challenges reminiscent of the struggles from 1996. Despite advancements like large language models, many organizations remain stuck in basic issues of tool selection and system integration. Currently, 70% of companies are in the experimentation phase of AI, with only 10-15% realizing significant benefits. Those leading in AI are seeing notable revenue growth, with a focus on retrieval-augmented generation (RAG) as a primary approach. As AI agents gain traction,...