From Code to Confidence: Building AI Apps That Earn User Trust
As 65% of users report issues with AI applications, trust has become the new UX battleground. Learn how developers can build fair, transparent, and reliable AI systems through human-centered testing, inclusive feedback loops, and continuous trust monitoring from day one.
Tabnine Adds Agents Capable of Automating Workflows to AI Coding Platform
Tabnine introduces Tabnine Agentic, a new generation of AI agents that automate multi-step DevOps workflows including refactoring, debugging, and documentation. Built on Tabnine’s Context Engine, these agents bring governance, cost control, and compliance to AI-powered code development.
Rewriting the Rules of Software Quality: Why Agentic QA is the Future CIOs Must Champion
Discover how Agentic QA is transforming enterprise software testing with autonomous AI systems that embed quality into every stage of development.
Before You Go Agentic: Top Guardrails to Safely Deploy AI Agents in Observability
Observability platforms are evolving from passive monitors to active participants. Agentic AI promises a self-healing infrastructure that detects anomalies and fixes issues before users notice, reducing resolution time from hours to minutes. The potential is transformative, turning observability from reactive alerting into proactive, intelligent operations. But with that promise comes risk. Autonomous agents can misdiagnose […]
The Developer’s Guide to Agentic AI: The Five Stages of Agent Lifecycle Management
Discover how AI agents evolve from task executors to adaptive, self-improving systems through Agentic Lifecycle Management, driving agility and innovation.
How Model Context Protocol (MCP) is Fueling the Next Era of Developer Productivity
MCP standardizes how AI agents connect to tools and data, solving fragmentation in AI development with secure, reusable, and scalable integrations.
DevGovOps, A New Play on DevOps, or is It?
DevGovOps is emerging as the next evolution of DevOps, embedding governance into software delivery pipelines. At JFrog’s SwampUP 2025, governance, AI supply chains, and compliance took center stage—positioning DevGovOps as essential for responsible AI adoption.
Survey: Most IT Teams Not Prepared to Manage AI Workloads
A ControlMonkey survey of 300 IT leaders finds most organizations are unprepared to scale AI workloads, citing automation gaps, reliability, skills shortages, and cost challenges. Modernizing infrastructure management with IaC is critical to meeting demand.
Coding at the Speed of AI: Innovation, Vulnerability, and the GenAI Paradox
Generative AI accelerates software delivery but also reintroduces vulnerabilities, making secure coding practices, oversight, and developer training essential for safe adoption.
Developers’ Enthusiasm for Generative AI is Growing: Survey
Programmers say that adopting generative AI is a top priority for their companies and that such investments create openings for new products.
Say ‘No’ to ‘NoOps’: Why We Can’t Afford to Let AI Run Wild
As we anticipate the advancement of AI over the next decade, we must recognize that we are not equipped to grant these tools complete control.