Systems | Development | Analytics | API | Testing

In this post, Kong Champion Sachin Ghumbre shares his journey of transforming a complex GenAI application from a state of operational challenges to streamlined control. Discover how Kong AI Gateway provided the enterprise-grade governance needed to secure, optimize, and scale his GenAI solution, tackling issues from escalating LLM costs to prompt injection risks.

OpenAI's new Agent Builder is revolutionizing how businesses create AI-powered workflows with its intuitive drag-and-drop interface. But here's the challenge that every enterprise faces: how do you give your agents access to internal databases without exposing credentials or compromising security?

In an era when every second counts, enterprises that can act on information the moment it arrives are positioned to win—and real-time streaming data is the fuel that brings artificial intelligence (AI) to life. Powering agentic AI and advanced analytics can’t be done with static or delayed data; organizations need a comprehensive, reliable supply of streaming data representing their entire businesses.

AI doesn’t just learn from data, it learns from us, and we are far from perfect. When it scrapes the internet for knowledge, it also absorbs our biases, blind spots, and noise, shaping how it interprets the world..

By Erez Schnaider, Technical Product Marketing Manager, ClearML The GPU-as-a-Service market is experiencing hyper growth. Yet across telecommunications companies, cloud service providers (CSPs), and enterprise organizations, GPU infrastructure has been viewed as a necessary cost center rather than a strategic asset. This perspective is changing as energy optimization technologies and multi-tenant capabilities transform GPU infrastructure into monetization engines and competitive differentiators.

When you think about test automation, what image comes to mind? For many QA leaders, automation still means running the same scripts every night, chasing down false-positives, and fighting maintenance debt. That model served us well for a while, but it was always limited: automation only runs what humans script. The next era is about AI-powered testing. AI-powered testing doesn’t just execute predefined tasks; it generates coverage dynamically, adapting as your application evolves.

Traditional test scripts are too brittle for today’s fast-moving, complex systems. AI-powered agents enable a shift to scenario-based testing - high-level, reusable flows that describe user intent and behavior. Agents can help extract, generate, and evolve these scenarios, while humans guide relevance, risk, and validation. This approach improves stability, cross-platform coverage, and business alignment.

‍ For many enterprises, their legacy systems are becoming more of a liability than an asset, slow to change, expensive to maintain, and risky in today’s security climate. But the rise of cloud infrastructure isn’t just hype: 94% of enterprises report using cloud services in some form by 2025. Moreover, the cloud‐first mindset has gone mainstream. According to Gartner, 85% of organizations will embrace a cloud‐first strategy by 2025.

‍ 2025 is turning out to be a big year for AI agents. Grand View Research notes that the global AI agent development market, worth about USD 5.40 billion in 2024, could soar to nearly USD 50.31 billion by 2030, growing at a remarkable 45.8% CAGR. And it’s not just about numbers. A recent McKinsey report highlights that agentic AI is the next logical step after generative AI. Evolving from simple task execution to actively driving goals and delivering results that make a measurable difference.