Systems | Development | Analytics | API | Testing

Best AI test automation tools for fast, high-quality releases

The promise of test automation was simple: automate repetitive testing tasks, catch bugs faster, and ship quality software at scale. Yet for most development teams, that promise remains unfulfilled. Traditional test automation frameworks demand specialized coding skills, require constant maintenance when applications change, and create bottlenecks that slow down release cycles rather than accelerate them.

Leveraging the MCP Registry in Kong Konnect for Dynamic Tool Discovery

As enterprises start deploying AI agents into real systems, a new architectural challenge is emerging. Agents need a reliable way to discover tools, services, and capabilities dynamically, instead of relying on hardcoded integrations. This is where the Model Context Protocol (MCP) ecosystem is rapidly evolving. MCP servers expose tools and capabilities that AI agents can use. However, once organizations begin deploying multiple MCP servers across environments, the question becomes clear.

Enterprise AI Infrastructure Security Series - 3) Configuration Governance with Administrator Vaults

Securing ClearML for the Enterprise — Part 3: Configuration Governance with Administrator Vaults In this video we walk through ClearML's vault system — how personal vaults and administrator vaults work, and how administrator vaults let you enforce platform-level policies on storage locations, container images, and credentials across your teams and service accounts.

Why Databox MCP Wins for AI Analytics Over Individual Connector MCPs

The Model Context Protocol (MCP) has given AI assistants something they’ve never had before: a standardized way to pull live data from external systems. Instead of just generating text, an AI agent can now query your CRM, check ad performance, or pull revenue numbers in real time. The industry’s response has been predictable. Every major platform is racing to build their own MCP server.

AI Coding Agents Have a UX Problem Nobody Wants to Talk About

The pitch was simple: let AI write your code so you can focus on the hard problems. Three years into the AI coding revolution, and developers are focused on hard problems alright, just not the ones anyone expected. Instead of designing systems and solving business logic, engineers in 2026 spend a startling amount of their day managing the AI itself. Should you use Fast Mode or Deep Thinking? Haiku or Opus? Cursor or Claude Code or Windsurf? Should you write a SKILL.md file or a custom system prompt?

Ep 64 | AI Managed Services: A Smarter Path for SMEs

AI adoption is accelerating across small and medium-sized enterprises (SMEs), but many businesses lack the in-house expertise to build and manage AI infrastructure effectively. In this episode of The AI Forecast, Paul Muller speaks with Hyve’s Marketing and Operations Director, Charlotte Webb, about how managed service providers (MSPs) are reshaping AI adoption for SMEs. They explore the build vs. buy debate in AI solutions and why cloud computing alone doesn’t guarantee lower costs, better performance, or compliance.

WSO2 AI Gateway: Prompt Management & Semantic Caching

Learn how to ensure consistent AI interactions and drastically reduce latency using the WSO2 AI Gateway. This step-by-step tutorial demonstrates how to standardize your LLM requests for quality and efficiency while cutting down on redundant API costs. We explore "Prompt Management" to enforce organizational guidelines using templates and decorators, and "Semantic Caching" to leverage vector embeddings—serving instant, cached responses for semantically similar queries to minimize expensive LLM calls.

How AI Is Redefining Route Optimization to Enable Faster Deliveries?

When executives talk about improving logistics performance, the conversation often circles around the same three goals: speed, cost efficiency, and reliability. Yet the reality on the ground tells a different story. Traffic congestion, rising fuel costs, driver shortages, changing customer expectations, and unpredictable disruptions continue to make route planning one of the most complex operational challenges in logistics. Now add one more pressure point: customer expectations have fundamentally changed.