Systems | Development | Analytics | API | Testing

The AI agent revolution is transforming how we work, but most analytics platforms are stuck in the past—forcing you to context-switch between your agents and separate BI tools to get data insights. This fragmented approach creates friction, breaks workflows, and ultimately slows down decision-making. When speed matters most, you need your AI agents to seamlessly access and analyze enterprise data without the traditional barriers of maintaining custom integrations and limited API functionality.

By Noam Harel, Co-founder and CMO, ClearML AI is certainly transforming industries, but delivering it at scale is a harder task The shift to enterprise-grade AI isn’t just about building better models. It’s about managing the growing sprawl of infrastructure, tools, and people involved in every phase of your AI production From building and training to production deployment, teams are bogged down by fragmented workflows, manual provisioning, inconsistent environments, and underutilized compute.

The API ecosystem is rapidly expanding into the world of AI. Enterprises are increasingly integrating generative AI services like OpenAI, Claude, and AWS Bedrock into their workflows, but face challenges with secure, governed, and scalable integrations. That’s why Bijira, WSO2’s AI-native API management SaaS platform, introduces AI Gateway support. This is a purpose-built solution to create, expose, and manage AI service integrations as first-class APIs.

As organizations increasingly rely on AI to power their products and services, addressing bias is now a critical responsibility for quality and engineering leaders.

In my previous post, we discussed how we can implement a basic AI Agent with Kong AI Gateway. In part two of this series, we're going to review LangGraph fundamentals, rewrite the AI Agent and explore how Kong AI Gateway can be used to protect an LLM infrastructure as well as external functions.

Exploratory Testing is one of the most valuable practices in software testing, especially when we want to discover issues that impact user experience. But at the same time, it is also one of the most demanding approaches in terms of time and focus. In a world where product development life cycles are becoming shorter and where QA teams need to ensure faster releases without compromising quality, a question arises: How can we gain time without sacrificing the depth of exploratory tests and their findings?

Today, we're announcing the Koyeb MCP Server in public beta to let you interact with your Koyeb resources in natural language. Using the Koyeb MCP Server, LLMs and agents can easily discover and leverage Koyeb primitives to: All of this using your favorite AI assistants like Claude, Cursor, Windsurf or any other applications that support the Model Context Protocol.

Agentic AI: autonomous, goal-seeking systems powered by large language models (LLMs) is rapidly transitioning from novelty to necessity. While developers are building impressive prototypes, many large enterprises remain stuck in pilot purgatory: mesmerized by potential, but unable to translate demos into durable systems that generate value at scale. Two leading visions frame the conversation: Each view highlights an essential dimension of the challenge but neither is complete on its own.

In a significant leap forward for the software testing industry, Tricentis has introduced Agentic Test Automation, an advanced, AI-powered tool set to transform how QA teams create and execute automated test cases. This industry-leading agentic AI solution, directly embedded into Tricentis Tosca, allows teams to generate comprehensive test cases autonomously using natural language prompts, unlocking a new level of efficiency and productivity in software testing.

Automation has long been a key driver of efficiency. Traditional RPA and IDP technology promised to relieve carriers from rekeying, extracting data from forms, and other repetitive tasks. At Appian, we saw early that automation in isolation doesn't achieve transformative outcomes. Why? Because AI has too often been deployed at the edges of workflows: copilots, chatbots, or analytics dashboards that assist us when prompted.