Systems | Development | Analytics | API | Testing

Synthetic monitoring has been a critical part of application reliability for years. It gives engineering and operations teams a way to proactively test applications, APIs, and critical customer journeys before users encounter problems. But there is a fundamental limitation with the traditional approach: Someone has to create the tests. As applications become more distributed and customer journeys become more complex, organizations can end up maintaining hundreds or even thousands of synthetic scripts.

Katalon's library of testing skills for AI agents holds 13 skills. Building it produces 124 files. The 13 skills are the actual work: how to plan a test cycle, how to design cases from a requirement, how to read a failing run and decide whether a release is safe. The other 111 files are packaging. Four of them arrived this month and they are the subject of this post.

A faster, efficient, more capable Astera with same licensing cost Astera 11.2 is available now for every product on the platform: Centerprise, ReportMiner, EDI Connect. High-volume jobs process up to 2x faster, extraction templates are generated under five seconds with AI-powered Auto-Generate Layout, and Dataflows can run a full retrieval-augmented generation (RAG) pipeline without a separate tool. The release also simplifies deployment, and adds email-driven workflow triggers.

Ask Claude for your average sales cycle today. Ask again next Tuesday. Same prompt, same MCPs, same playbook. There is a real chance the two numbers do not match. The reason is not exciting, and it costs you decisions. If you have wired Claude to your CRM, product, and finance tools through MCPs and given it a detailed playbook, you are already ahead of most companies asking whether AI can analyze the business. The gap between that and running the company off the answers is real, though.

We are happy to announce the general availability of version 9.18 of Yellowfin BI, our business intelligence and embedded analytics platform. This release marks a step forward in how organizations and ISVs can combine the power of modern AI models with an auditable data analysis process.

dbt Wizard brings conversational AI to analytics and context engineering, grounded in your dbt project’s lineage, compiled state, tests, and semantics.

Misconceptions about microservices load testing are common, especially for teams moving from monolithic systems. It’s tempting to believe that simply scaling infrastructure or running legacy load tests will reveal the same issues and provide actionable results. However, distributed, cloud-native architectures introduce new complexities and risks that demand a different approach.

Design, document, and verify APIs across OpenAPI, AsyncAPI, GraphQL, and MCP. gRPC and A2A support coming soon.

Bob Dylan wrote those words in 1964 about a world in flux - where the old rules were being quietly rewritten, and the people who hadn't noticed yet were about to find out the hard way. He wasn't thinking about enterprise data architecture. But if you've been following SAP's moves on data access, extraction, and platform strategy over the past two years, those words might be landing a little closer to home than usual.

Most teams building multi-agent systems hit the same wall at roughly the same point. The prototype works. Agents chain together, tasks complete, the demo impresses the room. Then someone asks: "What happens when this runs a thousand times a day? What happens when an agent calls an external API that's down? How do we know what the agents actually did?" That's when the architecture conversation starts. Here's the framing that clarifies most of these questions.