Systems | Development | Analytics | API | Testing

With the rise of AI use in vehicle software development, concerns arise around its presence in safety-critical applications, especially when it comes to functional safety and regulatory compliance. ISO 26262, the essential standard for automotive development that requires processes for managing, designing, and verifying safety-critical systems, still applies. However, it can fall short when applied to AI models, which are inherently non-deterministic and continuously evolving.

Every enterprise moving AI into production is about to face a familiar problem in an unfamiliar form: the cost explosion, but for LLMs. This is *very *similar to what happened with cloud. In the early days of cloud, teams spun up infrastructure with no visibility into who was consuming what. Finance got the bill. Engineering got the blame. No one had the data to make good decisions. It took years of hard-won FinOps discipline to fix that. LLM spend is on the same trajectory *and moving faster*.

In 2026, APIs have moved far beyond simple integration points. They’re now strategic business assets powering AI transformation, microservices architectures, and multi-cloud ecosystems. But a critical challenge threatens to undermine digital initiatives: the fragmentation of API testing. As organizations rush to deliver faster, they’re discovering that their testing infrastructure – cobbled together from disparate tools and disconnected processes – has become the bottleneck.

Katalon is introducing a new MSP Program designed for our official solution and service partners. Built for partners delivering QA services across multiple customer engagements, the True Platform MSP Program offers a more flexible way to scale delivery with Katalon’s all-in-one testing platform.

The Model Context Protocol (MCP) is quickly becoming the standard for how large language models connect to enterprise data. As adoption accelerates, engineering teams face a foundational decision: build a custom MCP server from scratch, or adopt an AI data gateway that ships with MCP support, security, and governance out of the box. Both paths have real tradeoffs. This post breaks them down so you can make the right call for your stack, your team, and your risk profile.

The quality tax isn't primarily a resourcing problem. It's a paradigm problem. And paradigm problems don't yield to incremental solutions.

Most founders have a build problem. Very few have solved the distribution problem. That gap is where startups die. When someone comes to me with a startup idea (and this happens more than you’d think), I always ask the same question first.

Audit logs are essential for making AI systems accountable, reliable, and compliant with regulations. They act as a record-keeping system, documenting every critical interaction within an AI system, such as user prompts, model decisions, and policy enforcement. Here's why they are crucial: Audit logs are not just a legal requirement - they are a key part of managing AI systems effectively and minimizing risks.

The best platforms for validating and handling errors in CSV files combine schema enforcement, real-time error detection, and automated remediation within a unified pipeline. Integrate.io ranks as the top choice for data teams that need enterprise ETL solutions for seamless CSV handling and error detection, offering a no-code interface, robust pre-load validation, and deep connector coverage.

AI workflows for manufacturing need to be deployed quickly. Quality control systems, predictive maintenance tools, and supply chain optimization algorithms may be going live, yet compliance infrastructure is lagging behind. The result is a familiar pattern: pilots that prove out technically but stall before production because they can’t clear audit, safety, or regulatory review.