Systems | Development | Analytics | API | Testing

AI agents move fast. Securing them shouldn't slow you down. Kong and Noma are partnering to change that. This program delivers high-quality, reliable integrations providing real business value for customers..

Cloud testing security remains a source of confusion for many IT teams. It’s not simply about protecting your test environment, nor is it interchangeable with general cloud security. In the context of load testing and performance testing, cloud testing security means safeguarding the data, assets, and processes involved in evaluating how your website or API performs under stress, all within a cloud-based environment.

In the first two articles of this series we showed what the OctoPerf MCP Server does. This one is for the builders: how we designed it, and specifically how we kept its token cost under control. Because here is the thing nobody tells you when you start writing a Model Context Protocol server: the hard part is not exposing your API to an LLM. The hard part is not exposing too much of it.

AI runs on data, and global enterprises are awash with petabytes of data. That might suggest that it’s easy for companies to advance their businesses through the power of AI. Yet enterprise data is often fragmented across departmental and technological silos, and that data is often inconsistent, ungoverned and disconnected from mission-critical systems. As a result, many AI initiatives stall before they can deliver operational value, and the root cause is rarely the model.

The EU AI Act's general provisions are already in force, and high-risk AI system obligations apply from August 2026. The National Institute of Standards and Technology (NIST) AI Risk Management Framework and its Generative AI Profile set the baseline for what auditors expect, framing governance around four functions: identify, measure, manage, and monitor. Deploying artificial intelligence (AI) agents in regulated environments isn't a sandbox experiment anymore. It's a strict governance challenge.

Moving a retrieval-augmented generation (RAG) prototype from a Python notebook into production isn't an API orchestration challenge. It's a distributed systems problem. For engineering managers and data platform leads, the build-versus-buy decision on streaming infrastructure will dictate your artificial intelligence (AI) feature velocity for the next three to five years. This guide assumes you've already prototyped a RAG pipeline.

The gap between how fast organizations can generate software and how confidently they can validate it is becoming one of the defining operational risks of this era.

Custom AI agents via MCP (Model Context Protocol) let an autonomous QA system reach beyond its built-in skills by connecting to external tools such as GitHub and browser automation services. In practice, that means a QA agent can inspect source code changes, identify new features, compare them against existing test coverage, and create missing test cases automatically. For teams managing growing test suites, this turns AI from a closed assistant into a connected workflow engine.

Your AI agent is mid-task, waiting on the result of a search tool call it made 30 seconds ago. The user is watching a spinner. Then a network blip drops the connection. The application reconnects in under a second, fast enough that most monitoring wouldn't flag it. But the tool call result that came back during the gap is gone, and so are the 200 tokens the agent generated before the silence began. The reconnect succeeded - but the session didn't.

Generative AI for QA is the use of large language models to accelerate the creation and analysis of testing artifacts — drafting test cases, summarizing requirements, and generating synthetic test data. AI agents extend that capability into multi-step autonomous workflows that plan, delegate, and execute testing tasks across an entire delivery pipeline. For SDETs, the shift is not about learning to prompt more cleverly.