Systems | Development | Analytics | API | Testing

Katalon AI Test Execution and False Passes

In a release test I published in July, Katalon’s AI runner reported seven cases passed and four failed. I could open the steps, inspect their screenshots, and watch the browser session. The failed rows showed which expectations broke. The passed rows deserve the same scrutiny: what had the agent observed before it called each step passed? An AI test runner has to perform the action and judge the outcome. A false pass is a passed verdict when the expected behavior did not occur.

10 Best Agentic QA Tools in 2026: Compare Workflows, Ownership and Cost

Your team has more changes to test, more environments to cover and less time to maintain regression suites. AI can help produce tests, but that introduces another workload: deciding whether those tests check the right behavior, whether a repair hides a defect and whether the evidence is enough to support a release. Agentic QA tools aim to take on more of that work, from planning and authoring to execution and failure investigation.

Strategic ROI Frameworks for AI-Driven Quality Engineering

Enterprise software organizations allocate most of their total IT budgets to software validation and quality maintenance. According to the Consortium for Information and Software Quality (CISQ), poor software quality drains over $2.41 trillion annually from the US economy in operational failures, technical debt, and unmitigated production incidents.

Introducing Kong Bot Detector: Native API Bot Detection for Dedicated Cloud Gateways

Bots make up more than a third of internet traffic, and API endpoints attract more of it than web pages do. Scanners hunting for.env files, credential-stuffing scripts, LLM training crawlers, and the long tail of low-effort probes all hit your gateway before they ever reach a service. Every one of those requests costs you — in upstream load, in noise across your analytics, and in the bill.

AI Test Case Generation From Testable Requirements

“Customers can apply a discount code” looks ready for testing. Give that sentence to an AI model and it can produce cases for valid, invalid, expired, and reused codes in seconds. The trouble starts when someone asks what a valid code should do. Which basket is eligible? Does the discount change the displayed total before or after tax? Should the saved order carry the same amount?

Katalon AI for Software Testing: Agent Actions and Evidence

In one release test, my AI agent triaged 43 backlog items into 134 executable cases in Katalon True Platform. The first pass at the steps caused trouble. We had bundled setup, navigation, action, and judgment into long instructions. The runner returned verdicts, but a failed step left me unsure which expectation it had checked. We split the cases into one action and one checkable expected result per step. The failures became specific enough to review against the screenshots and session recording.

Threat intelligence For Products Explained

TLDR: Product threat intelligence comes from using product specific threat intelligence platforms like PCA CERVUS and is specific to individual products and their parts. Most threat intelligence platforms are built for security operations centres (SOCs) and thus they track attacker infrastructure and campaigns aimed at corporate networks. These platforms are designed to help an SOC defend an organization and give the TTPs needed to run red teaming or pen testing against these environments also.

Device-centric threat intelligence and where to find it

TLDR: Device specific threat intelligence is provided by using device specific threat intelligence platforms like PCA Cervus that look at component level risks. Device-centric threat intelligence is threat intelligence that focuses specifically on risks to a connected device like a point of sale terminal or a card reader. This post explains what device centric threat intelligence is and how it differs from traditional SOC centric threat intelligence and what to look for in threat intelligence feed so you can be sure it gives device centric intelligence vs generic SOC focused intel.

Agentic AI Governance: The Complete Guide to Frameworks and Controls

Agentic AI governance is the structured management of the authority an organization hands to autonomous AI systems that plan and take action on its behalf. It covers what an AI agent is allowed to do, which systems it can touch, and how those permissions are monitored, limited, and revoked using robust autonomous AI governance controls.

Accelerating Animal Care with Snowflake CoWork and Cortex AI

Mandai Wildlife Group, the steward of Singapore’s wildlife and nature destination, worked with Infinite Lambda to build an AI agent that lets keepers and veterinarians ask questions in natural language and quickly retrieve years of animal care records. Their AI agent, Animal 360, enables keepers and veterinarians to quickly retrieve decades of animal care records through natural language queries. This innovative solution ensures accurate and efficient data access, enhancing the quality of care for wildlife.