Systems | Development | Analytics | API | Testing

Securing Non-Deterministic Systems: Comprehensive AI Security Testing for Enterprises

Enterprise QA teams are discovering that deploying machine learning models breaks their existing validation pipelines. Legacy testing environments rely on a simple truth: fixed inputs must produce predictable outputs. Because intelligent architectures operate on probabilistic distributions, deterministic testing alone can no longer guarantee reliability. When conducting a code review or architectural risk assessment, treating an active model as a standard black-box API leaves critical flaws unaddressed.

How to Monitor Your MCP Server (Step-by-Step Guide)

MCP servers have quietly become production infrastructure. If you have shipped a Model Context Protocol server so that Claude, an agent, or a connector can call your tools, that endpoint is now on the critical path. When it goes down, or starts returning errors, your users feel it the same way they would feel any API outage. The difference is that most teams are not watching it yet.

The Reason Your Tests Are Flaky And How to Fix It Using Keploy

Ever had an API test fail even though nothing in your code actually changed? That's a noisy field problem and it's one of the most common causes of flaky tests. In this video, we break down: If you're tired of re-running tests just because a timestamp didn't match, this one's for you. Timestamps.

A Google Data Cloud Leader's Formula for Token-Efficient AI

For most enterprises, autonomous AI agents still feel like a risk waiting to happen. Andi Gutmans, Vice President and General Manager for Data Cloud at Google, joins Cindi Howson to explain what it takes to build the data foundation that trustworthy agentic AI depends on. He breaks down how organizations can finally activate the 90 percent of enterprise data that's unstructured, why tokenmaxxing is the wrong way to measure AI value, and how open standards like Apache Iceberg are helping leaders tear down fragmented, multi-cloud data silos and unify data across clouds.

Ep 82 | Behavior Change: How Startups Are Making AI Stick

Behavior change is the biggest hurdle in AI adoption. AI only creates value when people make it part of their everyday work. In this episode of The AI Forecast, Paul Muller sits down with Varun Puri, CEO and co-founder of Yoodli, to discuss why successful AI adoption starts with changing how people work. Drawing on his experience at Google, Google X, and as the founder of an AI startup, Varun shares practical lessons on embedding AI into everyday workflows and building habits that stick.

Natural Language to Data Pipeline: How to Build Migrations Without Writing Code

You build a data pipeline from natural language by describing the source, destination, and required transformations in plain English to a platform with a prompt-to-pipeline feature, which then generates a draft pipeline with inferred field mappings, transformations, and a schedule for you to review and adjust. This guide is for operations teams, data analysts, and junior team members who understand the desired outcome of a migration but don't write SQL or Python.

How to Ingest and Reconstruct Multiple Unrelated CSV Exports or a PostgreSQL Dump from an Acquired Legacy System

You ingest and reconstruct multiple unrelated CSV exports or a PostgreSQL dump from an acquired legacy system by first mapping the dump's underlying schema and relationships, then building a staged pipeline that loads raw files or tables as-is, reconstructs relationships through keys, and only then applies business logic to produce clean, usable records.

How to automate API regression tests without code | SmartBear Swagger Functional Testing

Learn how to automate API regression tests without writing code. In this tutorial, you'll use an OpenAPI specification to build automated API tests with SmartBear Swagger Functional Testing using a visual, low-code workflow. What this solves.

Prompt Engineering for Manual Testers: How to Get Useful Output from AI Tools

You opened the AI assistant for the first time with a fair amount of hope. You typed "write test cases for the login page." You got eight test cases back in about three seconds. Valid login. Invalid password. Empty username. Empty password. The kind of list you could have written in your sleep, missing every scenario that actually matters for your product. So you closed the tool and thought: this is fine for the basics, but it does not really get testing. AI is overhated.