Systems | Development | Analytics | API | Testing

API testing for agent-driven code: SmartBear ReadyAPI MCP for Claude, Copilot, Cursor, and compatible agents

Key takeaway: When code moves at AI speed and testing stays manual, the widening gap between a code change and its test coverage becomes your biggest quality risk. SmartBear ReadyAPI MCP reduces that gap by letting you generate, run, and heal API tests in plain language, right inside the Claude, Copilot, or Cursor session that wrote the code.

How to run your first API contract test in five minutes | SmartBear Swagger Contract Testing drift

Learn how to create, configure, and verify your first API contract test with SmartBear Swagger Contract Testing drift. Teams know contract testing prevents breaking API changes, but most stall out before they start – citing “we don't have time, we don't have the right skills, or we don't have the resources.” SmartBear Swagger Contract Testing removes that onboarding friction with drift, a capability that automates schema validation so you can adopt Interface-Driven Development (IDD), making your API specification the primary artifact of your build process in minutes.

How agentic QA cuts the test maintenance tax

Every QA budget has a line item for building test coverage, but 30–50% of that automation budget ends up spent on maintenance instead of new tests. That disparity stays invisible until a release goes out, the application shifts underneath the tests, and the QA team spends the next three days rewriting broken scripts instead of finding new bugs.

Use of AI in Software Development

Quick application integrity check: can your quality strategy survive the tsunami of code coming its way? AI is accelerating development, increasing code abstraction, and multiplying the volume of software teams need to validate. But existing QA approaches weren't built for this level of speed and scale. Application integrity closes the growing gap between what teams build and what they can verify, providing continuous assurance that software works as intended.

API definition-native AI testing: Support faster, confident shipping with your existing Swagger and OpenAPI specification

APIs are the backbone of modern software. They connect microservices, power mobile experiences, and make integrations possible across industries. For all their importance, API testing remains one of the most fragmented, manual, and maintenance-heavy parts of the software development lifecycle (SDLC). So as development accelerates in an AI-disrupted SDLC, application integrity – continuous, measurable assurance that your software just works as intended – becomes harder to maintain, not easier.

What Is the SmartBear Zephyr Agent for Rovo? AI testing in Jira, explained

AI can now handle the slowest parts of test management, inside Jira. – The Zephyr Agent for Rovo creates test cases and links them to your work items, in the projects where your team already plans and builds. This guide covers how testing is changing in the AI age, what the agent is, where to find it, and how to run your first task.

Why traditional test metrics fall short in the AI era

Most QA teams already track the basic metrics: how many tests ran, how many passed, how much coverage exists, and how many defects turned up. Those numbers still matter, and engineering leaders will keep asking for them. The real challenge is turning those numbers into decisions, and that gets harder as AI-assisted development speeds up the volume, frequency, and complexity of software change.

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.

What SmartBear's AWS AI Software Competency means for software teams

AI is changing how software gets built. That raises the bar for every team responsible for keeping it working. SmartBear has been developing AI capabilities across the entire quality lifecycle that help teams deliver software they trust will work at AI speed and scale. Today, SmartBear announced it has achieved AWS AI Software Competency status in the Agentic AI category through the AWS Partner program.

How to design & test APIs with OpenAPI & Swagger | What's changed in 3.1 & 3.2

Outdated API docs and last-minute bugs cost teams time and trust. Learn how the OpenAPI Specification help you document, govern, and test your APIs from design to deployment – all inside SmartBear Swagger. SmartBear's Yousaf Nabi, Developer Advocate, and Chris Armstrong, Manager of Developer Relations, explain why API documentation drifts out of sync and walk through what's changed between OpenAPI 3.0, 3.1, and 3.2. After covering a brief history of Swagger and the OpenAPI specification, they demo the full API workflow across Swagger.

Enterprise test management: Should you build or buy in the age of AI?

AI has opened the door for teams to build tools they previously had to buy. With the right prompts and internal workflows, teams can generate test cases, summarize results, analyze defects, and automate parts of the testing process faster than ever. For enterprise QA and engineering leaders, that raises a practical question: “should we build our own test management layer, or adopt an AI-powered test management platform?” It’s a fair conversation to have.

Ship production-ready APIs faster and confidently with SmartBear ReadyAPI | Demo Den

API testing gets harder as it grows: fragmented tools, brittle scripts, thousands of endpoints, and external dependencies that block progress. SmartBear ReadyAPI lets teams ship production-ready APIs faster by validating functionality, performance, and security in one test, with virtualization built in – no separate tools to juggle. In this Demo Den, Thomas Hurley, senior manager of product management at SmartBear, shows how one test does the work of three, so teams cut maintenance time and stop waiting on external dependencies.

AI tip #2: Improve pull requests with AI

AI tip: For pull requests, let your AI agent take the first pass before your team ever sees it. That's the workflow Ilia Mogilevsky, Software Engineering Manager at SmartBear, built. By packaging prompts into a skill loaded with Git history, Jira context, and CI checks, he turned a basic AI assistant into a reviewer he trusts. The skill then generates a structured report with approval-ready fixes. Accept the changes, adjust what's off, and push – review cycles shrink and deploys move faster.

SmartBear Swagger: Meeting You Where You Work

Some approaches to API governance interrupt developers mid-flow, forcing them to context-switch into a separate tool and manually verify their API definition before they can ship. That approach has never really worked. Not because developers don’t care about quality, they do, but because the best time to fix an API is the moment you’re already thinking about it. That’s what has always guided how Swagger grows. Not “come to us.” But “we’ll be there.”