Systems | Development | Analytics | API | Testing

Designing error models in OpenAPI for agent-safe APIs | Swagger Studio

Poorly documented or inconsistent error models lead to brittle clients and unreliable automation. Whether you're building APIs for human developers or AI agents, proper error handling is crucial for automation and reliability. In this guided tutorial, SmartBear Solutions Engineer Rosemary Charnley demonstrates how to design robust error models in OpenAPI specifications using Swagger Studio.

Connect API design, testing, and governance in one workflow | Swagger

API design, functional testing, and governance shouldn’t live in silos. In this demo, Product Owner Wojciech Nowacki walks through a practical, end-to-end workflow that connects: You’ll see how API definitions created in Studio feed directly into automated functional testing ensuring style compliance, functional correctness, and governance checks across the full API lifecycle. Perfect for API platform teams, architects, and developers looking to unify design and test automation.

Best tool for AI-powered automated testing: Reflect vs. ACCELQ

If you’re shipping multiple releases weekly and your team is drowning in test maintenance, you’ve likely discovered the painful truth about traditional automation: code-heavy frameworks break faster than your developers can ship features. Every CSS class rename triggers test failures. Every component refactoring creates maintenance sprints.

How to make APIs AI-ready | Automating reviews with Swagger Studio & Spectral

As AI agents increasingly interact with APIs, design clarity and structured metadata matter more than ever. In this focused demo, Senior Solutions Engineer Mairtín Conneely take us through how to use Spectral rulesets in Swagger Studio to automatically enforce AI-ready API design standards across your OpenAPI definitions. This video covers:What “AI-ready” API design meansCreating custom Spectral rulesImporting governance rules into Swagger StudioRunning automated AI-readiness checksScaling API quality with governance automation.

Reflect vision-based AI demo | Create one test for multiple platforms

Create a single mobile test that runs reliably on both iOS and Android - without building separate tests per platform or relying on brittle, platform-specific locators. In this high-level demo, we use SmartBear Reflect’s vision-based AI to record a typical workflow in a sample coffee app, where each step is backed by visual context and intent. Then we run the same test across a mix of Apple and Android devices, including an iPhone, to show how Reflect adapts to the environment at runtime and helps reduce flakiness and false positives.

Maintaining compliance when adopting AI in regulated industries

Key Takeaway: Organizations in regulated industries can adopt AI without compromising compliance. Automated testing enables continuous validation of AI-enabled systems while maintaining the predictability, documentation, and audit-readiness that regulators require. In compliance-first industries, such as banking, healthcare, or telecommunications, AI adoption is rarely a simple technology decision. You are often caught between two competing pressures.

4 best API testing tools for enterprise teams

Enterprise development teams face mounting pressure to deliver secure, performant APIs while managing complex distributed architectures, strict compliance requirements, and accelerating release cycles. The API testing platform an organization chooses directly affects product quality, team velocity, and regulatory risk. Functional validation, security testing, performance testing, and CI/CD integration must all scale across global teams without introducing governance gaps.

ReadyAPI vs. Postman: Why enterprise API testing needs more than collaboration tools

Enterprise API teams rarely struggle with a lack of tools. They struggle with fragmented toolchains that promise agility but deliver chaos. According to IBM Systems Sciences Institute research, late-stage defects can cost up to ten times more to fix than early detection, while industry analysts report that tool sprawl can waste up to 30% of software expenses through redundant licensing and operational overhead.

Celebrating Datalex: Setting the standard for developer visibility in API-first development

At SmartBear, we recognize organizations that improve software quality by increasing clarity, alignment, and confidence across the development lifecycle with the Developer Visibility Award. For 2025, the award goes to Datalex, a leading airline e-commerce solutions provider. Datalex equips airlines with API-driven platforms that provide tools for driving revenue and profit as digital retailers.

SmartBear QMetry's AI-based test generation: Execute tests in minutes

In this video, you’ll discover how SmartBear QMetry's AI-powered test generation automatically transforms requirements into complete, executable test cases within minutes. Watch as we demonstrate test generation cases from Jira, Rally, and Azure requirements, demonstrate how to refine existing tests, and save your teams hours of manual work.