Systems | Development | Analytics | API | Testing

Build vs Buy: The Hidden Costs of DIY MCP Server Infrastructure

You whipped up a simple MCP server prototype over the weekend. It routed a single AI agent to a few internal tools, your demo impressed leadership, and the team asked the dreaded question: "When can we ship?" You smiled and said, "Give me two weeks." Fast-forward three months**.** You’re firefighting expired tokens at 2 AM. The compliance team is camped in your inbox. Your once-elegant codebase is now a distributed systems nightmare. Sound familiar?

10 Types Of API Testing Explained With Examples (2026)

APIs (Application Programming Interfaces) are the backbone of modern software; they let applications talk to each other, share data, and trigger actions across systems. Before any API goes live, it needs to be thoroughly tested to ensure it works correctly, handles edge cases, performs well under load, and stays secure. This guide covers all major types of API testing with real-world examples and tool recommendations.

How does BearQ autonomous QA work? Your top questions answered

Testing software at scale has always been a race against change. Then, AI-coding turned what was once a challenge into a crisis: rapid development cycles accelerated by AI have made it impossible to maintain comprehensive test coverage and catch issues before they impact users. In SmartBear’s Closing the AI Software Quality Gap Study, 60% of software experts told us they experienced quality issues as development outpaces testing.

How to scale API standards across large teams | Swagger Studio

When multiple designers and teams contribute APIs, you face inconsistent schemas, divergent patterns, and broken assumptions. However, the "shift-left" approach to API standardization helps you catch issues early, automate compliance, and maintain quality without manual gating – making your API program truly scalable. In this video, SmartBear Senior Solution Engineer Joe Joyce demonstrates how to enforce consistent API standards across large development teams using Swagger Studio's governance, collaboration, and CI/CD integration features.

When Your Observability Literally Stops Traffic

Last week, a fleet of autonomous robotaxis in China suddenly stopped working—at scale. Over a hundred vehicles stalled across a city, stranding passengers in traffic and raising immediate concerns about safety, reliability, and trust in autonomous systems. This wasn’t just a bad day for self-driving cars. It was a distributed systems failure, one that happened in the physical world, not just in dashboards.

OpenTelemetry Trace Testing for CI Release Gates

OpenTelemetry is great at answering one question: “what just broke?” The problem is that most teams need a different answer first: “what is about to break in this release?” That is where trace-based testing comes in, especially for teams running a vendor-neutral OTel stack (Collector + Tempo/Jaeger + Prometheus) and needing deterministic release gates.

Full Stack AI for Healthcare: Optimizing Clinical Workflows with Conversational AI for Authorization

Prior authorization is one of the biggest drivers of clinician burnout and care delays, costing the U.S. healthcare system billions in administrative waste every year. Traditional automation hasn't been able to handle the complexity of real-world clinical documentation. Until now. In this session, we go beyond the AI hype to show real outcomes of AI in healthcare, demonstrating how Agentic Conversational AI, integrated directly into EHR workflows, is transforming the prior authorization process.

Inside the SmartBear Roadmap: Delivering Application Integrity Across the SDLC

As software teams move faster across APIs, testing, and observability, keeping application integrity intact is harder than ever. Join SmartBear product leaders for a Now / Next / Later look at how we’re evolving our platform to help teams build, test, and operate software with confidence. What you’ll get from this session: Get a clear view of where SmartBear is headed and how these capabilities come together to help your teams scale quality alongside velocity across the SDLC.

SmartBear testing tools compared

AI-accelerated development has fundamentally changed how software is built, and across the industry, its impact on quality is already measurable. In SmartBear’s Closing the AI software quality gap study, we found nearly 70% of software professionals report application quality is declining as AI speeds up code generation, with development velocity increasingly outpacing teams’ ability to test effectively.