Systems | Development | Analytics | API | Testing

Connect agentic testing with Jira, Linear, and Azure DevOps| SmartBear BearQ

Oct 15, 2026 By SmartBear In SmartBear
Watch this video to see how see how SmartBear BearQ keeps bug reports and test results in sync with Jira, Linear, and Azure DevOps at AI speed and scale, with no manual copy-pasting. BearQ’s agentic QA system connects to the tools your team already relies on, so its three built-in agents can pull in context and push results right where your team lives.
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EP21: The Architecture of Product Design

Oct 15, 2026 By WSO2 In WSO2
In this episode of Discovering the Architecture Middle Path, Asanka is joined by Selvaratnam Uthaiyashankar (Shankar), WSO2's Chief Product Officer, to explore the architecture of product design. The conversation looks at the principles behind product decisions, the tradeoffs that shape those choices, and how architecture, developer experience, deployment models, and different user needs influence product design. They also discuss how product principles evolve as customer expectations and technologies such as AI and agentic systems continue to change.
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smartbear

SaaS or on-premise? How to settle the Swagger deployment question

Oct 15, 2026 By Daisy Priya In SmartBear
Choosing between SaaS and on-premise for SmartBear Swagger Studio, SmartBear Swagger Portal, and SmartBear Swagger Contract Testing The question that most often stalls an API solution evaluation is a simple one: do we run this as SaaS or on-premise? It’s simple to ask and hard to answer, because three teams each hold a veto. Security needs to know where your data will live. Your platform team has to decide whether it can take on another workload.
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speedscale

Use AI and traffic replay to test AI-generated code

Oct 14, 2026 By Ken Ahrens In Speedscale
When I ask an AI agent to change code, I also want it to run the application and test what it changed. Asking it to write some tests is a start. But if it invents the expected responses from the same assumptions it used to write the code, those tests can miss the same mistake. Traffic replay gives the agent something concrete to test against: requests and responses captured from a working application. The agent can run those requests against its changes, inspect the differences, and repeat the test after a fix. I can open the same results and check its work.
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speedscale

Observability Never Really Shifted Left

Oct 14, 2026 By Alan Mon In Speedscale
We made software development faster. Now we need to make validation faster, too. For the last decade we have shifted development left. Testing, security, performance, and CI/CD all moved closer to the developer. Observability mostly did not. It still starts after deployment (we covered the incident side of this in Your Observability Stack Found the Fire), which means the people writing the code rarely have meaningful production behavior to observe while they build.
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smartbear

TestComplete vs. Tricentis Tosca vs. Ranorex: Which test automation approach fits your team?

Oct 14, 2026 By SmartBear In SmartBear
Test automation gets harder when the application portfolio stops looking like a modern web stack. Desktop clients, packaged systems, internal applications, and long-lived business software all create different requirements for how teams build, run, and maintain tests. In an AI-disrupted SDLC, the rest of the stack changes even faster, and the risk concentrated in those systems grows with it.
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Connect Bolt.new to On-Prem SQL Server Without Exposing Sql Server to the Internet

Oct 13, 2026 By DreamFactory | AI & Applications Data Gateway In DreamFactory
Connect Bolt to an on-prem SQL Server database without opening a single firewall port or handing over a database password. In this video I put DreamFactory in front of a SQL Server database on my local network, generate a REST API from it, lock that API down with a read-only role and a scoped API key, then I have Bolt build a live delivery operations dashboard on top of it. Bolt calls the API from a Bolt server function, so the key never reaches the browser.
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astera

MCP vs. API: What's the Difference and When to Use Each

Oct 13, 2026 By Nida Fatima In Astera
An API is a defined way for one piece of software to request data or actions from another. The Model Context Protocol (MCP) is an open standard that gives AI agents a consistent way to discover and use tools and data sources, so they don't need a custom integration for each one. The short version: MCP doesn't replace APIs. Most MCP servers call APIs behind the scenes. What MCP changes is who the interface is designed for. APIs are designed for developers who read documentation and write code.
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