Systems | Development | Analytics | API | Testing

smartbear

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

Jul 13, 2026 By Klaudia Makiej In SmartBear
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.
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perforce

Introducing a Smarter Path to Intelligent Testing With Perforce Autonomous Testing

Jul 13, 2026 By Perforce In Perforce
Software teams are under constant pressure to release faster. Yet testing, the safeguard that protects quality, has not kept pace with modern delivery speeds. More code and shorter sprints overwhelm QA capacity, while fragmented tools and late-stage performance checks create bottlenecks that slow everything down. The question is not whether testing needs to evolve. The question is how to evolve without a costly rip-and-replace of your existing stack.
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Your Guide to Perforce Autonomous Testing

Jul 13, 2026 By Perforce In Perforce
Software testing is struggling to keep pace with modern release cycles. More code, faster deployments, fragmented tools, and increasing quality demands are creating bottlenecks for QA, engineering, and DevOps teams. Discover how Perforce Autonomous Testing transforms the way teams validate software by bringing functional, performance, web, mobile, and desktop testing together through a unified AI-driven experience. Using natural language, teams can define testing intent, automate execution, orchestrate complex workflows, and gain actionable insights faster than ever before.
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katalon

How to Test AI Applications Manually: A Playbook for Hallucinations, Bias, and Non-Deterministic Outputs

Jul 13, 2026 By Depa Panjie Purnama In Katalon
You have tested hundreds of features. You know the drill. Open the test case, write the preconditions, list the steps, fill in the expected result, run it, compare. Pass or fail. Move on. Then someone hands you an AI feature. A chatbot. A "summarize this ticket" button. A search box that answers in full sentences instead of returning a list of links. You open your test case template, you get to the "expected result" field, and you stop.
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databox

Salesforce MCP: Is CRM Data Enough for Your AI Agent?

Jul 10, 2026 By Nevena Rudan In Databox
Connecting Salesforce to Claude via MCP is the advancement the SERP says it is. You authenticate once, your AI agent queries live CRM data, and you stop copying deal records into chat windows. For a Revenue Operations Manager who spent Q1 begging an admin to export pipeline snapshots, that matters.
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xray

How Xray's AI Test Prioritization Helps Teams Focus on High-Risk Tests

Jul 10, 2026 By Mariana Santos In Xray
Test execution is one of the most time-sensitive stages of software delivery. Teams are expected to validate functionality, ensure stability, and support release decisions within increasingly shorter development cycles. Even with strong automation in place, there is rarely enough time to execute every Test before a release. This makes prioritization a critical part of the QA process.
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ably

Introducing AI Transport v0.5.0: durable execution with Steps

Jul 10, 2026 By Zak Knill In Ably
AI Transport v0.5.0 is now available. It adds first-class support for running an agent turn inside a durable execution framework, such as Temporal or Vercel's Workflow Development Kit (WDK), while every client watching the conversation still sees one clean, resumable stream. The last release, v0.4.0, let an agent hydrate its history from your own database. This one is about what happens when the process running the agent isn't around for the whole turn.
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