Systems | Development | Analytics | API | Testing

Self-Driving Regression Testing with AI Agents | Somu Suryanarayanan

Sep 24, 2026 By The Test Tribe In The Test Tribe
This session looks at how agentic automation can take over web and mobile regression. We start with the regression tax: the slow 3-day cycle, the manual triage, and the flaky reruns nobody trusts. We then cover why scripted suites hit a wall and make the case for handing regression to agents instead. The core of the talk is how it actually works, with Appium MCP driving mobile and Playwright MCP driving web, interpreting intent rather than hard-coded steps and adapting when the UI shifts.
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appsignal

Add an App With Your Coding Agent

Sep 23, 2026 By Serena Chou In AppSignal
You can now ask your coding agent to set up AppSignal in your application. Give it a prompt, let it follow the installation instructions, and watch a checklist fill in as data starts arriving. Installing AppSignal was already straightforward, but it still meant checking your environment, adding the right integration, creating an app, handling credentials, and confirming that telemetry arrived. These are useful checks, but they don't all need your attention.
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thoughtspot

4 Signs Your AI has a Context Problem, Not a Model Problem

Sep 23, 2026 By Laura Cohen In ThoughtSpot
Most AI agents can write SQL. The problem is they write it against the wrong definition of revenue, for the wrong team, using the wrong business rules, and you won't catch that in a demo. Before you evaluate a single semantic layer vendor, there are four things worth getting right first. The semantic layer market has never been more crowded. Every major analytics vendor, data platform, and BI tool now claims to have one.
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smartbear

Autonomous doesn't mean unsupervised: Trusting agentic QA without losing oversight

Sep 23, 2026 By Bridges Smith In SmartBear
AI agents review code, triage incidents, summarize tickets, and draft documentation, and the industry has largely decided the help is worth having. Leadership is often pushing teams for AI productivity gains and many teams accept the mandate. The obstacle is what happens next: the agent works on the wrong thing, the time and money spent on it return nothing, and the team ends up less efficient than before it started by creating more work.
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kong

AI Governance Tools and Platforms: An Enterprise Guide

Sep 23, 2026 By Kong In Kong
*Enterprise AI governance requires a composed stack connecting high-level risk and compliance oversight with real-time runtime enforcement. While oversight tools manage inventories, approvals, and evidence, AI gateways apply policy controls directly to live model, API, MCP, and agent traffic. Implementing both runtime controls and structured governance frameworks ensures comprehensive security, observability, and cost management across your entire AI estate.*
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Ep 92 | Making AI Fluent in Your Business

Sep 23, 2026 By Cloudera, Inc. In Cloudera
Every new AI chat is another first day on the job. Rob Collie describes today’s general-purpose AI as a brilliant new hire who wakes up on their first day every time you start a new conversation. It may understand your industry, but your workflows and business context have to be explained all over again. In this episode of The AI Forecast, Paul Muller speaks with Rob Collie, founder and CEO of P3 Adaptive, former Microsoft product leader for Excel and Power BI, and author of “Fair Game: Bringing AI Into Reach for All Business” to explore why business context can make or break enterprise AI.
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oril

AI Is Reopening the Build-versus-Buy Question in PropTech

Sep 23, 2026 By Zorian Fedoryga In ORIL
For much of the past decade, the PropTech default was straightforward: buy the commodity software and reserve engineering capacity for what differentiated the product. AI-assisted development is moving that line, and the answer looks less obvious than it did two years ago. For the broader framework, including cost, integrations, vendor lock-in, data ownership, and long-term maintenance, see our guide to build vs buy real estate software.
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How Will Software Engineers Interface with AI in the Future? aicoding #devops #techdebate #aiagents

Sep 23, 2026 By Speedscale In Speedscale
A breakdown of the three potential ways software engineers will interact with AI coding assistants, ranging from local desktop setups to fully automated software delivery factories. Learn more: speedscale.com.
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ably

Why WebSockets beat SSE for AI streaming at scale

Sep 23, 2026 By Madeleine Quinn In Ably
Picture a support agent mid-refund: it has pulled the order, flagged the tool call, and is one approval away from processing it. Then the user needs to approve it. On Server-Sent Events (SSE), there's no channel for that approval to reach the agent, because the connection only carries data one way - from server to client. A WebSocket keeps that connection open in both directions instead, so the approval could reach the agent the moment it's given.
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speedscale

Imaginary Test Data. Real Token Bill.

Sep 22, 2026 By Alan Mon In Speedscale
Ask an AI for K-pop concert advice without saying the group, city, date, or budget. It may confidently send you to a BLACKPINK tribute night in Cleveland with a $400 resale ticket. The AI was plenty confident. It just had nothing real to go on. That is exactly what happens when developers test AI applications with invented traffic. The test may look reasonable. The result may even pass. But when real users arrive, with messy histories, incomplete inputs, odd request sequences, and unpredictable timing, the application has to improvise. And improvising is expensive.
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