Systems | Development | Analytics | API | Testing

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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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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thoughtspot

From Dashboards to AI Agents: Huel's Analytics Journey

Sep 21, 2026 By Bhav Patel In ThoughtSpot
You’ve rolled out a modern data stack, built self-service dashboards, and empowered your team to ask their own questions. Job done, right? Not quite. The data landscape is shifting rapidly beneath our feet, which makes it critical to understand how to build your AI for BI platform so you can scale and navigate technology evolutions.
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kong

Kong Gets a New Look with Electric Agentic-Era Rebrand

Sep 21, 2026 By Juliette Rizkallah In Kong
We're pulling back the curtain on a project we've been working on for some time now: the next evolution of the Kong brand. You may have noticed some changes recently to our site, swag, or socials. But today it's official: we're announcing Kong's rebrand and introducing our new mascot, Karl. Without further ado, let's dig in. Along the way, we'll give some insight into the "why" behind it all, while looking back at how things have changed since the early days of Kong. We were the API connectivity company.
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RAG in Quality Engineering: Ship Faster, Test Smarter | Janani Balasubramanian

Sep 19, 2026 By The Test Tribe In The Test Tribe
How can Retrieval-Augmented Generation (RAG) help Quality Engineering teams ship faster and test smarter? In this TTTribeCast session, Janani Balasubramanian explores the practical applications of RAG in Quality Engineering and how teams can use organizational knowledge, testing data, and engineering context to improve the way they design, prioritize, and execute testing.
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speedscale

Team-Based DLP: Give Each Group Its Own Redaction Rules

Sep 18, 2026 By Matt LeRay In Speedscale
A shared Kubernetes cluster rarely belongs to one team. Payments runs checkout in one namespace, search runs search-api in another, and a risk team runs a scorer somewhere else. One Speedscale forwarder captures API traffic for all of them. Redacting that traffic before it leaves the cluster is what makes it safe to use for testing (the background is in The PII Testing Dilemma). Until now, that forwarder ran exactly one DLP rule. Every team that needed a field redacted had to edit the same JSON document.
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AI Adoption: What Goes Wrong & How Leaders Fix It | Brenn Hill

Sep 18, 2026 By The Test Tribe In The Test Tribe
In this interactive AMA session, Brenn Hill, AI executive and author of The Delivery Gap, explores why many AI adoption initiatives fail to create lasting impact despite growing investment and enthusiasm. Drawing from his experience helping engineering organizations adopt AI at scale, Brenn unpacks the common pitfalls that hold teams back and shares practical strategies for engineering leaders to drive meaningful adoption. The session will cover how to align AI with business goals, measure success beyond hype, and build a culture that enables sustainable AI-driven transformation.
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