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harness

Bringing Third-Party Apps into Harness AI Chat: Our MCP Gateway for Distributed Enterprise Systems | Harness Blog

Aug 14, 2026 By Vivek Dixit In Harness
TLDR: When you work in Harness AI Chat, your work doesn't stop at Harness. Your pipelines live here, but the change you actually need to make might be a YAML file in GitHub, a Jira ticket, or a Confluence doc. So we built an MCP Gateway inside Harness that lets AI Chat reach those third-party apps for you: safely, under Harness's own access controls and secrets, and without dropped sessions across our distributed fleet. This is the story of what we built and why.
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grafana

Better context, smarter testing: How to give your AI coding agent direct access to k6 docs

Aug 14, 2026 By İnanç Gümüş In Grafana
As testing workflows become more AI-assisted, fast access to accurate documentation matters more than ever. Whether you're writing a new load test, troubleshooting an issue, or having an AI agent generate a script for you, you need reliable guidance that keeps pace with the way you work. But most documentation still lives in a browser. Every time you or your agent needs to verify an API or look up a best practice, you're forced to leave your terminal or editor and interrupt your workflow.
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mezmo

What an AI SRE agent actually finds when you point it at a broken Kubernetes cluster

Aug 14, 2026 By Mezmo In Mezmo
‍ Most of the AI features that shipped into observability tools this year summarize alerts. You get a paragraph that restates the dashboard you were already looking at, and the agent never reads the cluster itself, because giving it cluster access is a security conversation nobody wanted to start. This walkthrough starts it.
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mezmo

Builder in the loop: what production agents were missing before AURA

Aug 14, 2026 By Mezmo In Mezmo
Builder in the loop is a Mezmo interview series with the engineers, product leaders, and operators shaping AURA. Each installment looks past the product layer to explore the decisions, tradeoffs, and lessons involved in building agents for real production work. This installment features Mike Shearer, the engineer who built AURA and, until recently, its only developer. AI agents are easy to believe in when the task is small.
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What is going wrong with AI coding? Live Laugh Logs ep. 4

Aug 14, 2026 By Coralogix In Coralogix
Welcome to Episode 4 of Live Laugh Logs, the podcast from the Coralogix Developer Relations team. This week, Chris Cooney joins Annie to share five key DevOps skills that have become even more important in the age of agentic code development, and gives you five key actions you can do today to start levelling up these skills. Subscribe to our channel for more insights into observability and AI.
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Where AI Media Actually Slows Teams Down - And It Isn't Generation

Where AI Media Actually Slows Teams Down - And It Isn't Generation

Aug 14, 2026 By OpsMatters In OpsMatters
The constraint on AI-generated video and imagery inside most organisations is no longer the model. It is the review loop, the consistency of a set, and a cost model nobody agreed on in advance - and none of those three get solved by switching to a better generator. In short: budget for iteration rather than render time; build a reference library before the first deliverable; define what a project's generation allowance is up front; and evaluate models on how they respond to a single prompt edit rather than on peak output quality.
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5 Best CAFM and Facilities Management Software Providers in 2026

5 Best CAFM and Facilities Management Software Providers in 2026

Aug 14, 2026 By OpsMatters In OpsMatters
Managing facilities across multiple businesses, sites, assets, and contractors can quickly become a full-time administrative job. That's where Computer-Aided Facilities Management (CAFM) software comes in. It gives teams a central place to manage reactive maintenance, planned preventative maintenance (PPM), assets, contractors, compliance, and more.
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speedscale

Synthetic Monitoring Is Broken. Your Production Traffic Can Fix It.

Aug 13, 2026 By Ken Ahrens In Speedscale
Synthetic monitoring has been a critical part of application reliability for years. It gives engineering and operations teams a way to proactively test applications, APIs, and critical customer journeys before users encounter problems. But there is a fundamental limitation with the traditional approach: Someone has to create the tests. As applications become more distributed and customer journeys become more complex, organizations can end up maintaining hundreds or even thousands of synthetic scripts.
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