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The Leverage of using AI is Bigger the Smaller You Are

Jul 13, 2026 By Vic van Gool In Cloud 66

A few weeks ago I went to quantify a trend I had already clocked: our test-writing had climbed, and kept climbing. Where a typical month once brought a handful of new tests, last month brought 436.

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How We Secured AI Worker Agents in Harness | Harness Blog

Jul 13, 2026 By Shivansh Srivastava In Harness

When we launched Autonomous Worker Agents, the message we led with was simple: governance is inherited, not integrated. Agents don't get security bolted on after the fact. They inherit the OPA policies, RBAC, and audit trails already running your production pipelines. This post is about the layer underneath that promise: isolation. We let an Autonomous Worker Agent run shell commands and call APIs inside our pipelines.

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Cut AI Agent Validation Startup Time From 45 Seconds to 12

Jul 13, 2026 By CircleCI In CircleCI

A fresh Chunk Sidecar took 45.6 seconds to start. With a snapshot, it only took 12.

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Part II: Inside Alert AI Analysis: From a Single-Agent Prompt to an Agent Harness

Jul 13, 2026 By Kevin Klein In logz.io

TL;DR: This is the engineering companion to our announcement post, Upgraded Alert AI Analysis: Automated Incident Investigation, read that one for what the new generation does for your team; read on for how it works under the hood. Interested in hearing more? Book a demo to see the Alert AI Analysis Agent live. Root cause analysis is one of the harshest tests you can give an AI.

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Upgraded Alert AI Analysis: Automated Incident Investigation

Jul 13, 2026 By David Lotan Bolotnikoff In logz.io

TL;DR: OrionIQ has launched the next generation of its Alert AI Analysis agent within the Open 360 AI platform, designed to automate and accelerate incident investigation. Key features of this evolution include: Agent-Based Investigation: Instead of relying on a single prompt, the system coordinates specialized AI agents to correlate data across diverse sources like logs, metrics, deployments, and tickets.

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What Separates a Serious AI Data Collection Company From One That Just Says It Is

Jul 12, 2026 By OpsMatters In OpsMatters

Most AI projects don't fail at the model architecture stage. They don't fail at deployment. They fail earlier and more quietly - at the point where the data that was supposed to train the model turns out to be insufficient, inconsistent, or simply wrong for the task it was collected to serve. Choosing the right ai data collection companies is, in this sense, one of the highest-leverage decisions an organization makes when building AI capability - and one of the decisions most commonly made on the wrong criteria.

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Monitoring AI Applications in 2026: What You Actually Need

Jul 11, 2026 By Sarah Morgan In Scout

Last updated: July 2026. Your AI feature works in development. It demos well. Then it hits production and you discover three problems your test suite did not catch: the LLM hallucinates product names that do not exist, the RAG retrieval step adds 4 seconds to every request, and your OpenAI bill is 3x what you budgeted because one prompt template is burning tokens on context that does not help the output. Traditional APM would have caught the latency.

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Your AI Coding Agent Is Flying Blind in Production

Jul 11, 2026 By Sarah Morgan In Scout

Your AI coding agent can refactor a module, write tests, and open a PR. It can read your codebase, understand your patterns, and suggest changes that follow your conventions. What it cannot do, unless you set it up, is see what is actually happening in production. That is a problem. The agent that writes the code should have access to the errors, traces, and performance data that code generates once it ships. Without production context, your agent is writing fixes based on the code alone.

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AI Is Reshaping the Tech Industry in 2026: What Consumers and Businesses Need to Know

Jul 11, 2026 By OpsMatters In OpsMatters

Artificial intelligence has evolved from an emerging technology into one of the biggest drivers of innovation across the global technology industry. In 2026, AI is influencing everything from smartphones and laptops to cybersecurity, cloud computing, enterprise software, and digital productivity tools. Companies worldwide are investing heavily in AI powered products that improve efficiency, automate repetitive tasks, and deliver more personalized user experiences.

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