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atatus

Introducing AI-Powered Incident Correlation & Root Cause Detection

Jul 13, 2026 By Mohana Ayeswariya J In Atatus
An API latency spike hits your checkout service, and within ninety seconds your on-call phone won't stop buzzing. A CPU threshold breaches. A database connection pool exhausts. A pod restarts. An error rate crosses 5% on a downstream service. Six engineers get paged inside four minutes. Forty alerts. Seven services. One incident. Every monitoring tool in the stack is doing exactly what it was configured to do, telling you that something is wrong.
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redgate

99% of database professionals are seeing AI benefits. So why are the security challenges increasing?

Jul 13, 2026 By Sarah Lahiff In Redgate
The numbers from the 2026 State of the Database Landscape: AI Edition are striking. 99% percent of respondents using AI report at least one measurable benefit for their database work. Automation is up, performance is improving, and three-quarters report significant cost savings. By almost any measure, AI is delivering. However, sitting alongside that near-universal positivity in the same dataset, security and privacy concerns have climbed to 64%. Regulatory compliance anxiety has risen to 40%.
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IT on the 4th of July? Not with AI. | Zero Ticket Minute

Jul 13, 2026 By Resolve In Resolve
What if your IT team could enjoy the Fourth of July without getting interrupted by password resets, VPN issues, and routine service requests? In this week's Zero Ticket Minute, see how agentic AI and automation help eliminate repetitive tickets so IT teams can enjoy the holiday while work gets done.
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harness

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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logz.io

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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logz.io

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

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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scout

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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