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Wide Events vs. Three Pillars: AI Observability Costs

As agentic AI workflows gain traction within organizations, those organizations are asking how to account for their behavior while keeping costs manageable. Some are sticking with the old three pillars of observability approach: take a measurement to create a metric, record output to a log, and track serial progress with a trace. Each of these is useful, but treating them as distinct formats from the start means paying for them distinctly too. Separate storage doesn't come cheap.

Introducing Infrastructure Knowledge: Teach Netdata AI What Your Metrics Can't Show

Netdata AI sees everything your infrastructure does: every metric, every anomaly, every alert. It does not see what your infrastructure is: which services matter, which host is supposed to run hot, who owns what, what your team considers normal. Without that context, “CPU at 91%” is just a finding. With it, it might be a machine doing exactly its job.

Assisted, Augmented or Agentic? Choose Your Splunk Starting Point

Episode two of Beyond the Thread explores how organizations can leverage a solid data foundation for AI-driven actions. Hosted by Courtney Wright and featuring experts Greg Ainsley-Malik and Sonal Pardeshi, the discussion delves into the Cisco Data Fabric, powered by the Splunk platform, and its role in transforming machine data into actionable insights. The episode highlights the journey towards agentic operations, addressing the challenges faced in moving from AI-ready data to effective implementations, and examines different adoption strategies that organizations may pursue.

Agentic Operations Start with Context: Build the Right Data Foundation

Episode 1, "Beyond the Thread: Deconstructing the Cisco Data Fabric Powered by the Splunk Platform," explores the intersection of data strategy and operational efficiency. Hosted by Splunk's Courtney Wright, the session features insights from experts Keith McClellan and Michael Sondag on the complexities organizations face in data management and operational models.

How to Build an HR PTO AI Agent with Resolve Agent Lab

See how to build an HR PTO agent with Resolve Agent Lab. In this Resolve Reels demo, we create a purpose-built AI agent by adding automation skills, instructions, conversation starters, and guardrails. The agent can answer PTO questions, check balances, account for calendar conflicts, and submit requests through systems like Workday or ADP. See how Resolve helps teams build AI agents that take action across enterprise systems.

AI Agents on Kubernetes 101: From Laptop Script to Production Pod

In short, this is a beginner’s guide to deploying an AI agent on Kubernetes. You will containerize an agent, store its API key as a Kubernetes secret, write a deployment with health probes and resource limits, expose it with a service, and lock down its network egress, in that order, with a working manifest at every step. On a local kind cluster the whole walkthrough takes about an hour.

Your AI Economics Pulse for September 2026

Across a same-store panel of 430 CloudZero customer organizations, AI reached 2.66% of the median company's cloud bill in August 2026, up from 2.61% in July and roughly four times its level a year ago. The 75th percentile crossed 11%. The share of organizations with at least 10% of cloud spend attributed to AI jumped to 28.2% from 23.9%, the largest one-month move that tier has posted. Two-thirds of the panel now spends at least $1,000 a month on AI. The typical bill barely shifted.

From idea to working software: what the full development lifecycle needs to look like

GitHub's research found that developers using Copilot completed tasks 55% faster than those who didn't. Tools like GitHub Copilot and Cursor, powered by large language models such as Claude or GPT, are designed to automate the tedious parts of programming so engineers can focus on harder, more creative problems. With this. new repos spin up every week. The promise is being kept. But where are the products?

The VM Boom For AI Agents | David Crawshaw Co-Founder & CEO, exe.dev

What happens when AI agents stop simply answering questions and start using computers of their own? It could create an entirely new boom in virtual machines. In this episode of Uplink, David Crawshaw, Co-Founder and CEO of exe.dev, joins host Michael Reid to explore the infrastructure behind the rapidly emerging world of AI agents. As agents become capable of writing code, running applications, operating tools, maintaining state, and working independently, they need more than access to an AI model. They need computing environments where they can actually get work done.