Operations | Monitoring | ITSM | DevOps | Cloud

Your AWS Kiro Agent Can Now Query CloudZero. Here's What To Ask It

CloudZero's new AWS Kiro integration puts cost intelligence directly in your agentic IDE. Ask plain-language questions about spend, attribution, and cost-per-serve without leaving your development workflow. We see a similar pattern playing out across engineering teams running agentic development tools: code gets shipped fast, something moves in the cost data, and understanding why still requires leaving your environment entirely.

Your CEO Wants You To Ramp AI Usage Without Breaking Budgets. Here's How You Can Do It

Notes from a finance leader whose job this is. A few weeks ago, I traveled to Philadelphia for a conversation with a prospective CloudZero customer. We’d been working with the prospect’s engineering team for some weeks, demoing our platform in view of the RFP they’d drawn up. This stage had gone well, and so the next step was talking it over with the prospect’s CFO. We expected a conversation centered around the key criteria in the RFP.

Automate your critical workflows with AI agents in 5 steps

Many teams remain bogged down by operational chaos and manual drudgery, even with access to a variety of automation solutions. These tools often operate in silos, creating disconnected islands of automation that require significant human effort to bridge. Agentic AI offers a path forward, creating a cohesive system that can intelligently and autonomously handle complex operational workflows.

last9-genai: Closing the Conversation Gap in LLM Observability

OpenTelemetry's GenAI instrumentation gives you spans and token counts. It does not give you conversations, workflow cost rollups, or prompts visible in your dashboard. last9-genai is an OTel extension that fills those three gaps — without replacing your existing observability stack. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

The Best AI Chatbots of 2026

AI has since become an integral part of our lives, whether it’s for work or personal use; we all use AI in some form or another. However, deciding which is the best AI depends on how you want to use it. Whether it's for general questions, coding, deep research, or image creation, we’re lucky enough that there is an AI model available to help you out.

15: Optimizing AI Workloads: Balancing Cost, Performance, and Scalability with Bijit Ghosh

In this episode, Andrew Hillier and Bijit Ghosh discuss the evolving landscape of AI, discussing the growing prominence of inference over training, hybrid cloud strategies, balancing cost with performance, and the orchestration of complex hardware environments. The conversation also touches on emerging concepts like AI factories, the challenges of sovereign cloud, and how enterprises are navigating data gravity and regulatory constraints. It's a deep dive into optimizing AI infrastructure, managing costs, and the disruptive changes that are transforming both technology and business outcomes.

Demo - Selector Platform CoPilot Diagnosis

See how Selector’s AI Copilot accelerates issue diagnosis in real time. In this demo, watch how natural language queries and AI-driven insights help teams quickly analyze incidents, surface root cause, and understand impact - without digging through multiple tools. Instead of manual investigation, Selector guides operators to answers faster, reducing noise and speeding up resolution. Built for network and operations teams who need clarity, speed, and smarter troubleshooting.

Introducing the Cortex AI Assistant (now in Slack)!

Mention @Cortex in any Slack channel the Assistant has been invited to, public or private, and get grounded answers pulled from your Cortex data. Questions can be as simple as "who owns payments-api?" or as analytical as "what's driving our incident trends this quarter?" The Assistant pulls context from all across Cortex, including ownership, Scorecards, Initiatives, on-call, dependencies, and Eng Intelligence metrics, and holds context across a threaded conversation.

Accelerating AI Agent Development on Google Cloud with JFrog MCP Registry

Developers building agentic AI on Google Cloud have powerful infrastructure at their fingertips: Gemini 3 for reasoning, Google’s Agent Development Kit (ADK) for orchestration, and a rapidly expanding ecosystem of Model Context Protocol (MCP) servers that connect agents to data and tools. So why are so many teams still waiting weeks to ship their first agent to production?

What "AI-Ready Data" actually means for observability teams

Many organizations deploying AI are learning similar lessons right now: the challenge isn’t this or that AI model, it’s the data. According to Gartner, 60% of AI projects will be abandoned by organizations because of failures to support these projects with AI-ready data. Also, 63% of organizations either lack or aren’t sure they have the right data management practices to get there.