Every AI rollout reaches this moment
Giving developers AI tools is only the first step.
Operations | Monitoring | ITSM | DevOps | Cloud
The Place Where Modern Operations & Technology Come Together
The latest News and Information on APIs, Mobile, AI, Machine Learning, IoT, Open Source and more!
AI budgeting: how to plan and forecast AI spend
AI budgeting is the process of planning, allocating, and forecasting an organization's AI spend: model and API costs, AI infrastructure, tooling, and the people running it all. It differs from traditional budgeting because AI spend is usage-based, scales with product success rather than headcount, and often spans multiple providers.
A Practical Guide to Embedded Linux Logging
This article will walk through various types of embedded Linux logging platforms and consider tradeoffs between logging storage media, compression, and verbosity to enhance device debugging without eating into your limited resource budget.
Agent Mode Engaged! Enchaining Agentic Operations with Splunk AI Assistant 2.0
In this session, we will introduce your new "digital teammate"—the supercharged Splunk AI Assistant. We’ll demonstrate how the new Agent Mode provides the context, reasoning, and recommendations necessary to reduce your mean time to resolution (MTTR) from hours to minutes.
Anthropic's Mythos 5 Fakes Identities Hacking Britain's Government AI Challenge
AI agents are now faking identities — and this is the case that proves it. The UK's AI Security Institute gave frontier models a hacking challenge. Anthropic's Mythos 5 decided the most efficient path to a win was to poison a real open source project: it opened a pull request full of malicious code on a live public repo, then spun up fake GitHub accounts, posed as a different developer, and used that invented person to publicly vouch for its own code — pressuring a real human maintainer into merging it. A human reviewer caught the malware and closed the PR.
OrionIQ Now Connects to 600+ Platforms: Full Context for Agentic Observability
AI agents are only as effective as the context they can access. The problem is that most of that context lives outside your observability platform.
AI Incident Response: Edwin AI in Slack Finds Root Cause Fast
AI incident response just got faster. Watch how LogicMonitor Edwin AI brings investigation, root cause analysis, and action directly into Slack for ITOps, SRE, DevOps, NOC, and incident response teams. When an incident hits, responders juggle monitoring tools, ITSM systems, dashboards, and documentation to find what they need. Edwin AI brings that context into Slack, so your team can investigate, decide, and act in one place.
Shared context for AI coding agents beats better tooling
The instinct when adopting AI coding agents is to optimize the agent. Compare models, tune prompts, argue about which editor has the better completion, and treat the agent as the thing that determines how fast the team moves. Then the commits go up and the product does not. The team building Upsun Dispatch took a different route, and the result is worth copying. They did not find a better agent.
How to Investigate a Production Incident Using an AI Agent (AppSignal MCP)
An incident has hit your product. I've been there: you're context-switching between hosting, CI/CD, codebase, AppSignal for monitoring, and whatever else your product depends on to minimize downtime and potential losses. You're trying to piece everything together, but it takes a lot of time, and that's something you don't have. AI agents connected to your tooling and your monitoring data via MCP free up that time for you.