Operations | Monitoring | ITSM | DevOps | Cloud

Building Investigations: what it takes to build an AI SRE

At incident.io, we've spent the last two years building Investigations, our AI SRE. When you get paged, it starts investigating straight away, looking across your telemetry, recent deploys, past incidents, docs and code, and posts what it's found in your incident channel (or on your phone, if it's 2am and you're still deciding whether you need to get out of bed). By the time you open your laptop, you're starting at step six of triage rather than step one.

Shipped: Get alerted when AI spend spikes, with the cause attached

AI spend now comes from every department, and it can double in a week without anyone deciding it should. The invoice arrives after the month closes. By then the usual response is a spend cap, which slows every team, including the ones getting real work done with AI. You need to know about spend that breaks its normal pattern while there’s still time to act. The alert should reach the person who can act on it, with proper context and detail.

Your data says one thing. Your AI thinks another.

Aiven and SFEIR walk through what a governed, AI-ready foundation for product data looks like, and what it actually takes to build one. We'll be joined by Céline Thooris, Managing Director of WEnvision (SFEIR group), and Stan Dmitriev, Product Director for Aiven Context, sharing a first-hand view from the field and a concrete next step. What to expect.

Top 5 AI Pitch Deck Tools for Sales Teams That Cut Slide Creation Time

AI pitch-deck tools promise "60-second" creation, yet reps keep burning hours fixing fonts, charts, and brand colors. We benchmarked 12 contenders and timed every step-from first prompt to a client-ready PowerPoint or Google Slides file. Only five met our bar for deck quality, native editability, workflow speed, collaboration depth, value, and security. This guide shows which tool wins each sales scenario so you can choose based on data, not hype.

env zero Launches EZ Control, the Autonomous Cloud Control Plane for the AI Era

Industry-Leading Asset Coverage Spanning Nearly 2,300 Resource Types Across AWS, Azure, Google Cloud and Kubernetes, Structured into a Real-Time Ontology That EZ Control Uses to Enforce Security, Cost, Availability, Maintenance and Performance Policies Continuously.

This AI agent finds your app's bottlenecks and suggests the fix

Most teams collect the profiles and traffic data that explain a slowdown. Almost nobody has time to read it before users notice. In this Product Highlights conversation, Sylvain Guittard, Senior Director of Product at Upsun who leads the team behind the Upsun console and CLI, breaks down the Upsun Cloud Performance Agent, the first background agent running on Upsun Cloud. His take: "We monitor everything, we feed that into an agent, and the agent will be capable of finding what the bottlenecks are in your application. And on top of it, it gives you a patch, or a way to fix it." We get into.

AI cost allocation: how to attribute AI spend by team, product, and customer

AI cost allocation is the practice of attributing every dollar of AI spend to the team, product, feature, or customer that generated it. That spend includes API tokens, GPU compute, per-seat tools, and shared infrastructure. It's harder than cloud allocation because AI spend arrives untagged, spans vendors, and pools in shared resources. Four methods cover most cases: tag-based, key-based attribution, proportional split, and usage-telemetry.

AI in IT Operations: How to Build Trust, Automate Smarter & Prepare for Autonomous IT

What does it take to make AI and automation actually work in enterprise IT? In this episode of Agents of IT, Resolve’s Zach Austin sits down with Nick Dimmock, Co-Founder and CEO of TechWorks, to discuss how IT automation is evolving, why trusted data matters, and what organizations need to build before AI can deliver meaningful business outcomes.

Managing Claude Code Sessions Through Lynx

At Tigera, we spend a lot of time thinking about agent security: identity, policy, runtime controls, and the record left behind after an agent acts. Coding agents create an interesting problem because, in most organizations, they didn’t arrive through the front door. Few companies ran a platform evaluation and rolled Claude Code out to 500 developers. Developers installed it themselves.