Add a human breakpoint to your AI SRE agent workflow before it acts
Reaching a tool and being allowed to use it are two separate lists in the config. Put the mutable Kubernetes tools on the second one and each call pauses.
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Why More UK Firms are Turning to Colocation for their AI Workloads
The last few years have seen AI conversations dominated by the need for investment in hyperscale infrastructure as firms race to build ever larger training models. But as those conversations evolve, the emphasis is shifting to the next phase of AI adoption, focusing on the scaling of use cases and real-world value.
Closing the AI gap: How next-generation knowledge access unlocks mission outcomes for government
A recent IDC Spotlight report based on a survey of 685 public sector respondents found that 72% describe scaling AI from pilot to production as "very" or "somewhat" difficult.¹ Choosing the right model is only part of the challenge. Agencies also need to get their data ready for AI.
AI Week: Friday announcements
Friday, the last day of AI Week, is all about collaboration. Here's what we're announcing today! Check out grafana.ai for more details on everything we announce this week.
Observability and AI explained - Grafana Labs
Senior Developer Advocate Nicole van der Hoeven explains how we're thinking about observability and AI at Grafana Labs. She talks about how you can use AI with observability across the entire SDLC and the different tools you can use for both AI for observability and observability for AI.
Agent Observability Deep Dive Demo | Grafana Cloud
Grafana AI Observability is our new database and platform for observing AI Agents. Over the past year at Grafana Labs, we built Agents and we needed a way to understand how they are performing, what are the costs associated with them, what's the error rate or time to the first token as well as how they are behaving. Grafana Staff Engineer, Ivana Hučková provides a deep dive demo on how Grafana AI Observability connects our experience building Agents with our experience building observability systems.
Kepler Is in Public Preview: One Task, Every Repo, Every Agent
A faster car doesn’t get you home faster if the freeway is still jammed. That is the problem most teams run into once they add a second, third, or fourth AI coding agent to the mix. More agents generate more code. They do not automatically generate more finished work, because someone still has to track which agent is waiting on input, which one just opened a pull request, and which one has been quietly stuck for twenty minutes. Kepler is GitKraken’s answer to that traffic jam.
LLM cost optimization: 7 strategies to cut inference spend
LLM cost optimization is the practice of cutting what you spend on large language models, mostly inference, without losing the quality that makes the AI worth running. The biggest levers are routing requests to cheaper models, caching repeated tokens, batching anything that can wait, trimming prompts, right-sizing models, cutting calls you do not need, and putting one gateway and cost view in front of all of it.
What is FP&A? Financial planning and analysis in the AI spend era
FP&A stands for financial planning and analysis. It is the corporate finance function responsible for budgeting, forecasting, variance analysis, and decision support. If you're asking what is FP&A in practice: FP&A teams build the annual operating plan, project revenue and expenses, explain gaps between plan and actuals, and give leadership the numbers behind strategic decisions. Accounting reports what happened. FP&A models what happens next.
Software is a team sport. Most AI tools forgot that.
Every AI coding tool ships the same promise: your developers, faster. Autocomplete in the IDE, agents in the terminal, a working prototype before lunch. And it delivers, at least for the person holding the keyboard. The problem is that most of what it takes to ship software was never a solo activity, and that is the part the market keeps skipping.