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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.

Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues, reducing operational costs by 30%. That shift points toward a zero-friction workplace, where employees do not have to navigate support just to get back to work.

Enterprise conversations today are dominated by one phrase: Agentic AI. Across boardrooms and innovation labs, organizations are experimenting with copilots, autonomous agents, and AI bots capable of resolving tickets, recommending actions, and orchestrating complex processes. The promise is real — AI that doesn't just generate insights, but takes meaningful action. Here's the uncomfortable truth: most enterprises are architecturally unprepared for the agentic future they're trying to build.

llm-d is an open source solution for managing high-scale, high-performance Large Language Model (LLM) deployments. LLMs are at the heart of generative AI – so when you chat with ChatGPT or Gemini, you’re talking to an LLM. Simple LLM deployments – where an LLM is deployed to a single server – can suffer from latency issues, even with just one user. This can be because of lack of memory-bandwidth on the server, or because of KV cache pressure on system memory.

The role of a Site Reliability Engineer (SRE) is evolving. The focus has shifted from simply working harder during an outage; A new kind of teammate is here to help: the SRE Agent. But what are the key differences when you compare an SRE agent versus a traditional site reliability engineer? This isn’t just a superficial change. It signifies a fundamental alteration in how teams construct and sustain dependable services.