12 stories
Sovereign AI control is becoming a business continuity issue as autonomous agents take on essential enterprise workflows. Keeping data within national boundaries addresses part of the challenge, but organizations also need control over agent behavior, external dependencies and the economics of…
The full-stack AI cloud market is about more than access to graphics processing units: it also encompasses the tools needed to improve models and agents in production. Providers are seeking to distinguish themselves through the tooling, partners and expertise they combine with hardware.
On-device personal AI requires models and agent software that can operate within fixed hardware limits. Developers also need ways to keep those systems improving after deployment. Model builders are rethinking architectures designed around elastic cloud capacity.
AI model training can leave startups managing infrastructure alongside the products they are trying to build. Willow Care Inc., the company behind AI dictation app Willow Voice, chose CoreWeave Inc. for its forward-deployed support rather than compute alone.
Open-weight models keep multiplying, and the AI coding harness around them is becoming just as important to get right. Developers and enterprises are trading off cost against capability. The harness helps translate a model’s capabilities into useful work. Cline Bot Inc.
AI infrastructure reliability depends on how well compute, networking, power and cooling operate together. Turning early hardware breakthroughs into repeatable deployments requires coordination across engineering, manufacturing and operations.
Agentic AI infrastructure must support the work between a model’s decisions as agents move from answering questions to executing tasks. Central processing units handle much of that execution, while graphics processing units power model reasoning.
Agent workload isolation is becoming a practical infrastructure challenge as research teams move beyond training models to running code and testing agents. Systems built for intensive computation must now accommodate workloads that interact with tools, storage and other services.
AI factory economics increasingly depend on more than access to high-performance graphics processing units. As agentic systems draw on multiple models, databases and tools, the entire data center must work as one computing system.
An agentic security strategy must account for software that can act on legitimate instructions in unexpected ways. Agents can use credentials, call tools and choose their own routes toward a goal, expanding the paths security teams need to watch.
As enterprises face AI-driven cyberattacks, AI security governance must control autonomous systems that can reach data, connect applications and act on users’ behalf. That makes human accountability a core security requirement.
As artificial intelligence matures, the security guardrails — and the computer system itself — are changing. This week, the conversation centered around agents going rogue.
Showing 12 of 12 stories
You're all caught up.
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news
Join 15,000+ AI enthusiasts
