Everyone is talking about AI models.
But after spending time at @VAST_Data's VAST Forward, one thing became very clear:
Agentic AI isn’t a model problem. It’s a data platform architecture problem.
As organizations move beyond copilots into AI systems that take action, the real bottleneck isn’t the LLM.
It’s the platform underneath:
• governed data access
• high-speed data platform services
• persistent AI memory
• GPU-accelerated data services
• distributed infrastructure that can manage fleets of AI systems
At VAST Forward, the conversation wasn’t about bigger models.
It was about how the data platform becomes the runtime for AI agents.
That shift is huge.
The companies that win in the next wave of enterprise AI won’t just deploy better models; they’ll build data platforms designed for agentic systems.
I break down the four architectural shifts emerging from VAST Forward and what they mean for platform teams, CTOs, and data engineers.
👇 Short video (6 min) and Full article below https://t.co/Gh6ROAzxez
BU spent a year window shopping while much of college athletics embraced revenue sharing.
Now, the Terriers are in.
@htconnors on why BU waited to opt into the House v. NCAA settlement, and what it means for the future of Terrier hockey.
🔗 https://t.co/ryBV5yeRdn
Hey @elonmusk - all the AI agents were safe in their "sandbox" on @Delta flight 1410 from BOS to AUS - great for a nerd bird between cities. Who knows what AI chaos would have transpired!
This with @stripe buying @OpenRouter makes all the sense in the world. Simplification of the AI stack is a must!
SO WHAT?
What will be interesting to see is will companies monitize their own Intellectual Property the via API and use these platforms for the transaction processing. More to come here!
Agents are getting really good at reasoning and taking action. Paying for something has been a lot harder.
AgentCore Payments is now GA with @coinbase and @stripe, giving agents a managed way to pay for APIs, content, data feeds, and even other agents, with spending limits and observability built in.
The @databricks CustomerLake CDP makes a lot of sense, given it is probably the number one use case I have used Databricks for and many others. @tasso did a good job breaking it down in customer360, like 1st & 3rd party ID resolution, & Campaign Agents - really complete vision
@databricks@kuhlenhuth@SpaceX Deep diving into Unity AI Gateway - agent registry, contextual policies, and the concept of budgets - smart routing, which is also coming to vLLM - agent tracing via MLflow | also adding Memory Service & Sandbox
AgentBricks now taking center stage for @databricks customers - explaining the how dev teams get stuck on 1/ frontier changes constantly 2/ competitive advantage is data in silos 3/ most privileged actors, sometimes too much from @kuhlenhuth - announcing Grok 4 from @SpaceX too
Giving the ability to share sessions and use multi-agent workflows with transparency extended to policies - such as the cost of each request to the agents.
This is very cool AI tech to help simplify and secure agents. It's not everything, but it is going in the right direction. Hope to see this in the @linuxfoundation AAIF ecosystem