7 lessons from the first wave of agentic AI deployment: theCUBE + NYSE Wired's AI Agent Conference insights
The enterprise artificial intelligence stack is getting smarter — but is it getting the context it needs to make agentic AI deployment actually work? The defining problem of the agentic era might never have been building the agents. Instead, evidence is mounting that even the most capable AI systems stall without a clear strategic grounding […] The post 7 lessons from the first wave of agentic AI deployment: theCUBE + NYSE Wired’s AI Agent Conference insights appeared first on SiliconANGLE.
Key Takeaways
- What does agentic AI deployment really require?
Enterprise leaders reveal why context, data speed and governance now decide which AI agents succeed.
- You need to train an employee when they come into an organization - even if they're rock stars, you need to make sure you onboard them well," she said.
"Same thing when it comes to AI agents: You have to give them the business context so that they are going to be able to run well.
- Frontier AI models are only as effective as the business context they are given, and that context lives in human expertise that has rarely been systematically captured, Liu noted.
As building agents on top of frontier models becomes more common, the companies that stand out will be those with a clear customer problem to solve and a defensible competitive moat, according to Hasker.
- That speed matters because the agent still has to turn the retrieved data into a useful answer before the user loses patience.
- Token lock is the new vendor lock.
What does agentic AI deployment really require? Enterprise leaders reveal why context, data speed and governance now decide which AI agents succeed. UPDATED 20:37 EDT / MAY 11 2026 AI 7 lessons from the first wave of agentic AI deployment: theCUBE + NYSE Wired's AI Agent Conference insights by Emile Louw SHARE The enterprise artificial intelligence stack is getting smarter - but is it getting the context it needs to make agentic AI deployment actually work?
The defining problem of the agentic era might never have been building the agents. Instead, evidence is mounting that even the most capable AI systems stall without a clear strategic grounding and the proprietary organizational knowledge needed to support enterprise decisions - a problem that no frontier model solves on its own. The missing ingredient, it turns out, is not better technology but better context, according to Vanessa Liu , chair at Appen Ltd.
"Data is actually incredibly important for companies to be able to take advantage of AI. You need to train an employee when they come into an organization - even if they're rock stars, you need to make sure you onboard them well," she said. "Same thing when it comes to AI agents: You have to give them the business context so that they are going to be able to run well.
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