Welcome to Eye on AI! In this edition…agentic AI at Brainstorm Tech…Microsoft to buy AI from Anthropic in partial shift from OpenAI…Oracle shares skyrocket on announcement of massive AI deals…AI is coming for YouTube creators.
I’m on my way back from Fortune Brainstorm Tech, where I spent the week in the mountains of Park City, Utah, with about 300 AI-focused executives—and I’m still thinking about AI agents. (I probably should be thinking about the wind in the evergreens and the first hints of fall color, but that’s life on the AI beat.)
One of the most popular panels I moderated was on AI agents: autonomous software systems that can perceive their environment, make decisions, and take actions toward goals with little or no human intervention. Full disclosure: The session was sponsored by Salesforce, which has a major stake in the space with its Agentforce platform.
But the conversation went far beyond one company’s pitch. Leaders from Zillow, Experian, and Okta joined to discuss how agentic AI is beginning to take shape inside large enterprises. Their consensus: Agents aren’t just souped-up chatbots. While it’s still early days, they represent a shift from tools that simply follow instructions to systems that can act on context—ideally within the guardrails companies set.
Shibani Ahuja, senior vice president of Enterprise IT Strategy at Salesforce, shared what I consider to be a really useful “maturity model” that helped me understand the evolution of AI agents within enterprise companies:
Nicholas Stevens, vice president of product for AI and home loans at Zillow, offered a concrete example of where the company is on this maturity scale—which, to my ear, sounded like Level 1 edging into Level 2. Zillow owns Follow Up Boss, one of the largest CRMs for real estate professionals, where AI is being used to lighten the load of human real estate agents juggling calls, notes, follow-ups, and property tours.
At first, Stevens explained, the tools are basic—summarizing calls or drafting a message that a human agent can edit. But over time, they begin taking on more autonomous tasks, like sending a pre-approval letter or booking a tour. The progression isn’t just about the technology, he said, but about user trust. Many real estate agents prefer to review and customize outputs early on. Yet as the system learns their tone, quirks, and even emoji preferences, they become more comfortable handing off responsibility.
Kathleen Peters, chief innovation officer at Experian, said the company already has conversational agents that recommend actions and now carry them out—for example, walking a customer through the steps to boost a credit score. On the B2B side, Experian’s agents are helping financial institutions assess risk in loan and credit card underwriting, shifting decision-making power from data science teams to product managers and even retail bankers. Looking ahead, Peters said the next step is “agents talking to agents, especially with a number of our partners like Zillow.”
One of the things I’m particularly concerned about, though, is agent security—especially as companies start implement protocols like MCP (Model Context Protocol), a framework that standardizes how LLMs connect to and interact with external data sources, tools, and services. When I asked how many in the room had already implemented MCP in their organizations, many raised their hand. Bhawna Singh, chief technology officer of cybersecurity platform Okta, put my concern into perspective: She pointed out that before MCP, there wasn’t even a common understanding to start from regarding AI agents. While it’s early days, she said, and she acknowledged that agentic AI development opens up a new attack surface that boosts the risk of malicious or spoofed agents, having a standard is a big step forward. Clearly, though, many enterprises won’t be able to get to Level 3 or Level 4 without more confidence in the safety and security of protocols like MCP.
Salesforce’s Ahuja agreed that the future was not about building agents that are “wild and free.” Instead, it would be about setting deterministic parameters for the things that must be consistent and reliable, while allowing adaptive reasoning where richer, more context-dependent answers are valuable.
With that, here’s the rest of the AI news.
