Building an AI demo now takes a day. Getting it safely into production still takes real work. In Episode 2 of the Ready, Set, Runtime podcast, AHEAD Field CTO MK Kolbrener talks with Kurt Boden and Habeeba Mansour, two AI consultants who work inside client engagements every week.
The demo got easy
Generative AI tools have shortened the path from idea to working prototype dramatically. Habeeba sees that change firsthand in her client work.
“We went from ‘I need a month to be able to create something that I can show to other people’ to ‘if you just give me 24 hours, you can turn something around that looks pretty good.’”
Habeeba Mansour
The bar for deployment went up
A fast demo proves an idea is possible. Whether it can hold up under real users, real data, and real security requirements is a separate question, and buyers have started asking it earlier. Clients now say,
“I don’t even want to see just a toy demo anymore. I want to talk about the production process. What are all of the nitty-gritty things that are going to come back and bite me?”
Kurt Boden
Trust decides how much you hand over
“It’s like a colleague that you gain trust with,” Kurt says. He describes the path from approving every action, to allowing all actions, to handing over heavier lifts. He also believes in letting “the rocket ship blow up on the launchpad” and learning by shipping. The pattern mirrors how teams onboard a new hire. Oversight starts tight and loosens as the agent proves reliable.
The hard part is what comes after launch
Some clients have hundreds of agents running in the cloud. “Operationalizing something like that and the governance around it is a nightmare.” If nobody tracks who owns what, the result is agent sprawl, and nobody is responsible when a security event happens. It echoes the shadow IT problem of earlier eras, except that these systems can act on their own.
MK adds his prediction: “The courts are going to basically say that the companies are accountable for the agents.” His biggest worry is agents permitted to build other agents: “That’s where I start to see a real loss of control.”
Old patterns, new labels
On the Model Context Protocol (MCP), Habeeba says it is “just another way to do an API.” Kurt says, “What’s old is new again,” and that clear metrics and strong engineering values still decide outcomes. MCP is an open standard that lets AI applications connect to outside tools and data through a common interface, which is why Habeeba sees it as familiar territory.
Two views on the road ahead
Kurt is a self-described optimist who believes “humanity skews to the good.” Habeeba is more skeptical and compares the AI race to nuclear weapon development. Both want guardrails. Their outlooks differ, but their conclusion matches. Clear ownership, monitoring, and access limits are what let teams move quickly without losing control.
Before you scale agents, ask three questions: who owns each one, how is it monitored, and what is it allowed to touch?
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