What would make you comfortable giving an AI agent the authority to act inside a live business process?
In this episode of Tech Talks Daily, I speak with Valerie Henderson, CEO of Caylent, about what has to happen between a successful demonstration and a system a business can depend on. The conversation centers on production readiness, including access controls, spending limits, traceability, workflow design, and the people responsible for the outcome.
Valerie describes why introducing agents requires changes to how work gets done. If an organization adds new technology without reconsidering the handoffs and decisions around it, the hoped-for productivity gains can remain difficult to achieve. Her advice is to begin with a bounded problem where automating a few steps could provide a clear benefit, then build on the evidence rather than starting with an ambitious network of agents.
We discuss the difference between read-only workloads and agents that can alter live systems. Valerie says many customers are already comfortable with agents retrieving information, while autonomous operations such as self-healing infrastructure are concentrated among the most advanced customers she sees. Those observations reflect her experience with Caylent engagements. They do not establish a universal level of enterprise adoption.
For me, one of the most useful questions is what meaningful human oversight looks like. Valerie is clear that people remain accountable for the instructions and access they give agents. She emphasizes deliberate testing, appropriate governance, and the ability to understand what a system has done. We also discuss how agents can repeat work without producing anything useful, creating a bill that needs to be considered alongside accuracy and reliability.
The conversation moves into Caylent's own operating model. Valerie describes moving away from assumptions built around human time and materials, making existing technical knowledge easier to reuse, and changing delivery incentives to reward speed and customer satisfaction. It is a practical reminder that organizations adopting AI may need to reconsider what they reward as well as which tools they buy.
She also shares a customer example involving Smarsh. According to Valerie, redesigning its software delivery process with agentic AI freed engineering capacity that could be reinvested in the business. She describes that capacity in financial terms, but the interview does not establish a measurement period or separate capacity value from realized cash savings. The useful lesson is the connection between a defined objective and a decision about how to use the resulting capacity.
We touch on Caylent's agentic AI readiness research, while keeping the broader discussion focused on practical experience. The useful question for leaders is what controls and evidence should come before greater autonomy, and how they will reassess that decision as the system changes.
Finally, Valerie explains how she thinks about success for employees as well as customers. She wants technologists to have time for difficult problems and opportunities to develop their skills. She also argues that customers should be able to explain the value of the work they have undertaken. Model performance is part of that assessment, alongside the environment, permissions, costs, and business process in which the agent operates.
I would love to hear how your team is making these decisions, so please share your thoughts. Which responsibilities are you ready to delegate to an AI agent, and what evidence would you need before going further?