Tech Talks Daily
00:00:04 · Neil C. Hughes
[00:00:04] S0: What would you need to see before allowing an AI agent to act inside your business? A convincing demo might show that it can complete a task. But production, that's something that brings questions about access, spending, and accountability. And, ultimately, what happens when something could go wrong? Well, today, I'm joined by Valerie Henderson. She is the CEO of Caylent. And together, we're gonna discuss how organizations are moving from AI assistance towards greater autonomy. And we will also talk about starting with bounded work, measuring useful outcomes, and giving people a role in some of those decisions. And Valerie will also share how Caylent has changed its own delivery model and incentives as AI becomes part of its own software development. So for anyone facing any form of pressure to deploy agents quickly, today's conversation will offer a way to take a step back and think about readiness beyond model performance. Yep. You don't need another impressive demo to appreciate the problem, but you do need to know what the system can do, what it should do, and who's responsible for its actions. And that feels like the perfect moment to introduce you to Valerie right now. So thank you for joining me on the podcast today. First of all, can you tell everyone listening a little about who you are, what you do, especially, after stepping into the role of CEO back in March? Tell me. Tell everyone listening about you.
00:01:41 · Valerie Henderson, Neil C. Hughes
[00:01:41] S1: Yes. Valerie Henderson, CEO of Caylent. I've actually been at Caylent for over five years now, and I've had the pleasure of being here through a tremendous growth curve. But, yes, I did just step into the CEO role earlier this year. It's funny. Time is a construct, so I don't even think about it at this point. But Caylent is a really special organization. We are a cloud native AI professional services company that works really closely with our customers to bring their ideas to outcomes. And, well, that's, you know, the 10,000 foot view. We have an incredible amount of capabilities to help customers in a very noisy, overwhelming time and space right now actually drive ROI from this technology. And anytime there's been a major shift in technology, there's been a seam created between the tech platform and the business outcomes. And I I believe the reason this time it's different is AI said, it's all gonna be easy. It's just... It's AI, and it's... We've seen that that's not the case because, number one, humans changing behavior. You've gotta change workflows, operations, all of that. It's a lot of change that's needed to get the most out of this technology. [00:02:57] S0: It really is. And time is just a construct. I'm gonna be using that line. Believe me. But Yeah. [00:03:02] S1: Palant time is just a different beast for sure.
00:03:02 · Valerie Henderson, Neil C. Hughes
[00:03:02] S1: Palant time is just a different beast for sure. [00:03:05] S0: Now if we have a look at that, I think many companies are moving on from just asking questions to now asking agents to go and act on their behalf and complete tasks. So I've got to ask from what you... Where you're sitting here, everything that you've seen over the last few years, what is changing for a business when software goes from just advising to actually acting on its own behalf?
00:03:27 · Valerie Henderson
[00:03:27] S1: Yeah. Well, in short, everything. So I know that that can be really overwhelming for any customer to hear, and that's part of why Caylent exists is to break that down and help customers understand what needs to change first. Obviously, for decades, work has run on a cycle of handoffs and sinks, and now things are happening concurrently and seamlessly. And humans get to have the most effective conversation, which means they can make decisions and quickly correcting what agents did. So we think about how we're helping customers do build and then actually just get into the point where they're mostly just making decisions really quickly as the human in the loop. We talk also a lot about the the steam engine to the electric engine. And when the electric engine came out, they just dropped it in to where the steam engine was. They changed nothing about the factory floor, the different pieces, parts of that workflow, and they didn't get productivity for thirty plus years. So that's a huge part of what needs to happen. You have to redesign processes. So agents, human workers, and the legacy systems can coexist with a more clear view on the human in the loop and the change management. It's the operating system piece. And we did a survey, and it shows 60% almost of enterprise leaders are ready to let agents run autonomously in production, but only with defined guardrails. And we partner with AWS. We only build on AWS. It's the one cloud we build on. And that's part of why I think Bedrock has been such a powerful solution just with those built in guardrails and really getting people more security, more operational structure from the beginning.
00:05:18 · Neil C. Hughes
[00:05:18] S0: If we also take a look at that, I've been to a few tech conferences recently, and one that struck me was last year, it was an industrial AI event, and they were talking about individual agents and working for individuals. This year, that event, they were talking about teams of agents. In twelve months, it's gone from individual agents to absolutely teams of them there. So we're... Before a company deploys autonomous agents, from what you're seeing out there, what are the big decisions leaderships need to to make and what it can access? And most importantly, who is accountable? That is a big question that many struggle to answer now as well if one of those agents has an unexpected delivery item there. What's the best way around that, and where should they start to avoid mistakes later on?
00:06:01 · Valerie Henderson
[00:06:01] S1: I think I have... We've always told our customers to think about a problem that a couple of steps being automated would add a huge amount of value and start there. Let's start very simple, and then I think if you start simple, you can build on that. The teams of agents and how they're working together, I think it's very exciting. But to your point, the access they have is a huge part of them actually being able to be productive. And we find that in terms of accountability, there's a reason why there's a human in the loop. The humans are the ones that are telling these agents what to do and where to go and why to do it. And at the end of the day, we have to be held accountable. And I believe that you should assume nothing. You have to be very intentional about what you are doing and building and testing before you're moving anything into production. That hasn't changed. That's been the same for a very, very long time, and it doesn't change now. And the security piece, I think that this is such a very full time for companies in cyber and how they're thinking about evolving their platforms. I... That's one area I'm just very fascinated to see how that evolves because it's just gonna continue to need a heightened level of inspection and visibility. And I always like to talk about the known unknowns. And right now, there are a lot of them. And it can be overwhelming to customers, which is why I would say start small. We often start in HR or go to market motions where there isn't necessarily gonna be some major issue on these agents and the work that they're doing. And if they make some mistakes, which they will, that's okay before we're moving things into more business critical applications and departments.