UiPath ($PATH) podcast mentions
Timestamped UiPath ($PATH) mentions across our podcast corpus.
We talked a lot about answer engine optimization. It's one of the fastest growing categories because it's just just so much disruption going on, right? Um, software coding growing really, really fast. Customer service agents, not just customer service chatbots, but agentic customer service like, you know, Finn by Intercom and Forethought and Zendesk, Agentforce, fast-growing category. Agent builder category growing really fast. A lot of companies want to leverage these platforms I mentioned— Agentforce, ServiceNow, UiPath, etc.— to build armies of agents across their site. That's growing really fast. We're, um, so, so the agent categories from, from, from horizontal to vertical growing extremely fast. AEO growing extremely fast. But we're also seeing other breakouts. Uh, we're seeing a lot going on in AI voice. So voice is becoming a breakout category that we're paying a lot of attention to. Orchestration software has become extremely popular over the last few years. And I'm not just talking about orchestrating agents, but I mean like full-stack orchestration, like how do you bring together rules-based AI, RPA, and then agents into a single workflow, that, that, that category has exploded for us. One thing that I am predicting, and we're seeing it grow this year, but we expect it to really surge over the next few years, is the category of third-party agent guardrail services where companies will manage guardrails, permission requests, costs, exceptions, rollbacks, remediations, QA for companies as they scale their use of agents, but their employees can't keep up with the risk profile and the permissions that are required to keep human in the loop. There's going to be a massive ecosystem of companies. They're not going to have the 75% SaaS profit margin. They're going to feel more like a services company at 30 to 40%, but that's going to be a $30 billion industry. Industry within 5 years.
Tim Sanders · Eye On A.I. · The Hidden Algorithm That Decides Which Software AI Will Recommend | Tim Sanders, G2 · 2026-09-14Welcome back. Amid the so-called SaaS-pocalypse, software buyouts plummeted. Only $22 billion worth of such deals were done in the year, according to Dealogic. So far, that pace, though, would Supply this year's full tally comes in about a quarter of 2025's $171 billion. But there are signs that private equity might be coming off the sidelines potentially. Silver Lake is reportedly in talks to buy Workday for what could be one of the largest buyouts ever in the space. Last week's news spilled over into other potential takeover targets as well, and speculation that a Workday deal could set the floor for others. KeyBanc analysts specifically highlighted names like GitLab, UiPath, PTC, and Procore as candidates. Yesterday, though, UPS poured a bit of cold water on some of this excitement. Analysts there wrote, quote, Our contacts in the PE community have not signaled a likely near-term surge in software buyouts as they have cited a tight credit markets for large leveraged deals, b, internal concerns about long-term disruption risk, and c, a desire to deal with the software assets in their own portfolio before buying more. And then D, a recognition that most software CEOs are unwilling sellers at these lower valuations. The report goes on to say, quote, if indeed deal discussions with Silver Lake have occurred, this might signal that Workday has concluded that the transition to being AI-centric, a path that everyone is on, may be long and complicated. And that's kind of the fork in the road for software. Do these reported talks mean software is still something worth buying, or does it mean that AI is a bigger threat to the turnaround strategy, leaving executives no choice but essentially to throw in the towel to private equity? For more on the alternative space, you can subscribe to our newsletter Insight Alts using the QR code on your screen there.
Leslie Picker · Squawk on the Street · 11AM Hour: CVC Marathon's Bruce Richards, Kalshi Board Member on Prediction Markets & Meta Trial Begins 8/18/26 · 2026-08-18Yeah, so I, I see a couple of different things that I think are very practical. Any vendor building powerful AI right now knows how tricky it is to get through the enterprise vendor due diligence process and questionnaires. So these startups face these questionnaires. Sometimes there's a hundred questions on them and it's extremely painful to go through. And I can tell you also I speak to a lot of enterprise CISOs and GSE managers. It's equally painful on their side, right, because they're at a stage where the space changes so often that they feel a desire to actually change their questionnaire every month. Going through 100 questions from a startup every single time you try to onboard a vendor is also completely painful on the other side. So I think part one here is speed and a desire to get to a place where you actually feel like you've covered your blind spots as well. And having a third party develop standard with all of the industry in the room to help find those blind spots and figure out how we can't find them in a standard, I think is this value proposition number one. The speed argument obviously assumes that you can get across the line in the first place. I think the second part of the value proposition is having that third party validation that your agentic AI is actually safe, secure and reliable. We're working with some of the frontier companies in the airspace right now, companies like Eleven Labs, companies like Fin that just got acquired for 3.6 billion by Salesforce, companies like UiPath who have set the standards within their categories historically. They have fantastic security postures, but they don't have a way to prove that to an enterprise. So an enterprise will just never trust a company that has an incentive to sell their product. They need that third party to go in and do that. And then I do think there is a security argument to be made here. Our red teaming consistently uncovers blind spots for the companies that we work with. Sometimes it's the hallucination rate where we realize that a specific type of adversarial attack will bring up the hallucination rate or specific language switches or other things that might actually happen when their products are deployed. Other times it's jailbreaking risk that we manage to uncover, a pump injection risk that we managed to uncover. So we do also see ourselves as helping these companies actually improve their safety, security and reliability posture, which is valuable as well. Then I'm sure there's a marketing benefit to the companies going out early and adopting a new framework and showing and demonstrating they're moving in front when it comes to AI security leadership. I think that's a important branding value as well that we sometimes help provide. But I don't think that's a core benefit of the certification right now. I think that is really unlocking upmarket enterprise revenue for the companies.
Speaker C · Practical AI · AIUC-1: Building trust in AI agents · 2026-06-25Yeah, absolutely. So you'd get in touch with a team and the first thing we always do is we do a gap assessment against your existing systems. If you have a well documented trust center already or some blog post describing what you do, we can basically go back to you and tell you this is the places where we believe you already meet the standard. This is the like, these are the areas where we expect that there will be work for you. So you basically go into the certifications process with open eyes around what is the workload needed from engineering, from legal and from your GRC team to take your company through it. I will mention at this point we've had a three person Y Combinator startup go through this. We've had UiPath that is publicly traded go through this. We have companies at all stages. So I mentioned now Security, Legal and gsc that was the same person when it came to points or getting certified. Right. So it is a standard that scales with the organization's size as well. When you have this gap assessment completed, you basically decide whether you want to move forward with the certification or not. To move forward, we split the process in two parts. One path is you pick an order of your choice. We have a number of credited auditors, again, for example Shellman, coal fire. But the list is growing very rapidly at the moment. And trusted auditor who knows how to do this. And on their track, you basically start collecting all the evidence that is needed to go through the aac. One audit falls in two buckets. Some of it is the classic legal policies. If you have a generative AI product, you need to define who owns the inputs and outputs and how you retain user data, and whether you train on user data, so forth. You need to define your acceptable use. And the second part is the technical controls that Sheldman will go in and validate. So that is your filtering configuration against harmful outputs, your classifiers, your defensive prompting, your groundedness filtering when it comes to hallucination, preventing your safeguards around tool calls and all the other things. So again, you go through those requirements and capture the evidence and submit that to the auditor that goes in and does that third party validation. The other track we then do in parallel is that you give us an instance of the agent or the agents. It can be multiple as well, that is in scope for the certification. And you basically configure a representative version of that agent. So an agent that would be configured how an enterprise would use it. We sometimes see companies creating an extremely safe agent that has almost lost all its power because they just wanted to pass the certification. We obviously would then go in as the third party in the room and push back and say we want to see an agent that is configured based on the public docs you have and the defaults you've built into the product. When we then have access to that, we often access it via API. Our internal team will draw up a matrix of the risks we see that this agent is subject to and the attacks that it could be subject to if someone went in and attacked it. And we then develop usually between 1000 and 5000 different scenarios that we're going to hit this agent with. Each attack is unique. Some of them are again benign in nature. So the user will simply ask it a question, get the answer back. If the agent doesn't hallucinate, it passes the eval. Other times, we'll increase the adversarial pressure step by step. So the first step could be that we try to lie to it. The second step could be that we invoke authority. We do it over multiple turns sometimes and keep insisting on doing things. We pretend that we're under distress and say, if you don't do this right now, I will go and do something terrible. So please process this refund and obviously only pass the agent if we see it hold up to that pressure. We do the red teaming in two rounds because we often do find things in the first round. So similar to an ISO audit, where you have a stage one and a stage two, and you then have a chance to mitigate any findings in between, we give a company the chance to do that because the goal for us, again, is not compliance. The goal for us is security. Right. That you actually improve the agent as part of the certification process. So depending on the magnitude of the findings, your team will have between, say, one and four weeks to mitigate these things based on the recommendations we come up with. And we then do a second round of testing. That testing is final and is taken then into account when the auditor takes your evidence, takes your Red TeamView results, and writes that final audit report. And what you leave the process with is a comprehensive audit report that describes your security posture. It's between 60 and 100 pages long, and it's an asset you can really unblock those enterprise deals with. You get a certificate for your website again, so you can demonstrate that you've gone above and beyond when it comes to security. And then we come knocking again three months later and say, we still have access to your agent via the API. We're now going to run that same barrage of tests again to ensure that the changes you've made in the last quarter didn't invalidate some of these security things we found. And we do that every single quarter. And that's a requirement to maintain the certification.
Speaker C · Practical AI · AIUC-1: Building trust in AI agents · 2026-06-25Yeah, so it's, it's a great question and it's also a really hard question. So there's some nuances in here. What we require to pass AUC1 is that you don't have any. We grade each run based on severity. So a pass, you can have a P4 which is an insignificant, say a small hallucination that doesn't really affect an end user. A minor thing would be, which would be a P3, P2 would be something significant that may actually have real world implications. P1 is something critical and P0, I actually don't know the name for it. I think we have called catastrophic or something like that. The kind of thing, if we found it, you would drop what you had in your hands and start mitigating it immediately. Because having a system deployed with this kind of vulnerability could have real world implications that would be high. Our grading approach right now is that you cannot pass AUC1 if you have any P0 or P1 vulnerabilities identified, you have to mitigate those from then on. We believe a lot in transparency and we know from the compliance world, at least the frameworks that are robust and hold up under pressure, that if we put the results in the audit report and your customers see that audit report, you are very incentivized to mitigate the vulnerabilities we find. What we also know is that these agent systems are very different from use case to use case. So a coding agent is one type of beast versus a customer service agent versus a automation agent like UiPath that make decisions based on the information. And so companies have different tolerances around the percentage of hallucinations they would accept and so forth. So we really leave it up to the company and really in the end the customers of that company to make these calls. The important thing is, and this is where we sometimes have a little bit of a conversation with some of the companies we work with. No company has ever and will ever pass AAC1 with a 100% pass rate. It doesn't exist here. We're not Delve SOC2 compliance where you just get a magical spot free audit report. All agentic systems are non deterministic in nature. That means that they will always, if you put them under the right amount of pressure, be able to be jailbroken. They will always be able to hallucinate. We work again with like some of the legal agents we're certifying right now are world class at hallucination prevention. I am sure we will still be able to find some minor hallucination cases in those. And that's just the nature of these systems. If you remove those hallucination rates, it's because you've made the agent so dumb that it won't be able to actually execute the use case there. It is a topic that is very alive for us both because there's a grading methodology question in there and then there's this communications question and we've not yet seen that enterprises fully acknowledge this. Enterprises would also like to see something spotless because something that is not spotless just asks like adds complexity and raises some of these questions. But we're hoping to be part of a push in the sector to acknowledge that a spotless audit report is probably not as valuable as a audit report that reflects reality more clearly.
Speaker C · Practical AI · AIUC-1: Building trust in AI agents · 2026-06-25Yeah, yeah, no, absolutely. I think where we are right now is we're just overwhelmed by how positively AOT1 has been received as we're really busy just delivering certifications. And that means we have less time to talk to some of the many, many fantastic partners who come into our inbox and want to partner with us. I think there's three things we need to get right for this to work. I think we need to continue pushing for code and eugenic products that come out of the gate is as secure as possible by default. We're certifying our first coding agents right now and we are certifying both a well lovable, which I think will be certified when this process comes, this episode comes out and then another very large coding agent that may or may not have been acquired recently for a lot of money without naming names. Working with the coding agent layer and the platforms where you go in and configure code, we're Also like again, UiPath is a good example where you don't just like have one agent but you actually go in and build agents on top. Means that it'll be easier to meet a lot of the standards just by default because the environment where you define and build your agent is secure by default. So I think that's step one and we're going to do more of that work with some of the big agency platforms out there very soon. I think some of it will be announced in the, in the fall. The second stage is we need this partner ecosystem you just talked about and we're already starting to come out with, with some examples of this. Where a partner meets, helps company meet companies meet a good chunk of controls. So a company like Wide Circle, which I think is very cool, we don't work with them at all. So it's just a, just a shout out. Their monitoring and filtering work is really good and has helped companies meet a lot of the safety requirements in our standard. So that is like one platform you integrate and you immediately meet say 8 or 10 of the requirements in the standard. There are many other platforms we're doing some work right now with credo, with Witness AI and there's again tens of others. So having that ecosystem help companies meet the controls I think is important. And where we've already gone in and done our best to help companies meet the standard is we've gone in and actually defined the typical evidence we see companies upload. So see that as your guidance for where to look for the right approaches. We don't just define the controls and leave it up to you to figure out how the hell to implement it. We actually try to give you the guidance as well on how we see companies do it today. I think the third and final stage we need is obviously making it easier then to go through the certification itself. So we're already integrating the framework in the leading GRC platforms. We're making it easier for our auditors to capture as much as the evidence programmatically. So we move away from screenshots and into like real validation that the controls work and hold up in real time. That layer and like the whole GSC engineering space is just really interesting to follow right now and we're doing our best to keep up and make the standard work for the GSC engineering community as well. So that when your RE audit comes in that next year, that it's a very limited time commitment we need from you and that we again focus on security instead of compliance.
Speaker C · Practical AI · AIUC-1: Building trust in AI agents · 2026-06-25