WEBVTT
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Hello and welcome to Full Tech Ahead.
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I'm your host, Amanda Razzani, and with me today, I'm excited to have Federico Romalo, founder and CEO of Density Labs.
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How are you doing today?
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Hello, Amanda.
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It's a pleasure to be here today.
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Happy to have you on the show.
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Can you share a little bit about your background and what do you do over there at Density Labs?
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Yeah, sure.
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So at Density Labs, what we're doing is AI engineering.
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We partner with companies that are trying to figure out how to leverage AI within the organization.
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We take all the hype away and we focused on business impact outcome.
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We track the return of investment of those efforts.
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The other thing we we do is we track the total cost of ownership.
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We've seen a lot of projects where the token consumption goes to the roof and then the cost for running these projects goes super high.
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So we we we work on that.
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Okay, great.
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So we're gonna talk today about your experience in working with these companies and what are some of the challenges and the ROI for software development companies when they're utilizing AI tools.
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I know everybody's trying to harness AI in the best way possible.
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So let's start with the fact, though, that roughly 95% of enterprise AI pilots never reach production.
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Why is that, do you think?
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It's a very interesting situation that is happening with that.
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The main reason is that they test the demo with the best case scenario, and then they don't consider all the use cases.
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That's the first issue.
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The other issue that is happening is that even though AI is a technology tool, it is actually an organizational change.
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And uh a lot of companies don't see it that way, and that's the the uh underlying reason or the root cause of why uh this type of projects fail.
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And the the last issue that I've seen is that because you have so many different possibilities of outcomes or use cases, what we start doing is setting up a model that evaluates another model.
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Uh, that way we reduce those uh that that complexity towards expected outcomes, expected behaviors.
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And then we set guardrails to to avoid that, right?
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But basically that that that issue that that that is that is the the core issue is that it becomes unwindling, unwindly complex when you have so many use cases and you don't have a way to track that the outcome is what you actually expect.
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Can you share a little bit more about you said create a model to review the the model?
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Can you share more about that?
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Yes, absolutely.
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So the first thing we need to understand is that the when we have agents executing and doing actions for us, right?
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What we are actually doing is we're providing tools to a model, to a large language model that is making those decisions and then executing for us, right?
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The issue is that these are non-deterministic outcomes, is what we're going to get, right?
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So, how can we test that the outcome is what we expect?
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What we do is we build uh it's called Eval LLM, and basically uh what we have is another model, and by model what I mean is uh thinking it of as an engine, right?
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Uh, it's the core of every um AI automation tool that we use that can that we can have a conversation or we can it can read images, there are different models, but basically what we do is we set up one model to analyze and make a decision to execute, and then another model to evaluate what the first model is going to do, and then make the decision.
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Okay, does that make sense or it doesn't make sense, right?
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The reason, technical reason we need to use two models is because what we want is to have uh let's call it second opinions, right?
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If we if we have the same models evaluating themselves, then we have the higher probability of them to be to agree to a wrong decision.
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Okay, so when putting AI agents into an engineering org, what are some of the things that break or some of the challenges that you see?
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Even though I am advocating for uh the use of AI, I believe that using at l as little AI as possible is the best approach.
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So when uh what are the best scenarios to leverage AI within an organization?
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So when we have uh business processes, right, um, and we want to automate them, we can use uh an AI agent to just automate the whole workflow, right?
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Or we can build software, we can build code that is actually going to automate that workflow, right?
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So what I am advocating is to build code to solve the automation uh for all the deterministic workflows.
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By deterministic, I mean uh think of math.
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Math is deterministic, one plus one equals two, right?
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It's always the same result.
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So when we have those types of rules, or we can uh set up those types of rules, then we can build code, we can build software that is going to run those automations.
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But when you have to analyze an email, understand the sentiment within it, or um think of it as intuition, you know, you have a problem and you don't know exactly what the outcome should be.
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So you have to uh intuitively find the outcome.
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So for for those problems, AI is great.
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So what I suggest is to build a software for the deterministic workflows and then use models as fallback only when we have a situation that is non-deterministic, right?
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That means we can save money on running tokens or on running models, and uh we can have a faster uh automation process and it's uh safer because when you have a model running um within your organization, somebody has to supervise what it's doing, right?
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Um no agent, no model can be accountable for anything, only humans can.
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So if we have code, it is easier to manage, it is easier to test and make sure that it works properly, right?
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And then the models, they can be um supervised, they can be uh tested, but the level of effort is much higher, and the cost of operating them is much higher, right?
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So you argue that AI agents are not necessarily good junior engineers.
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Explain that a little bit further.
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I see a lot of people using AI agents to develop all sorts of things.
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Where do they encounter problems doing that?
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Well, what I was explaining before was mostly about automating business processes.
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Um, what you're talking about, uh asking me now, is about why uh models are not good software engineers.
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And I think that one of the reasons is that the software engineers, what we do is we abstract, we build abstractions, layers of abstractions of a of a of a problem in real life, right?
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Um, and then we set uh expectations of behavior.
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What do we want the application to do?
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The models are not really good at those levels of abstraction, and they um they lose um they lose that that ability, right, uh of abstracting and understanding what is the core problem.
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They are becoming better though, but they're not there yet, right?
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So the quality of the code becomes uh lower than what a senior engineer could do, right?
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What I called the craftsmanship.
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Now, what is going on is that once you start building code with agents, now the the code is easier to be replaced.
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You can build new code easier because you have agents to help you with, right?
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So even though I I come from that craftsmanship of building high-quality code, now I'm learning that it becomes less relevant when you can just replace it faster, right?
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Um having said that, the key um uh key tip for successfully build software with agents is uh you need to be very, very specific on the specifications, right?
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The more specific, the better, because that's when you can do the analysis on how the application should work, and then the agent can focus on executing and building the application, right?
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So we are seeing the use of AI more and more.
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What are some of the cybersecurity risks, and how do leaders address that?
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The security agents can mimic human behavior, right?
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And that that is becoming an issue.
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It used to be that you could have captchas to to you know separate bots from humans.
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Now agents can overcome CAPTCHAS, they can overcome a lot of the behavior of patterns that we used to use to track bots and feed out cyber attacks.
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Uh, and that complexity is going to increase because the attackers, hackers are also using agents, so they're having they can elaborate, uh, they can build more elaborate uh attacks, right?
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Um on the other hand, on the on the safety part, what we're seeing is uh more increasingly complex applications that can detect attacks.
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Because the issue with with attacks is what happens if you have a lot of false positives, right?
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Then eventually somebody is going to turn off the alarm, right?
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Because who's going to listen for a tool that is not providing real threats, right?
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They're building tools to filter that and being being able to double check if it's actually a threat and can make decisions to autonomously uh fix the the attack, right?
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Or defend for the attack, right?
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Uh so that's going to be very interesting happening.
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And the other thing that I've seen happening is we have we used to have phishing for humans, now we have phishing for agents.
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So we have emails or or links or things like that that uh are target to agents to act on, right?
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And that that is becoming an issue, right?
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And the last thing we're going to see, or where we're seeing already, is um a higher volume of both attacks and uh defense actions because both can be automated now.
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So what tips do you have for business leaders when it comes to building and leading high trust engineering teams across countries and different time zones?
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The first thing is that building a culture around how to build software, understanding uh or being able to transmit to the software engineers that what they're building has an impact to the users, what is the value for the user becomes the most important question that the developer should ask, right?
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Once they understand that, then the developers can leverage agents to build the software, right?
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To build the tests, to test specifically the value for the user, right?
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To spend additional effort on testing that, right?
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Because if you do that, then the application you're going to build is going to be uh more useful for the user, right?
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Um, so that's the first part.
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Uh on the business process automations, I think that the core suggestion that I can give is think of it as an organizational transformation.
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It's not only a technology implementation, right?
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And getting the team to adapt to these new tools, right?
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There are organizations that uh are capable of those of adapt, uh adapting, and that makes them uh leverage AI faster, and they can have a competitive advantage over other organizations that are still trying to figure that out.
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Fantastic.
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Well, if there was one key takeaway you could leave our audience with today, what would that be?
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Think of AI as a new hire that doesn't have accountability.
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Use uh or or lean on the context of your team, right?
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If you have a team with long tenure, lean on that because they know the business, they understand how everything works.
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So what they they need to think is that the new responsibility of the humans is going to be to manage those agents and to guide them when those agents are making a mistake within the business process automation.
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It also applies for software, right?
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But uh, I think that's that's the core takeaway that I can give is lean on that on that team, lean on the humans and help them upskill to use AI as augmentation instead of replacement.
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Absolutely.
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Well, thank you so much for coming on the show and sharing your insights.
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Absolutely.
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A pleasure to be here, Amanda.
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And thank you to our audience.
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If you have any questions or comments, leave them below, and I'll make sure to respond as soon as possible.
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And have a wonderful day.
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Hello and welcome to Full Tech Ahead.
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I'm your host, Amanda Razzani, and with me today, I'm excited to have Federico Romalo, founder and CEO of Density Labs.
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How are you doing today?
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Hello, Amanda.
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It's a pleasure to be here today.
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Happy to have you on the show.
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Can you share a little bit about your background and what do you do over there at Density Labs?
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Yeah, sure.
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So at Density Labs, what we're doing is AI engineering.
00:00:48.880 --> 00:00:57.600
We partner with companies that are trying to figure out how to leverage AI within the organization.
00:00:57.920 --> 00:01:03.759
We take all the hype away and we focused on business impact outcome.
00:01:04.799 --> 00:01:08.799
We track the return of investment of those efforts.
00:01:08.959 --> 00:01:13.040
The other thing we we do is we track the total cost of ownership.
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We've seen a lot of projects where the token consumption goes to the roof and then the cost for running these projects goes super high.
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So we we we work on that.
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Okay, great.
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So we're gonna talk today about your experience in working with these companies and what are some of the challenges and the ROI for software development companies when they're utilizing AI tools.
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I know everybody's trying to harness AI in the best way possible.
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So let's start with the fact, though, that roughly 95% of enterprise AI pilots never reach production.
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Why is that, do you think?
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It's a very interesting situation that is happening with that.
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The main reason is that they test the demo with the best case scenario, and then they don't consider all the use cases.
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That's the first issue.
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The other issue that is happening is that even though AI is a technology tool, it is actually an organizational change.
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And uh a lot of companies don't see it that way, and that's the the uh underlying reason or the root cause of why uh this type of projects fail.
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And the the last issue that I've seen is that because you have so many different possibilities of outcomes or use cases, what we start doing is setting up a model that evaluates another model.
00:02:40.479 --> 00:02:49.280
Uh, that way we reduce those uh that that complexity towards expected outcomes, expected behaviors.
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And then we set guardrails to to avoid that, right?
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But basically that that that issue that that that is that is the the core issue is that it becomes unwindling, unwindly complex when you have so many use cases and you don't have a way to track that the outcome is what you actually expect.
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Can you share a little bit more about you said create a model to review the the model?
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Can you share more about that?
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Yes, absolutely.
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So the first thing we need to understand is that the when we have agents executing and doing actions for us, right?
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What we are actually doing is we're providing tools to a model, to a large language model that is making those decisions and then executing for us, right?
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The issue is that these are non-deterministic outcomes, is what we're going to get, right?
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So, how can we test that the outcome is what we expect?
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What we do is we build uh it's called Eval LLM, and basically uh what we have is another model, and by model what I mean is uh thinking it of as an engine, right?
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Uh, it's the core of every um AI automation tool that we use that can that we can have a conversation or we can it can read images, there are different models, but basically what we do is we set up one model to analyze and make a decision to execute, and then another model to evaluate what the first model is going to do, and then make the decision.
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Okay, does that make sense or it doesn't make sense, right?
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The reason, technical reason we need to use two models is because what we want is to have uh let's call it second opinions, right?
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If we if we have the same models evaluating themselves, then we have the higher probability of them to be to agree to a wrong decision.
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Okay, so when putting AI agents into an engineering org, what are some of the things that break or some of the challenges that you see?
00:05:03.680 --> 00:05:14.879
Even though I am advocating for uh the use of AI, I believe that using at l as little AI as possible is the best approach.
00:05:15.120 --> 00:05:20.959
So when uh what are the best scenarios to leverage AI within an organization?
00:05:21.199 --> 00:05:34.480
So when we have uh business processes, right, um, and we want to automate them, we can use uh an AI agent to just automate the whole workflow, right?
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Or we can build software, we can build code that is actually going to automate that workflow, right?
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So what I am advocating is to build code to solve the automation uh for all the deterministic workflows.
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By deterministic, I mean uh think of math.
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Math is deterministic, one plus one equals two, right?
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It's always the same result.
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So when we have those types of rules, or we can uh set up those types of rules, then we can build code, we can build software that is going to run those automations.
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But when you have to analyze an email, understand the sentiment within it, or um think of it as intuition, you know, you have a problem and you don't know exactly what the outcome should be.
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So you have to uh intuitively find the outcome.
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So for for those problems, AI is great.
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So what I suggest is to build a software for the deterministic workflows and then use models as fallback only when we have a situation that is non-deterministic, right?
00:06:48.480 --> 00:07:08.720
That means we can save money on running tokens or on running models, and uh we can have a faster uh automation process and it's uh safer because when you have a model running um within your organization, somebody has to supervise what it's doing, right?
00:07:09.199 --> 00:07:15.279
Um no agent, no model can be accountable for anything, only humans can.
00:07:15.519 --> 00:07:24.079
So if we have code, it is easier to manage, it is easier to test and make sure that it works properly, right?
00:07:24.399 --> 00:07:37.600
And then the models, they can be um supervised, they can be uh tested, but the level of effort is much higher, and the cost of operating them is much higher, right?
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So you argue that AI agents are not necessarily good junior engineers.
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Explain that a little bit further.
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I see a lot of people using AI agents to develop all sorts of things.
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Where do they encounter problems doing that?
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Well, what I was explaining before was mostly about automating business processes.
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Um, what you're talking about, uh asking me now, is about why uh models are not good software engineers.
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And I think that one of the reasons is that the software engineers, what we do is we abstract, we build abstractions, layers of abstractions of a of a of a problem in real life, right?
00:08:21.439 --> 00:08:25.759
Um, and then we set uh expectations of behavior.
00:08:25.920 --> 00:08:28.160
What do we want the application to do?
00:08:28.399 --> 00:08:41.360
The models are not really good at those levels of abstraction, and they um they lose um they lose that that ability, right, uh of abstracting and understanding what is the core problem.
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They are becoming better though, but they're not there yet, right?
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So the quality of the code becomes uh lower than what a senior engineer could do, right?
00:08:52.240 --> 00:08:54.159
What I called the craftsmanship.
00:08:54.320 --> 00:09:03.840
Now, what is going on is that once you start building code with agents, now the the code is easier to be replaced.
00:09:04.000 --> 00:09:08.639
You can build new code easier because you have agents to help you with, right?
00:09:08.879 --> 00:09:21.200
So even though I I come from that craftsmanship of building high-quality code, now I'm learning that it becomes less relevant when you can just replace it faster, right?
00:09:21.600 --> 00:09:38.000
Um having said that, the key um uh key tip for successfully build software with agents is uh you need to be very, very specific on the specifications, right?
00:09:38.240 --> 00:09:51.919
The more specific, the better, because that's when you can do the analysis on how the application should work, and then the agent can focus on executing and building the application, right?
00:09:52.240 --> 00:09:55.120
So we are seeing the use of AI more and more.
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What are some of the cybersecurity risks, and how do leaders address that?
00:10:00.159 --> 00:10:04.879
The security agents can mimic human behavior, right?
00:10:05.120 --> 00:10:06.720
And that that is becoming an issue.
00:10:06.879 --> 00:10:12.559
It used to be that you could have captchas to to you know separate bots from humans.
00:10:12.799 --> 00:10:26.159
Now agents can overcome CAPTCHAS, they can overcome a lot of the behavior of patterns that we used to use to track bots and feed out cyber attacks.
00:10:26.399 --> 00:10:38.240
Uh, and that complexity is going to increase because the attackers, hackers are also using agents, so they're having they can elaborate, uh, they can build more elaborate uh attacks, right?
00:10:38.720 --> 00:10:49.919
Um on the other hand, on the on the safety part, what we're seeing is uh more increasingly complex applications that can detect attacks.
00:10:50.080 --> 00:10:54.320
Because the issue with with attacks is what happens if you have a lot of false positives, right?
00:10:54.480 --> 00:10:58.000
Then eventually somebody is going to turn off the alarm, right?
00:10:58.320 --> 00:11:03.600
Because who's going to listen for a tool that is not providing real threats, right?
00:11:03.759 --> 00:11:16.000
They're building tools to filter that and being being able to double check if it's actually a threat and can make decisions to autonomously uh fix the the attack, right?
00:11:16.080 --> 00:11:18.879
Or defend for the attack, right?
00:11:19.120 --> 00:11:21.840
Uh so that's going to be very interesting happening.
00:11:21.919 --> 00:11:29.519
And the other thing that I've seen happening is we have we used to have phishing for humans, now we have phishing for agents.
00:11:29.600 --> 00:11:39.840
So we have emails or or links or things like that that uh are target to agents to act on, right?
00:11:40.240 --> 00:11:42.960
And that that is becoming an issue, right?
00:11:43.120 --> 00:11:54.480
And the last thing we're going to see, or where we're seeing already, is um a higher volume of both attacks and uh defense actions because both can be automated now.
00:11:55.840 --> 00:12:05.840
So what tips do you have for business leaders when it comes to building and leading high trust engineering teams across countries and different time zones?
00:12:06.559 --> 00:12:27.519
The first thing is that building a culture around how to build software, understanding uh or being able to transmit to the software engineers that what they're building has an impact to the users, what is the value for the user becomes the most important question that the developer should ask, right?
00:12:27.679 --> 00:12:34.559
Once they understand that, then the developers can leverage agents to build the software, right?
00:12:34.639 --> 00:12:39.919
To build the tests, to test specifically the value for the user, right?
00:12:40.000 --> 00:12:42.879
To spend additional effort on testing that, right?
00:12:43.039 --> 00:12:49.600
Because if you do that, then the application you're going to build is going to be uh more useful for the user, right?
00:12:49.759 --> 00:12:51.759
Um, so that's the first part.
00:12:52.320 --> 00:13:01.840
Uh on the business process automations, I think that the core suggestion that I can give is think of it as an organizational transformation.
00:13:02.000 --> 00:13:05.360
It's not only a technology implementation, right?
00:13:05.840 --> 00:13:10.639
And getting the team to adapt to these new tools, right?
00:13:10.799 --> 00:13:29.200
There are organizations that uh are capable of those of adapt, uh adapting, and that makes them uh leverage AI faster, and they can have a competitive advantage over other organizations that are still trying to figure that out.
00:13:29.679 --> 00:13:30.559
Fantastic.
00:13:30.720 --> 00:13:35.440
Well, if there was one key takeaway you could leave our audience with today, what would that be?
00:13:35.759 --> 00:13:40.480
Think of AI as a new hire that doesn't have accountability.
00:13:40.799 --> 00:13:46.559
Use uh or or lean on the context of your team, right?
00:13:46.720 --> 00:13:53.039
If you have a team with long tenure, lean on that because they know the business, they understand how everything works.
00:13:53.279 --> 00:14:08.080
So what they they need to think is that the new responsibility of the humans is going to be to manage those agents and to guide them when those agents are making a mistake within the business process automation.
00:14:08.240 --> 00:14:10.159
It also applies for software, right?
00:14:10.399 --> 00:14:25.600
But uh, I think that's that's the core takeaway that I can give is lean on that on that team, lean on the humans and help them upskill to use AI as augmentation instead of replacement.
00:14:26.159 --> 00:14:26.799
Absolutely.
00:14:26.960 --> 00:14:30.480
Well, thank you so much for coming on the show and sharing your insights.
00:14:30.960 --> 00:14:31.759
Absolutely.
00:14:32.000 --> 00:14:33.840
A pleasure to be here, Amanda.
00:14:34.080 --> 00:14:35.440
And thank you to our audience.
00:14:35.600 --> 00:14:40.000
If you have any questions or comments, leave them below, and I'll make sure to respond as soon as possible.
00:14:40.080 --> 00:14:42.080
And have a wonderful day.
