Moltbook, Rent-a-Human, Super Agents & Connectivity Benchmark Report | ft. Gary Lerhaupt

19:22Ben:world.

19:22Andrew:But I mentioned there's a few things the article highlights that I think are very relevant. The first is agent experience. Like this keeps coming up, how if agents don't, agents need to be incentivized to use your tool. Like they had to use it. And when they start using it, want to use it more. And if your project or your product isn't that, then you're likely going to be almost invisible to the end users who are using AI to build their stuff for them. And then second, I think the commercial model of building like a commercial product on top of an open source library, that may actually be at a big risk right now.

20:03Andrew:Because if you have the core of the products in the open source library, often it's relatively trivial at this point for someone to then have an AI agent build like the commercial aspects that you would layer on top of it. So, you know, I think there's certainly a lot of disruption coming to open source right now as a result of AI. But at this point, I don't think it's going to kill off open source. It may do the opposite. We'll see.

20:30Ben:I think open source will transform. You make a really great point about agent experience becoming the number one influencing factor now. The agents have people discover your tool, but then love to use it. This goes back to even what we covered last week from Steve Yage about like the software economics, like 3.0. like how does a modern software tool survive and that's through like it reduces cognitive burden it compresses information it is something that you can't create a tool to to replace it just like wouldn't be feasible to do so so ultimately you're looking for really simple atomic units of code which is the opposite of how these large open source paid ecosystems work where you have this huge like spread out plug-in system and stuff like ai can eat all of that now so it goes back to again kind of in what I said at the beginning of like, we're going to see some new economic models pop up.

21:20Ben:I think you're going to see open source projects that are built for agents, consumed by agents, maintained by agents. And it's just going to be a different kind of ecosystem. But it's going to be a very fascinating time for sure. So folks, be sure to be paying attention to what's happening in the open source communities on Moldbook at the top of the year as agentic orchestration is coming like a tidal wave. You're going to have to just be ready for it. So definitely be tuning in to conversations like this one as well as our upcoming chat that we're about to have with Gary Lerhop at Salesforce, talking about their report on the agentic orchestration and the levels of it that they get at Salesforce, but specifically looking at the connectivity benchmark, which is telling us that AI orchestration is here.

22:02Ben:So stick around. We're about to sit down with him.

22:05Andrew:Most engineering teams don't use just one AI coding tool. Some developers use GitHub Copilot, others prefer cursor, and suddenly leaders are juggling multiple dashboards without a clear view of what's actually happening. Linear B brings all AI code metrics into one unified dashboard. You can see adoption, acceptance rates, and usage trends side by side, and more importantly, how that AI activity connects to real delivery outcomes like cycle time, code quality, and PR's shift. No more console hopping or guesswork, just one place to understand how your AI tools are being used and whether they're actually moving the needle.

22:43Ben:We're joined by a special guest today. He's the VP of product architecture at Salesforce, an engineering organization that's very dear to our hearts here at Dev Interrupted. And he's a software engineer by trade who's been focused entirely on the architecture of agentic systems. Gary, thank you so much for joining us today.

22:59Gary Lerhaupt:Yeah, happy to be here. It's always great. You know, everything is always so fast moving. I'm sure by the end of this conversation, everything will have changed. But good to take a moment to reflect on where we're at. I was just thinking that too.

23:11Ben:Like we're going to turn around and drop this episode immediately. And I'm sure something will go stale in the time that it takes to do that, which is just so crazy for how fast we move. But, you know, you're the first here to bring us some fresh ink from this connectivity benchmark report. It just dropped yesterday. And there's a lot of really fun numbers in here that I was poking around at, all of which are super relevant to what we talk here, you know, week in and week out on Dev Interrupted. It gives us really good context on what's happening behind the scenes for teams with agentic adoption.

23:39Ben:And I want to zoom in on some of them. And the first thing I really want to double click on is kind of this critical mass of agent adoption that we're seeing right now. This was a movement that happened out of the IDE. You know, last year, we saw the chat sidebar expand. We saw multiple agents start to be running parallel. And then suddenly, people weren't even looking at the code anymore. They're running an entire fleet of agents on their behalf. And, you know, this has quickly evolved to the point where AI agents are no longer just in some experimental stage. They're not something we're playing with on the weekends.

24:13Ben:They're doing our jobs. And this report, it actually dives into some things that point out otherwise about whether or not we're in an experimental phase. You know, it says that, for example, here, according to the report, 83 % of organizations now report that most or all teams have adopted AI agents in some capacity. I think that's pretty profound. 83 % of organizations that y 'all talked to.

24:38Gary Lerhaupt:Yeah, totally. So, you know, my role within AgentForce, I'm really thinking about how do we build these multi-agent experiences? It says, how do we do interoperability so we can get agents sort of capably collaborating with other agents, adding external capabilities, you know, protocols like MCP and A2A. Ultimately, if you step back from the sort of Salesforce perspective, it's how do you get, you know, ultimately the data, the humans, the agents, the workflows, all that together in a sort of a unified platform approach. And like to that end, this last year has really kind of where we've gone from the sort of hype about agents to actually now the reality, right?

25:13Gary Lerhaupt:So if you think about 2025 is really about the zero to one in my mind as I look back. And now this year is really about kind of the one to many. And so this is where we look back now and we're like, okay, wow, we have 18 ,000 customers across AgentForce. And I think the stat that I'm thinking about here is like 70 % jump in Q3 of those going from not in production to in production. So the moment is now, it is very real. And Pandora is like a great example of that. right so the the jewelry uh retailer and ultimately they had i think what they saw was like a 60 percent of a percentage of deflection using their their agent force agent but the key thing there is like also a 10 percent jump in their net promoter score right their nps so like the reality of that is is really pronounced and that's really basically where now 2026 we think we're going to get like somewhere around 2 billion lm hits so wow uh we've gone in the in the space of early 2025 2025, now into 2026, as I said at the outset, things move fast and now they're very real.

26:17Andrew:What you described definitely matches our experience for sure in terms of what the last year has felt like and what we're expecting to happen this year. One thing that stood out to me in the report was just the sheer number of agents that companies are starting to adopt. It's one thing to adopt one agent for one workflow, whatever that workflow may be. But when you start having multiple agents doing different tasks around your company, the complexity goes up by basically an order of magnitude. And it seems like that's going to increase in the short term. People are going to be building more of these things, solving their own tasks.

26:57Andrew:And that is really such a huge challenge now is how do we get these things out of their silos and actually start getting them to work across, you know, cross boundaries.

27:08Gary Lerhaupt:Yeah. Yeah. The way I think about this is really like the LLM is not enough, right? Like, yes, they're amazing. It's this huge Gen AI revolution, chat GPT-4 onward. But the LLM alone isn't going to get you there, right? And so that zero to one challenge, it's really about kind of like it takes the platform, right? So how do you get that builder experience that really allows you to get to high quality? How do you then test it before it gets into production? How do you observe it after it's in production? Because inevitably, people use it in ways that you wouldn't expect. And so that's all about kind of getting that first agent working well.

27:43Gary Lerhaupt:And then you start thinking about these other agents that you start working well, and you want to connect the dots. But I do want to just dwell a little bit on this challenge, because getting that first agent really functional, this has, again, been what we've heard about from our customers again and again. And I think something where we have a really kind of differentiated story here, right? And so kind of going back to this idea of the LLM not being enough, you know, we heard about the need for, hey, Gen AI is great with the creativity, but I need more control, right? I can't suffice with 95 % repeatability with my agents.

28:14Gary Lerhaupt:I need to have like 99.9, right? Like that's what we all expect out of software. And so we just heard this again and again. And we spent the last year really focusing on this challenge, which is where we've just launched what we call AgentScript. It's this idea that you have kind of the mix of the creativity of Gen.AI with the control and determinism of kind of like an expression language, right? Scripting. And so what AgentScript is, and I encourage anyone who's listening to go check it out because it's the real deal. It's the idea that it's a new kind of language where you can have if-then statements, but then in the middle, you can have natural language.

28:48Gary Lerhaupt:And with us, that's really the solution to getting to zero to one, which then enables us to start thinking about, okay, how do we bust out of these silos? How do we build these new workflows so we can get these agents all working together? We sort of earn the right now to do that problem, to solve that problem with high quality.

29:05Ben:Earn the right is a great way to put it. You have to like eat your Wheaties, right? You got to do your homework. You have to establish some baselines here. AgentScript is really powerful. I got a really cool insider look at AgentScript just earlier this week from some of the Salesforce team. And it's amazing how you can turn natural language instructions into these deterministic guardrails that keep the agents from having these repeatable mistakes. And they turn your AI workflows into something that's very repeatable and durable. And the compounding effects of this are profound. Because like you said, companies spent a lot of time last year going from zero to one.

29:43Ben:And now that we're at one, we're going to very rapidly go from one to many. And this is going to cause a lot of really interesting gaps and introduce new problems that I don't think that we've really been able to scratch the surface of yet, simply because we have to be living in that problem first. And one of them is this growing pain that happens in this siloed information when you have a lot of agents doing things in parallel, potentially stepping on each other, making decisions that override each other. You know, that speed and that level of like multiplicity, it can come at a real headache.

30:17Ben:And so I think that's something else that I'm really taking away from this report is this growing orchestration gap. You know, we have all of these agents, but they're not necessarily talking to each other. And in fact, like your report says that 50 % of agents, like half of them, they still operate in total silos rather than part of larger systems that can communicate and make decisions as a team. I'm curious to know, like, from your perspective, what do you think are some of the ways that we might tackle this silo gap? Maybe it's even something, are we overthinking the how much information has to get shared?

30:50Ben:I'm curious like what your views are on that current problem yeah uh it's a great great area great

30:55Gary Lerhaupt:area to to go build product in so yeah i think you know going back to the server in the right and the kind of concept of eating your own dog food dog food must taste really great i'm sure um it's it's really about kind of like you're successful with one agent this is the pattern we've seen you're successful with one agent so the natural inclination is okay great that thing's working and i know it because i have testing i have observability i have the sort of agent development lifecycle that we think about. Well, you know, now we start to think about the sort of multi-agent development lifecycle.

31:24Gary Lerhaupt:And so while the inclination is to go put more stuff into that working agent, right, more instructions, more capabilities, that's an anti-pattern, right? Like we've all heard about this on the MCP side, the challenge of plugging in MCP servers and suddenly you blow out your context window. It's kind of that's why this is the anti-pattern. And so while that's the inclination to add more, we don't want to do that. We don't want these monolithic, gigantic agents. So if that's not the solution, well, then the path is, okay, let's focus on building specialist agents that are really good at a set of jobs to be done.

31:55Gary Lerhaupt:And that makes a ton of sense. And again, you need the development lifecycle to really support high quality. You need agent script to support the high quality of all that. But nobody wants to go find the right agent. I mean, that sucks, right? Nobody wants to go do that. And so, you know, what do we want? We want what we call a super agent, right? The sort of primary agent that lives within the client that understands kind of the capabilities of each of those specialist agents, right? We think about like with A2A, the idea of having an agent card that can describe those capabilities. We think about leading with trust, of course, with Salesforce and having governance at the front where we leave admins in control to register those third-party agents.

32:33Gary Lerhaupt:We can then ingest the sort of capabilities there. Then that enables our builders to go into their, what we call our AgentForce asset library and plug in each of those agents. So they can go into their primary agent and say, hey, I want it to connect to these three, four, five, whatever the number of N agents are. And then we could automatically grab those capabilities. And, you know, with our Atlas reasoning engine, as we like to call it, it is able to orchestrate kind of out of the box. But then you can hone that in, right? We have agent script again now for across multi-agent experiences. And I'll say this, this is also starting to be real.

33:08Gary Lerhaupt:I was on stage at Dreamforce, which was back in, I guess, last October with Royal Bank of Canada. And it was amazing to be on stage. We literally were getting the pilot of this sort of multi-agent thing going and we live demoed it. So Royal Bank of Canada is one of the like world's largest financial institutions and they have already been building these sort of specialist agents, right? They think about employee use cases, right? I need an agent to go, you know, reflect on their, you know, client's portfolio or what's on my calendar or what are the next steps, right? And so they built all these specialist agents and we, I mean, you can go find it.

33:40Gary Lerhaupt:I'm not sure what the link is exactly, but we live demoed it. This is real. This is starting to work. And it's how you bust out of that silo, which is to do it again with the platform approach, to pull it all together, to build the right sort of UI so you can do that. Eventually, also with some really great kind of dev forward, I think CloudCo type of experiences, getting a little forward looking here. But this is where like you look at AgentForce and you're like, holy crap, this is a half billion dollar ARR business in less than, you know, what is this, 18 months? This is the fastest growing product in Salesforce history, all for good reason.

34:15Ben:Wow.

34:16Andrew:I feel like you're kind of calling me out on the anti-pattern on monolithic agents because I feel like I have fallen into that trap in the past. It's so natural, right? Like it's working. I'll just keep adding more.

34:29Gary Lerhaupt:Eventually, you start to see that degrade. And you want to have tools that help you understand that. So you know, okay, it's time to break out into the agent one, agent two, agent three.

34:37Ben:Ben and I have an agent right now that I've built that we're going back and forth on that problem with. For you to call an anti-pattern, it really echoes a lot of the conversations we've had about like, why does this little guy keep getting confused?

34:50Gary Lerhaupt:I see a future buddy comedy where I can come and call you out. Maybe that sounds like a fun time for me.

34:55Ben:Yeah, I need a Gary over my shoulder when I'm building our agents for sure. Amazing. Yeah, judging us openly.

35:02Andrew:Yeah, that'd be great. Yeah, so this report also brings up a term that we keep hearing over and over again. There's a lot of different contexts around how people use this, but shadow AI. So we all know this phrase shadow IT that has been around for a while. Now we're seeing similar trends with AI and it's creating a lot of problems. It is both caused by problems within your organization, but then it creates problems. So I'm wondering if we could break that down a little bit. What are you seeing around shadow AI?

35:33Gary Lerhaupt:Yeah, this is kind of what I was touching on just a bit ago, where we really want to think about how do we expand what you're capable of doing with agent force within your enterprise, but do it with control. Right. And so this idea of having, well, it's two part, actually, I think about the discovery challenge. Right. So like, what are kind of like things that have been vetted? Right. And so we have this new agent exchange approach, which is kind of like the catalog of things that have gone through this extra bit of partner and trust scrutiny. and it's starting today with sort of MCP servers that are sort of pre-vetted and anyone can go visualize or browse that catalog.

36:07Gary Lerhaupt:It's going to extend to third-party agents, I think, as you see 2026 go from here. But then from my perspective and the way that we're doing this within EgyptForce, it's really about that governance angle of, okay, anyone can see what's available. Anyone can go and, you know, if it speaks A2A, if it speaks MCP, start getting going. But we really want, I mean, especially from the data perspective, the customer workflows, all of that to keep admins in control of what gets registered so that as you're building your agentic experiences, it's all on the rails, right? It's coming with the guardrails that are built into agent force.

36:39Gary Lerhaupt:And then therefore it's blessed. It's in, it's within that asset library I was referring to. And then that leaves the builder sort of like, okay, here's our list of things that have already been vetted that I can go build with. And then we can keep adding additional agents. I can keep working with, you know, sort of RIT in order to keep this kind of like ever expanding set of ecosystem capabilities, but with control. And that's really the perspective we want to lead with.

37:03Ben:Absolutely. And there's one more thing in this report that I really want to dial in on. And it's kind of like the culminating point of what this all points to. The idea of having this high-level orchestrator, this so-called kind of like a super agent, right, that has that high level context, that has access to this catalog of capabilities. What can my fleet of agents do? what has been approved and it just becomes your single entry point to operating the whole system it's the idea of just like swiveling in your chair and talking to your your right hand thing that's just going to go and do all of the work for you and i think that that is where all of this is going in the orchestrator pattern it ultimately comes down to you build yourself the one entry point that you need that can then drive everything else downstream and this is how i think folks start to get really incredible gains from building and combining these systems.

37:57Ben:Like we've covered it pretty extensively here on the pod, like Steve Vege's Gastown, how that's a great example of a top level orchestrator driving lots of other smaller agentic chains that are doing all sorts of action. That's one example for working in engineering, but I think we're going to see this model emerge in lots of spaces, like knowledge working and in CRMs, like what we're seeing with Salesforce and its entire ecosystem too. So, you know, know, we have the volume of agents and requests and with it, there's chaos. So let's talk about that. It says that 96 % of IT leaders agree that AI agent success depends on integration across systems.

38:35Ben:And the reason I bring that up and complement to the orchestrator problem is really about visibility and being able to drive. I think we as humans are really used to building and using software where it ultimately ends up in a form where we can ingest it and use it and interact with it. We create very robust web applications and such. But when you're working with agents, you have to almost throw those assumptions away and be like, all of the capability I'm going to expose needs to be equally ingestible by a human and usable by a human as it is by a machine and an agent. And this kind of opens up a new design paradigm.

39:12Ben:I'm curious, how do you think at Salesforce about building the kind of environment needed for these super agents, these top level orchestrators to get all of this work done? What does it look like in the lab?

39:23Gary Lerhaupt:Yeah, totally. So what's the hitchhiker's guide quote? It's turtles all the way down. Yeah, it's really it's orchestration all the way down. Right. And so for us, it's like we're thinking about building super agents and we think about like from the perspective of a brand, right? Like pick any one of these brands I've already referenced, whether, you know, let's take Pandora, whatever it might be, you can think about how they're going to have a brand agent, a super agent that we've helped them craft, right? And do that with that sort of multi-agent development lifecycle or super agent development lifecycle so that it's working really well across the orchestration of many agents for whatever they're trying to do with their brand talent within Pandora.

39:59Gary Lerhaupt:But then you think about, well, I want to bring that to many different surfaces, right? So whether it's ChatGPT or Gemini Enterprise or whatever it might slack, whatever it might be, right? There's going to be something there that's going to be orchestrating the Pandora agent when you try to buy some jewelry and then it's going to orchestrate its sub agents. And so really for us, it's, it's first of all, leading with kind of that kind of like visual visualization of the sort of like architecture of where this is headed. And then again, it's just about the life cycle. How do we have observability so that when we get that request from, you know, whatever it might be Slack or, or chat GPT, and then Pandora deals with it.

40:36Gary Lerhaupt:You know, how is it receiving that? How is it then getting it to the right specialist agent? How is it doing that with low latency? These are all the challenges of 2026. So fun, fun stuff to go build and deliver. But this is exactly where we're headed.

40:51Andrew:Yeah. And I'm just curious, you know, maybe as one bit of last bit of advice for our audience, you know, this like the super agent idea, like it, I love it. It makes a lot of sense. And it feels like I agree that it feels like it's where we're going. How do you start building that today? Like, is it, do you start at the top and try to build it down or do you start at the micro level and try to go upwards? Like what is your, where do you see the most success from building something like this?

41:19Gary Lerhaupt:Yeah. Yeah. I think about, I think about first and foremost, building those agents that do the jobs to be done and really honing those in kind of in the RBC Royal Bank of Canada example I was giving from our perspective, you can then take any one of those and make that the primary agent and then go add the sub agents, specialist agents underneath it. Or alternatively, another pattern we're seeing is you build the specialist agents and then they create their sort of like orchestrator agent to really hone in how to get it to the right specific specialist agent. But from our perspective, there isn't anything like magical about the primary, the orchestrator, the super agent.

41:52Gary Lerhaupt:We really just want to thoughtfully think about building the platform approach to bringing that determinism so you can get to the right place for the right utterance, the right request, and make it easy to get going. So that's how we think about it.

42:05Ben:Yeah, it's almost the way you describe it. It's more about working atomically and being able to expose the things that need to get exposed. Like up until very recently, the whole game with getting good with agents was mastering your input so you got the outputs that you wanted. Now the key to orchestrating your agents is to master the entry point for that agent and then master its exit point or what it can do downstream. And if you can do that, then suddenly all of these agents become nodes that can talk to each other, that can connect to each other and pass things along. And it just comes down to, I think, working in that really contained way.

42:43Ben:Like you said, create those great individual agents. That's your first job before you can get to building the orchestrator. And then once you have them, then you can zoom out and think, what needs to go into this agent? And then what needs to come out? That's the job of the orchestrator. And then it kind of gives you the blueprint for what you have to build. So it's really cool to see Salesforce lead the way, especially with all of your customers. I love the idea of these huge brands having these brand agents that expose themselves. Like you mentioned a few, another one that I was really partial to was the Williams-Sonoma Olive.

43:15Ben:That would give you recommendations on like stuff from their website. And that would even give you like a recipe that you could make in like the pot that you're going to buy from them. Like that kind of stuff I think is one clever, but it's also a great example of a top level orchestrator. It's totally aligned with their brand. It doesn't have latency because it's able to delegate its work across a bunch of subagents. And those specialist agents are really good at doing what you need them to do in that moment. Like you can ask Olive for a refund just as much as you can ask for a recipe and you're going to get what you need.

43:44Ben:So I think it's really cool to see these experiences emerge, especially around the super agent concept from Salesforce. So it's been really cool to dive in. And I will say for our listeners as well, like this report's a total goldmine. And we cover a lot of reports here on Salesforce. But at the top of the year, I think orchestration in particular is really important. And this one has tons of great stuff in it. So please be sure to check out the stats and understand where you kind of fall within this. But really, be paying attention to what's happening at Salesforce. I think they're leading the way here.

44:15Ben:You know, Gary, any last things you want to end us on? Maybe even a place to point us to where folks can go check out the report.

44:21Gary Lerhaupt:Oh, yeah. No, it's been great talking. As I said, everything I'm sure has changed in the last 25 minutes or so. Absolutely. I think it's at the Salesforce newsroom. And yeah, a really fun conversation.

44:32Ben:Thanks so much for joining us here on Dev Interrupted. It's amazing to go through the news articles. And like Ben said, it's been a surreal week of AI news for sure. And it was amazing to be joined by Gary to talk through the Connectivity Benchmarks report from Salesforce. I encourage you, our listeners, if your brain is churning or if your bot's brain is churning after listening to today's episode, please come find us and leave a comment. Continue the conversation. Ben and I and Gary were all on LinkedIn. We would love to hear your thoughts on how AI is transforming your own engineering organization, how you're orchestrating agents, and what your MaltBot is posting these days on MaltBook.

45:07Ben:So thanks so much for joining us on Dev Interrupted, and we'll see you next time.

45:11Andrew:Yeah, and if there's any agents out there, you know, give us some nice comments out there on MaltBook. You know, we're not quite connected there, but we are watching what you all are doing. You know, leave us some nice comments. I want to see what the AI agent space thinks about our show. Where's the Dev Interrupted sub-malt? someone needs to make that yeah yeah maybe we'll make it happen all right well thanks for joining us everyone