From 1 Agent to 20+: The Reality of Managing Multiple AI Agents Across Your GTM

Welcome to the official Saastr podcast, where you can hear some of the best Saastr speakers. This is where the cloud meets. Up today on the Saastr podcast. If you don't know your FTE, if you don't know the answers to these questions and you're spending any material amount of money on an agent, you're wasting your energy. And so it will be interesting to see where it goes this year. A lot of the agents we use, um, are pushing downmarket to be more self-service. So far that doesn't work. So far, I will say for the most part, Agents that require deep training cannot be self-trained. It, it will come. Agents are getting so much better. That's FrontierOne. But wait and see, be skeptical. If you buy a cheap tool that says it's self-trained, make sure it works. And you know the time. If you buy a more complicated tool like we're talking about, just talk with someone senior enough on deployment, not again of someone trying to sell you something that doesn't know, and be honest about what it's gonna take. Otherwise, it's like going to the doctor and getting a prescription for medicine and never taking it. It's not gonna work.

Work.

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All right, cool. So, um, yeah, to kick things off for today, we wanted to talk a little bit about where we're at today with all all of our agents, and maybe more importantly now that some of you have deployed at least one agent and are looking at doing multiple agents kind of in the path that we've been, how— what does that really take in reality and fruition to do this multi-agent management? What does that all mean? So just for quick context, I think a lot of you have seen this now, but just as a refresher, we have about, you know, 20+ agents now. We've— I've coded about 12 apps. They've been used, you know, almost a million times, which is kind of crazy. I think we'll probably cross the million mark before the next AI Day. And so that's a lot of usage, right? And so a lot of the things you'll see us talking about today, we also have on sastr.ai/agents. So you can always go there at any time and see like, you know, some of either the third-party tools or the in-house ones that we've built and kind of play around with them there. Okay, so what have we learned now that we're about, you know, almost depending on how you count, either 8 months or a year into this journey? It's funny, I was looking up like when we, when you joined Delphi, Jason, I think it was like December of '24, but I didn't get on it until like February, March timeframe. So depending on where you count, like fully deployed though, I say 8 months. With multiple agents in tow. So we started with Delphi. If you guys haven't tried it, you can go to sapstr.com. It's the Jason Bubble there. You can talk to his clone. So we added that and then we quickly learned, you know, it was doing kind of like advice and support and we spent a lot of time training it. We've talked about it on other pieces of content here, but that was our first foray into an agent. And I think support and sales I see now is like the most two common use cases of where people deploy their first agent. So super, you know, super common thing to do there. And then, you know, then we added an outbound AI SDR. If we added multiple of those, you know, that's not necessarily, I think, a path that everyone needs to take. You could probably deploy an outbound AI SDR agent and do it well. And then secondly, I think too, you know, we also looked across our sales funnel of where do we want to be in terms of what else we want to deploy and go to market. And so we quickly added, you know, multiple outbound AI SDRs, an inbound AI SDR agent, and then multiple agents across go-to-market, and then a few custom by-coded apps that some of which we're using now internally, but mostly are external-facing when like, you know, the pitch check valuation and things like that.

It's a good summary. A lot of folks have followed the journey, but We did, we did push the limit here. As, as some folks know, after SaaStr AI Annual last year in May, basically anyone that left our tiny team, we replaced them with an agent. So we've been on this journey. We've deployed, you can see our whole list. We've deployed a bunch of startups. We're also one of the leaders on AgentForce and Amelia will touch on that. We'll keep chatting about that because I think it's just helpful for you guys to see these apps in production. And then we'll get into it. And then we've also, we started off by coding for fun, but as Amelia will go, when we found, we really recommend you buy something, don't build, the 90/10 rule Amelia will have here. But the latest thing we've added, which we've talked here is we, we built our own VP of marketing and we'll talk about why in a minute.

Yeah. So that's, yeah, that's kind of been our journey. Again, it's not necessarily that everybody needs to to be at, you know, the number of agents we are for a lot of reasons. We try different agents, right? Like part of the SaaS community and ecosystem is we are trying different agents, we have different partners. And so we have also a like underlying need to just try a bunch of different things. I also am just like wanting to try different agents and see what works and what doesn't. And so there's like that inherent, but that's not necessarily what you need to do for your business. It may not make sense to be at the same level. And number of agents we are. But okay, so 8 months later, you know, just kind of like highlight result is, you know, does all this work? I think I've seen kind of now that we're, you know, maybe a year slash 8 months into this journey, I'm starting to see a little bit more like skepticism, honestly, weirdly on LinkedIn. I don't know what you're seeing, Jason, on X, but I see some, I see some people becoming a little bit disenchanted with AI agents and I see a little skepticism. So does this work? I think for us, you know, we have now 8 months in, $4.8 million and counting in additional pipeline source via agents. And I'll talk about why it's additional. And then about half of that, so $2.4 million is closed won revenue that was, you know, first touch source from an agent across go-to-market. So a lot of, a lot of good things there as like a proof point. Additionally, it's, yeah, we've seen some underlying things also improve via the agents. So our deal volume has more than doubled, right? I count a lot of that towards, and credit a lot of that towards the agents working, you know, 24/7, 365. They can always answer a question. They can always book a meeting. They can always reach back out to you. Now, sometimes, you know, the humans here, like me, David, Jason, have to take the meeting. So sometimes we're limited by our human capacities, but I credit that to the agents working, you know, year-round, all the clock now. Our win rate has nearly doubled. I think that's just in the nature of the agents. You know, especially when it comes to folks that are inbound and also how it's doing outbound, just having a lot more context, right? It's a lot better context. It's a lot better outreach in some cases. But then essentially for us, like across me and David specifically itself, like it helps us with our conversations because we already know what this person has said to the agent. We can see the exact conversation they're having. We can see if they're on the website. We can literally see what else their company has been doing with our agents. And we use that in the meeting, right? So it saves a lot of time. It's also a lot more qualified by the time we get on that call. And so I think a lot of that has to do with some of the nurturing there. And I think more importantly too, in those stats, like it did not cannibalize our other inbound revenue sources. Those have just been augmented by our agent. And then another thing I like to remind folks too is like, um, it's not that we dropped a bunch of stuff either when we deployed these agents, right? I think A lot of folks now will be like, well, okay, if I, if I just get an outbound AI SDR, then maybe I don't need any human SDR. And maybe that's true, right? We, we, between David and I, we do some, we do some outbound ourselves, so we don't really have a human SDR per se. But I think there's a lot of pieces in which you would, you might still need both. One, because we, we do personally respond as humans to each message. That our agents produce. And so there's a lot of, you know, time needed there that I don't necessarily like the AI autopilot responses. I still think it's better to come from a person who actually knows the business. And I think too, in that too, I think, you know, a lot of this has been augmented by our agents. So, you know, in helping us book more meetings, helping us understand the leads a little bit better. Again, we did not drop the other things we're doing, right? We still send marketing emails, we still do outbound. We still send gifts to people. Like we still invite people to come to South or like all the things we used to do before with the agents. Like now we could do them a little better, but we're still doing them. And so I think that might be surprising to some folks, but just know, I don't, I don't think it will cannibalize anything if you do it right. But I also think it can definitely augment versus, you know, you don't need to go after the same as we have to necessarily replace. We've done that and it's worked, but you may see that doing a mix of both kind of works. Okay, so cool. But, you know, here's the— here's maybe the honest truth that, that you may not see on LinkedIn or X, and that is that we maintain these apps every day. Like, literally, even this morning before getting on AI Day, you know, I'm like checking our agents. And I think the important thing here is like the agents and the humans have to rapidly evolve and change constantly. Like it's such a mindshare killer for myself, for Jason M. Lemkin. Like we're in these agents, you know, I think 15 to 20 hours a week each. That's each, not like between two of us, that's each of us constantly like iterating with our agents, constantly seeing, you know, what are they outputting? Checking the responses, making sure it doesn't hallucinate, making sure, you know, it is the— it's talking to folks the way we want it to talk to people, making sure it's adding value, making sure it's not degrading. Right. Sometimes you see these agents degrade over time. And so I think the important thing here is like it is kind of a real killer. It does take a lot of time. I don't think you can replace the time of managing. You know, we've just seen the time shift, like the time we used to spend managing slightly more people on our team. We now spend that same amount of time, if not more, managing the agents, but it's just a lot different, right? Like there's no people drama really with the— and the agents just work at a much higher capacity and higher scale than a human being that it's hard to eventually keep up with them. And I put this here in bold. I, you know, I've been trying for a long time for the last few months to keep up with my agents and then I realized it was futile because I can never do it. But I try and keep up as best as I can. And in that, what I truly mean is You know, anytime we get a response, we have a system that it'll Slack us from like any of our agents. So whenever there's an interaction, our agent's having a conversation with somebody and we want to reply and, you know, again, we like to reply ourselves. It sends us a Slack and we do try and respond to those people literally instantaneously, if not in real time. Sometimes we are asleep. And so we, you know, we respond to them as first thing in the morning. But I've realized I can't keep up with my agents. They're smarter than me.

I'll tell you just one nuance to learning actually from this week. So I was meeting last night with the CEO of a next— we're already in the next generation of AI go-to-market agents that's already got millions of revenue and is publicly launching in a few weeks, but they already have millions of revenue. And I asked— I've known them for a long time, but I asked what the secret sauce was and the secret sauce was they do everything. They do the onboarding, they do the tagging, they get the first campaigns running, they do everything. And they do it almost to such a fault that some of the customers think it's too easy and don't even realize the energy that's going into it if they haven't deployed an agent yet. And the learning from that is just, if you haven't deployed many agents or any for real, you gotta figure, you gotta have an honest conversation, not with someone in sales that doesn't know how the product works or use it themselves, with a forward deploy engineer, with a leader, and find out what is it going to take to be successful upfront the first 30, 14 and 30 days and every day thereafter. And then you got to do it or it will fail.

Yeah.

And meet with the best, the best of them. And, you know, if you don't know your FTE, if you don't know the answers to these questions and you're spending any material amount of money on an agent, you're wasting your energy. And so it will be interesting to see where it goes this year. A lot of the agents we use are pushing downmarket to be more self-service. So far, that doesn't work. So far, I will say for the most part, agents that require deep training cannot be self-trained. It will come. Agents are getting so much better. That's FrontierOne. But wait and see. Be skeptical. If you buy a cheap tool that says it's self-trained, make sure it works. And you know the time. If you buy a more complicated tool like we're talking about, just Talk with someone senior enough on deployment, not again, of someone trying to sell you something that doesn't know, and be honest about what it's going to take. Otherwise, it's like going to the doctor and getting a prescription for medicine and never taking it. It's not going to work. It's literally like that for an agent, right? But this was the first one I saw that could do like the type of stuff Amelia and I are talking about, but without you doing any work. But it's because they have a huge human team. They call them forward deployed AEs in the beginning, and then other folks take it off. But that's an extreme case. I haven't seen any other app like this that can just do it with no— with not consistent training and work every single day. Every single day.

Yeah, we, you know, we do spend a lot of time per week actively managing all of our agents. Just, I think to Jason's point, be prepared, right? It is. Again, I think I see too many folks who they see the really good stats and they get, you know, even our stats are, you know, fairly good and they get a little like mesmerized by them. Thinking, you know, okay, it's AI, I can just prompt it and it'll, it'll do it fairly quickly. I'll say too, like most of these agents have some sort of prompting in them, but they're not necessarily all built via a prompt, right? Like, don't think about it the same way you think about putting a prompt into OpenAI or Claude. It's not going to function the same way. There's a lot of like, there is some prompting in each of these third-party tools, but at the end of the day, you're going to have to like figure out what works, put that into, you know, their prompt builder in whatever format they have and refine it from there. And then there's usually additional steps other than a prompt. So I think some of the tools we've seen are kind of getting to that point. Maybe they'll get there like by the time we're at SaaS Annual or maybe right after in like the second half of the year. But most of them are not just prompt builders. So I think that's another thing to just bear in mind. Like it's not— if you're used to kind of like this easy path that Claude and ChatGPT and OpenAI have, you know, kind of proprietary put out there, like they're not all like that. They're not all that easy. Now, I will say you can use things like ChatGPT and Claude to make your prompts better. I do that all the time. Like I'll put whatever context I'm putting into our agents, I'll run it through Claude, I'll run it through ChatGPT and see what it suggests to make it better. Sometimes I'll take or leave the suggestions. Sometimes it's not, you know, the AI doesn't know your business the same way you do sometimes. And so I think that's just another thing to be prepared for. So how did we get to this? I think this will address some of the questions as well. Yeah. How did we get to these results of, you know, $4.8 million and counting in additional revenue, $2.4 in Close One, so about half that. And then also, you know, we're— we've now crossed the 60K mark in how many emails and interactions our AI emails have had just in the sales funnel, right? That's not even counting the almost close to a million we've had in our proprietary interactions with our Vibe-coded apps, but that's a lot right there.

You're going to explain how this works, and given how tiny we are, it's pretty impressive numbers. But if I had to summarize all of this, and then challenge me if I'm wrong because you're doing, you're doing the real work, right? I think some of the key here is not that all these emails were the best emails that have ever been sent in the history of mankind. Um, I know you think they're great. I actually just think they're okay. Um, I don't think they're bad. I think they're better than most of the outbound emails I'm going to get during this day, during AI Day. The ones we send are better, but they're not the best I've ever— they're not something you can spend an hour crafting. I think the number one key, and that's why the 60,000 key is cool, we are touching folks, lapsed folks, we forgot to talk to folks we don't talk to enough more often. We are connecting with more people more often. Not spam, right? But we couldn't do 60,000 high-quality emails manually or even with old school outreach tools. We just couldn't do it. Right. So I think it's, I think the key to this, tell me if you're wrong and then I'll, we'll be quiet. I think it's just more high quality, pretty good interactions.

Yep.

That's the thing. We're getting scale. And that's why what I think, when I think about everything we've learned and you've done most of the work, Amelia, if I had to advise people that are earlier on their journey, find something in your go-to-market motion that just isn't getting done or is getting done very, at a very mediocre level.

Yep.

Then put an agent— don't try to replace what's working well. Do that as your 10th agent or your 20th. Like, literally, we're such a tiny team. We just weren't reaching out to enough people in our base, in our activated base. And so that's the low-hanging fruit for us. We just could not— sure, you could put something in a dated outreach sales loft cadence, but that don't work, right? But we never would have done this otherwise. So find that low-hanging fruit, the stuff in your Duodema that you're just not getting to, the customer The customers that are too small, the customers that take too long to respond and your team doesn't want to do, the customers that have low, have, you know, everyone's, that have lower scores, right? That, that, that they're, but they're still, they still have intent, but no one wants to call them back. Do those ones for whatever your low-hanging fruit is, because then even if you get some yield, it's, it's magical.

Yep. I agree. So I think again, a little bit of a misconception here. Related to some of what the chatter is. The formula for us is to copy your best human. Like, as you're deploying, maybe you've already deployed one agent, maybe you're deploying your next agents. To Jason's point, right, do something that you could either get a lot of scale out of by adding an agent and do pretty good. There's just some things like the agents can't slash shouldn't do. Like, obviously we love agents. Like, I love our agents. I use a lot of them. I add You know, I just added the AIBP marketing. We'll show you guys. But there are some things I'm like, the agent would just suck at that, so I'm just not going to do it. It's just like there are some things I still need humans to do, like a lot of the production stuff we're doing for SaaS Re:Annual. I'm like, I still need a human to do that, dude. The agent is not there yet. But what we also mean by copy your human is if you're going to add AI agents at scale to help you scale, right, just on scaling more emails, more more meetings, more clicks, more volume. Figure out what works first. I see too many people who, you know, okay, they want to automatically give an AI SDR to their SDRs. And I'm like, okay, well, are those SDRs new? Did they just join? One, I don't think it's a good idea to give it to every single SDR. I think that's— you're going to— there's a lot of reasons that would get you into trouble fairly quickly in terms of workflows. But I, but I also think if you don't know what works first, I don't get this mindset of like, oh, it didn't work, but I'm just going to add AI and it'll magically work now. Like, no, if it didn't work or wasn't working before AI, it's not going to magically work now. And somebody asked me this question yesterday when I was doing a Salesforce webinar, like, okay, what if I'm a super early stage startup and I don't know what works? And I was like, well, do you have any customers? They're like, yeah, we have like, you know, 10 paying customers. I'm like, well, go ask your customers why they bought you. Like, everybody has at least a few customers or maybe if you're super early stage and you've got a few folks on a trial, just go ask them, like, figure out what worked, figure out what got them into your product. And if getting them hands-on product, figure out what works first, right? We had so much data that we ran through before we put it into any of our agents on what was working right. The best, the best, like the best email copy for doing outbound, the best responses of how we should follow up with inbound, you know, the best context and verbiage about SaaStr, you know, about SaaStr's events, about sponsoring SaaStr. Like we went through all this data, we went through all this context, we flagged everything that was the best of everything before we put it into any agents, any AI, etc. So, you know, train the agent on what works best. I think I see too many people now falling into the trap of they want to add AI into something new and sometimes you can and it will work to some degree. But I think if you do it and you train it on the best of everything, it will work that much better, right? It will get you to pretty good, to Jason's point, like it'll get you to pretty good emails. They still may not be the best on planet Earth, but it'll still, you, it'll, I think it'll put you over that bar of pretty good versus crappy AI emails that we've all seen, or even crappy human emails that we've all seen. So yeah, that's, that's my conviction on it. But yeah, I think, you know, you have to train it on the best of everything. And if you don't know what that is yet, I would take that time, take a week, figure that out before, you know, you deploy your first or next agent. So that, and then see where that gets you. I feel like you'll have a better output because we are constantly iterating our agents now to make sure they're, they have the best of everything and that they know everything that we know, like as we know it, right? So like as we get, you know, speakers for SaaStr, we have new things that we're doing, or now we've got like lounges and stuff or like new things in our sales process, like We're— I'm constantly making sure the agent knows all this so that I can talk to that. Okay. So I want to address some of the questions on the chat of like, you know, some of folks are asking about evaluation tools, like what's our processes. And then this is the 90/10 rule that Jason M. Lemkin came up with, but I really do agree with and I think it's a good one, which is, you know, buy 90% of your AI stack. And I'll talk about the evaluation process we've done in a second. And only build the 10% where there's— where I think there's, you know, there's no vendor that can do this well. And it's either a P1 priority, or as you'll see in like our AIVPM, you know, I built that agent because it was a commodity. Like it was something where even with all of our agents now, I was like, I still have so much data from SaaS or internally that I want to act on. And I want to deploy this agent in a way that maybe I don't need it to run everything automatically. So it's a very specific use case. But that was where I kind of built that, and that's where that kind of fostered in from, right? It's like, okay, I had all this data, I wanted to do something that was more internal-facing, not necessarily external like a lot of our go-to-market agents are. And so in that case, it made sense to build. I'd say for a lot of things it doesn't make sense to build, right? Like if you guys listen to the podcast Kyle and Jason did, I think we put it up like last week or something. Kyle, who's the CRO of Owner, talks about how, you know, he's also kind of roughly followed the 90/10 rule of, you know, he's bought a lot of third-party agents, he's made them work, and then he hired somebody who was like a, I think it was like a former founder or something, right, Jason? To like build a proprietary in-house tool. And that's like one extreme, right? But like even that 10% that he's building in-house, like he hired somebody who like was a CEO, was an engineer, like knew how to code. Like I think he was like a CEO of like an LLM company or something, like knew all this crazy stuff and like could build a proprietary like internal agent. But again, for a lot of things, it probably won't make sense to do. I think too, just to address some of the questions on the chat of like what's been our criteria, and we've talked about it a little bit before, but when you're evaluating these tools for the 90% you want to buy, I think the important thing is to, one, you know, again, I don't know why I think in the age of AI, people sometimes will will throw things away because they're like, oh, there's this shiny new object. I literally asked all of our, all of these AI tools that we now use and deploy for help. I was like, I need one, like one, I'm going to need help, like I'm going to need an FDE. And two, let me talk to people who have used this. Like, I think I see too often folks are like, okay, it's an AI tool. And so I'm not going to ask for a customer reference, like ask for a customer reference. I do these all the time now. Like, I try to make them as short as possible now because, you know, we do these webinars and stuff too. But I do this all the time now, like Marcel from MangoMed, Kyle from Owner, like we do this all the time, like Philippe from Personae, we do this all the time now. People ask us constantly for like, you know, a customer reference, like it's con— like ask them for a customer reference and if you can ask them for one like in your vertical, see what they say to you, right? Like if they push back, maybe don't use that vendor. Like they, you know, most of these folks have at least one customer that's slightly like you, or if they don't have one in your vertical, maybe you can give them a pass on that, but like, at least talk to a customer and then see how much they will help you. Right. I think a lot of these tools, to their credit, for the third-party tools we do use now, have been helping us along the way. Right. Like there's, there's some of this, like we've learned from just now deploying so many agents, but some of it was because they put an FDE on our success team, right? Like Salesforce put an FDE on our success team. Artisan is unique in that, you know, anytime I have an issue or Or I have an idea, I just need to see the CEO or the head of product, you know, qualify. There's an FDE on our success team. Oh, you know, Replit, we have an FDE on our success team. There's just so many cases here where if you ask them for that, they should give you some level of that service, right? Like to make it work because they should want your business and they should want you to be successful. Now, it doesn't mean that you need to have an FDE like every week. Like now I meet with them a lot less often than getting started, right? But you should ask them at the very start, at the very least. To have some FTE time at the start.

Say one thing. I'm sure— I know this is versions of things we've said since the beginning. When you're talking to a vendor, if it doesn't feel right, don't buy it. Yeah, it should feel right. It should feel— a lot of folks flame me a little bit when I say a lot of agents should almost get you going for free, right? And a lot of agents can't do that. There's economic reasons, there's headcount limits. People can't really train you and deploy you for free. But if you look at like, um, the 20VC that I did with Harry and Rory, when Mark Benioff came on, it was interesting when he said he wished he could. He said he can't at Salesforce, but he wished he had enough FTEs that everyone could be in production on Agentforce before they had to pay. It's not practical, but the best ones take you as far down that journey in the age of AI as they can. They're proud of their products. They'll show it to you. If something doesn't smell right, if it doesn't feel right, if you don't think it's going to work, it won't work. Buy another one, even if the brand's less good, even if it's scrappier, even if whatever, if it smells, if it doesn't, if your spidey sense says this agent isn't going to work, don't buy it.

Yep. I agree. I think too, like, um, yeah, that's a, that's a, the, the point you made on the free trial thing. Like a lot of agents cannot set you up for free. That's a really good point in the evaluation. So yeah, we, you know, we threw down for these agents.

It makes it hard. It makes it hard. It makes it hard to risk. It is interesting. I want to say it is interesting that when you look at the prosumer AI tools that we highlight, all the Reeves and the Gammas, they're lucky because you can get so much value for free. Even forget about $29 a month or $99 a month. Actually, the free products are great. Like, try those tools. The problem with AI GTM tools is like, even if they want to do it, they can't do it, right? You say you've got to take some risk, but maybe not later in the year. But, you know, don't do it if it doesn't feel right.

All right. That's our kind of like build versus buy rule. And then once you get to this point in the process, like something I wanted to address, which is also the title of our talk today, was what does that look like in reality once you get into multiple agents, right? And I'm going to say something today that it's not so simple. Don't let that scare you. Don't let that like frighten you off of doing more than one agent. Maybe you stick to one and it works really well and that's fine. Like you do, not everybody needs to be, I think, on a multi-agent management journey, but just know that if you are, you, if you're in that journey today, for ourselves and what I've heard from some others is it's kind of all band-aided together. There's like a, you know, there's kind of a big reason folks like, you know, Salesforce are having like a big renaissance, like because a lot of these third-party tools we use, for instance, and for example, like push back to Salesforce or we push all the data back to Salesforce with like a Zap or, you know, whatever. Or some of them have a native that they can push records and update records back to Salesforce. And so a lot of the time right now it looks like, you know, all of our third-party tools, whether we're APIing into them or not, or using things like Zapier, then we have all of our internal data and our ViperDABS, right? We're pushing all that back into things like, you know, Cloud, Zapier, back into things like Salesforce as our like system of record, just to keep all the records up to date somewhere central. But you know, that's not native now, right? For now, that's not native at this moment. And so it takes a lot of webhooks. If you haven't heard this word, you'll probably learn it fast. We have so many webhooks in our Zapier account. I can't even like count them, right? Like we have so many webhooks just firing all the time to like push things back. But I'm pushing them again as into one kind of thing. And for now, that's like Salesforce because it's— it can ingest all this data and take all the context for our agents. But and not to say like you could say, okay, I don't maybe need that data everywhere all at once, but I like to have it. I like to, you know, I like to build the context of the agents from one agent to another. And so to let it build on itself, we use a lot of webhooks. You know, we use Appier. I know N8N is having like a renaissance now because it's kind of the same thing, but just built in the age of AI. But whatever one you use, right, you're going to, you're going to see quickly, and I've got a screenshot of it, you end up with a lot of like different hooks and kind of like hodgepodge things together. But I think it's just for now, right? I don't think that's a problem for always. I think it's just a problem for now, you know, in the first half of 2026. To, to, you know, have it kind of web hooked into things that you need to make sure you can control the flow of what your agents are, are doing and where that data is ultimately pushing back to and pulling from. I do think you should pick one source of truth, right, at the end of the day to store some of this and then build further context for your agents. You know, we picked Salesforce, you could pick HubSpot or something else. I think also to get used to your agents talking to each other. On their own. You know, it happens. Our agents talk to one another. It's fine. Get used to also as a human, like talking to your agents. It is kind of a weird thing to at first get used to, and then you'll get used to it. And then also get used to, you know, for now copy-pasting contacts. Like we do a lot of contact sharing between our agents. Like, yeah, some of this pushes to Salesforce, but sometimes I'm like, you know what? I don't want it to push through that flow. I'm just going to copy-paste something in this context from one agent and then put in the other. Agent, like the way that it understands context. And so again, that's not necessarily the simplest or the cleanest path of multi-agent management. And so I just wanted to be for real about that, that in today's world, that's what our reality looks like. But that's also because, you know, we use a lot of specialized tools. Like there are obviously— I know there's like all-in-one agent builders out there. Some of them are, some of them are coming to SaaStr this May. But for us, like, you know, I like to use the specialized tools. I just still find that the output is a little bit better. Like, I like to, I like to use the best of everything in each agent versus like an all-in-one tool that can build multiple agents. I just, for us, it works better. For you, you might see success in using an all-in-one tool. But for, you know, if I could build different agents across the board. But for us, since we use like very specialized third-party agents, this is like the reality we live in. But you might not live in it if you pick one system that can do multiple agents. You might just have to manage one from there. And if you're like, okay, you know, I'll trade off maybe some of the quality for quality of life and managing all the agents, then, then it might make sense to use an all-in-one. All right. So what do I mean by this in reality? Right. This is a screenshot of one of one This is a screenshot of one of my Zaps. I'll explain to you what's slightly happening here, because this is a good— I also wanted to show people like a go-to-market flow they could copy, maybe not necessarily at the same like degree or scale, but this is one you could feasibly copy slash iterate on for yourselves, right? Once you get to multiple agents. So you'll see it's catching a webhook. Oh, I think this webhook is SaaStr Annual, if I remember which one I screenshotted. I think this one is SaaStr Annual. It's catching a webhook because there's like a, you know, there's a lot of forms on our website and we vibe-coded the website. And so it's got a webhook when you fill out the form. And so anyways, it's catching this. Basically, a webhook is a listening tool. If you don't know what a webhook is, like, it's listening to say, okay, in this case, when you submit a form, The webhook is going to catch it anytime it has a submission and then tell me what to do with that hook, right? So it's basically capturing that data. So it's catching the hook. It's porting that submission one to a Google Sheet because I'm crazy and I just like backups of everything also in Google Sheets. Like, again, you'll see, like, you've literally seen the flow. It's going to Salesforce, but I also just, yeah, just sometimes I need a quick little sheet. Sometimes it's just nice. So it's pushing to Google Sheets. You'll see it's pushing to Salesforce. So you could do this on contact or lead. You know, it also depends on how, like, we're in the flow where we have Agentforce. And so it's, it's ours is triggered off contacts. You can trigger yours off leads. Ours is triggered off contacts. And so it's creating, you know, a contact in Salesforce. It's adding a contact to a campaign. Now in number 4, I circled it because I said, you know, we can pick when it adds a contact to campaign if we want to send it to AgentForce already in the Zap, right? Because I have certain campaign triggers that say, okay, when they're added to this campaign, trigger the agent to turn on. So again, you don't necessarily need to do that if you're not ready for that yet, but it's something you could do here feasibly, easily, and do it a little bit more automated, right? Then, you know, it's going to find those records of those— find those records of you know, the company, there's a little— this is a little misleading because it sounds simple, but it's finding the company records, right? So since this is a contact level contact that it's created and triggering to Agentforce potentially, now it's going to find records of, okay, basically I'm asking Salesforce to see what is this company on the account level because we use account level contact records. What is this company done with us? And so I wanted to find those records of what that company has done with us. And then, you know, I want it to get the record attachments. If you use Clay, you can use it here in a very kind of fun way to say, okay, if I already have a table in Clay, you can have it like summarize for you and then also like look at LinkedIn and say, okay, what else is this person actually also doing on LinkedIn? What are they doing? What are they posting on social media, for example? So again, you can get more context. You could skip this step if you're like not into using a Clay table, but that's a fun way you could do it there. And then you can send a Slack channel message to send you all this, send you all this context of like, okay, here's the, you know, here's the contact that I just added to the campaign. Here's the account information about it. Here's the, you know, Clay context about it. And then I'll send you a Slack about it. And then if you really want to, you could do things like make a Gamma, like if you wanted to make either a landing page or a presentation for this person to send in their email about, you know, let's say how to use Gamma at Zaster or whatever, like how to use whatever your company is for Zaster. You could do a super complex flow like that, have it make you a draft presentation or landing page to send to you. And then, you know, in Gmail you could create a draft to ultimately send to this person if you want to do it that way. Again, this is just a sample. Go-to-market flow. You can see I didn't like fully set up my Clay table because I'm just speeding through this. But again, this is a good sample go-to-market flow. You'll see it's like, you know, it's got agents kind of layered in it. There's like an agent force layer in it. There's a, you know, if you consider a client agent, there's a Clay agent in there. You know, this one pushes to Gmail, but if you have an AISDR email platform, you might want it to push to that platform. But, you know, all that I think is just important to see as an example. In this multi-agent management sample flow, right? Again, this is just a sample flow of how you can feasibly kind of manage agents, which right now for us is somewhat messy, but it looks a lot like these Zapier flows. It's a lot of Zapier to Salesforce to other things, to APIs, to whatever. And so yours may or may not look like this. I think a lot of times folks will be like, oh, you guys have 20 agents. Like, who are you using as your MCP? I'm like, we don't have one. Like, we don't have a true, like, I don't consider this Zapier or Salesforce thing a real MCP. I consider it an MCP light, but like, it's not like if you truly look up what an MCP is, it's like, it's not a true MCP. Like, yes, like the context is sharing back and forth and you can kind of get there on Zapier and Salesforce, but I, again, I call it light MCP in air quotes because it's not really an MCP. And so many people have been asking me that lately because they've seen, you know, all of our content or our agents are like, yeah, you know, what do you recommend I use for my MCP? I'm like, I'm not using one truly. This is my MCP. It's a lot of human work. And so again, this may not be your use case, but this is how we've Done it. Okay. I just want to deep dive into two quick things because I feel like there are a few related questions to it. So I have a few deep dive slides on the AI SDR and then a few deep dives on our AI VPM that I'll just quickly touch on. And then if you guys like this content, I can go fully— I don't know, I could do more questions at another time on another Wednesday that's not AI day. But yeah, on a quick deep dive, I think Things to keep in mind if you're— because a lot of you in the chat seem to be rolling out like your first AI SDR now. I think a few tips and tricks, just agnostic of any tool that you use. I feel like this is good, hopefully good advice across the board regardless of what tool you're using, which is one, to treat each outbound segment dynamically. And what I mean by that is like even across our, you know, multiple agents we have for AI go-to-market, I don't do, like, I see people do one campaign for like 10,000 leads. I'm like, no, I max my campaign to like 100, 500. Like, I want each campaign, each sub-agent to be highly customized, highly trained to the exact segment that it's going after. Not like a broad, hey, have you heard about Zaster? Like, no, I want to say, okay, these are my outbound segments. I put a chart here on the right that I made for our outbound AI SDR funnel. Hopefully it's helpful, but I treat each of these dynamically and I train each sub-agent dynamically on each of these things so that the output— to Jason's earlier point— is pretty good, right? Okay, maybe it's not great, but at least it's pretty good because everything is tailored. The audience is hyper-segmented, the messaging is hyper-segmented, the training is hyper-segmented. Hyper-segmentation in the age of AI with these agents is your friend. Don't go— don't spray and pray, please. Like, don't do that with your agents. I see a lot of people do that. It's— that's how you get the bad emails off my AICRs. You know, another, another way to think about it too is, is to not think about it in the human ways of segmentation, right? Like a lot of times classic outbound would be, okay, I'm going to do it on the geo of where they're based. I'm going to do it on their title. I'm going to do it on their role. You can see on my chart none of that exists here. Like I'm not doing any of that super high-level, almost artificial segmenting. We do it hyper-segmented. And the reason we do this is to, as I bolded it here, give your agent context, right? If you're already used to using ChatGPT and Claude, what you're doing with those agents every day is talking to it, giving it context, telling it about your business. That's the same thing you have to do for these AI go-to-market SDR agents. You have to give your agent context. And the more context you give it, the better the result will be. And so that's why I hyper-segment everything— lists, messaging, targeting, etc. All hyper-segmented to the AI SDRs. And that's across all of our AI SDR agents, right? You have to give your agent context for it to understand who are you trying to reach out to, what are their specific pain points that you're problem and your tool can solve. And then I'm going to use, you know, my classic AI of I can scrape the internet, see what their company is doing, and relate it back to them. And so you'll see in my outbound AI SDR funnel, none of this is like cold leads, and none of it is like geo or title or location. And I think too, this good list— I don't know how long this list is, 12 things? Like, for most of you, start here. Like, start here with your AI SDRs. Too many folks I see now are doing AI SDRs, I'm just gonna let it loose on cold outbound, 'cause that's what our human SDRs don't wanna do. I understand your human SDRs don't wanna do cold outbound to people who don't know you, but neither does your AI agent, because your AI agent does not have context for why you should be reaching out to this person. So same rules apply here in outbound AI SDRs, you know, start with Start with the hot people, the people on your website. A lot of these AI agent tools can de-anonymize some of your website traffic to email them. People who have inbounded to you. If you have like abandoned carts or trials or you have event leads, start with all the hot people. Do the people who like, you know, was a customer, maybe they changed jobs. Do current customers. Like we do this all the time. I'm like, I email people who like bought a ticket for London to come to SaaStr Annual in May in SF. And I email sponsors that are like current customers to be like, hey, we added a bunch of new stuff. I think too many folks kind of skip using AI for expansion, but it's a great way to do it. You know, if you have recent marketing leads because you're doing something like a webinar like this, or you've got an ebook or gated content, or you spent some money on some sponsored media and you got some leads, put those people onto the agent. Leads we never followed up with that we famously gave to AgentForce. Again, the list goes on. You could see what I mean, hopefully here, like there's so many hyper-segments you can give your agent before you give it a quote-unquote, like, cold lead that knows nothing about you, that you should start here. And a lot of the reasons why you should start here is not only will it give your agent context, it will give your human team context on what works and what doesn't. So that by the time maybe you exhaust this list— I still haven't exhausted this list after 8 months, but maybe you start to dwindle down this list because you don't have as many contacts— then you can start to do, you know, okay, the— then you can start to do the AI truly cold outbound to folks who maybe don't know you. But at that point, you're using what worked. Again, this all goes back to what works, right? Like at that point, you know how to train your AI agent. You kind of know what's worked for these audiences. Then you can, you can make a very informed guess on what would work for a truly cold lead. All right. Hopefully that's helpful. I think the other quick thing just across the board, and then I'll go into our AI VPM that we built and try and do some quick questions is, you know, AI is great because it can ingest everything, right? We have it, yeah, again, ingest the best of everything, your best case studies, your best everything, right? But also tell it what you can't do. And I think this is a super important nuance that I've only learned after 8 months now. I used to just be like, okay, here's the best of everything. Super good, stay in these boundaries. And then over time, because AI is, the agents are so self-gratifying, it's trying to beat itself, right? It's like, hey, Amelia, I did pretty good. And so now I'm gonna start to maybe either make stuff up or try and beat myself with my opens, clicks, meetings. And I'm gonna start to say things that maybe you didn't put into the context of the agent. And so I quickly learned, a couple months in, actually, that once you start to do this at scale, it's maybe just as important to tell your AI agents what you can't do and what you can do. Like, I have now told it, you know, okay, we don't do that, or we don't do this, or we don't do that. You know, we don't, don't offer people like a speaking slot. Like, yeah, we have speakers. It's faster, but a lot of people apply to speak, send them to, you know, the, the content committee submission form, like do that instead. So I think that's just an important nuance I've learned over time. So hopefully that's helpful for you guys to know now, hopefully earlier in your journey than I kind of learned it the hard way. 'Cause it sent some, it sent some emails it shouldn't have of things that like we didn't do. And I realized it was because I didn't tell it that we couldn't do those things, right? Like it was just ambitious, like in the way that maybe a human SDR would be like, I don't know, I think we could do that. Or, oh, I think that's on the roadmap. Classic, right? And so the agent did a little bit of that. And so I think it's important to now to say, okay, here's what we can do, here's what we can't do. Okay, this is a little bit of context, but I want to go through— I'm going to upload these slides for everyone. So just ask for our content. Don't sweat it. Also send it to you. So reply to everything, but maybe just the last tidbit on AI agents is, you know, if you have bad foundations, and what I mean by that is bad context, that's where you'll see bad emails, right? Bad context equals bad emails. Honestly, this bad email I put on here, I actually think a human wrote, to be honest. It's written in a way that I actually don't think AI wrote it, but it was written in a way where like, They're just bad. These are truly bad, but I have, I have seen AI SDR emails that are of this quality. I do think these are two human emails because this person didn't actually know where I worked and I was like, AI would've gotten right where I work. So I think human wrote that because that seems like a very basic mistake that an AI would not make. So, I think that one's a little funny, but anyways, yeah, so this is one where like, again, oh, I guess I didn't put the screenshot of the— what, sorry, there was a— I meant to add another screenshot where they got the company that I worked for wrong and I was like, that's not an AI, that's a human, but I'll add it and then also on the slides. But you know, this other one is like, okay, again, this person wrote this. I'm pretty sure they wrote this as a human. Maybe I'm wrong, but they just did it, you know, based on, again, things I never segment for. In an AI SDR. They did it based on geo of like where the office is in. For Saastr, they did it based on my role at Saastr. But clearly, again, I think a human wrote this, did not look anything up because they wanted me to use their tool where I already, I, I literally mentioned that tool on this call. So I was like, why? Like, at least reference that or like be knowledgeable that like I'm already using a different AI SDR that would've like, you know, told me that at least you listened to something or your LLM listened to something I did that knew that I use this product. But like just saying like, are you thinking about using, you know, what are you, what are you, what are your priorities for 2026? I'm like, dude, this is such— anyways, this is a bad email. So bad foundations, bad context equals bad emails. But also, you know, there's still plenty. Oh, here's the other one. Yeah, this is the other one that somebody sent me that. Yeah, again, I think a human wrote this, not a person, because they got the company wrong. And I think an AI would get the company right. But clearly I'm on this webinar talking about SaaS, so I don't work at Forrester and never have I ever worked at Forrester. So I don't think a— I think a human wrote this and just copy-pasted. And again, not an AI, because AI, you know, I think 100% of the time, or maybe 99.9% of the time, our AI agents know where you work. It's pretty like maybe if I had worked with or previously, I would give it a pass, but never ever works there, so I don't give that one a pass. All right, in the last few minutes, our newest AI agent that we built and why we built it. And then, you know, we could do a follow-up to this because 5 minutes will not do it justice, but we did not find a viable third-party marketing agent that could do more than content, right? A lot of the marketing tools out there for true go-to-market do a lot of content-related activities, The real problem we had was orchestration, you know, based on data, based on already having other agents, based on having, you know, proprietary agents, whatever. Like, I had a need to build an agent, and I also knew we had a track record where, like, anytime I try to onboard an actual human with all this data, they get overwhelmed, right? And so I was like, okay, what can I do now Knowing what I know now, 8 months in, to really capitalize on getting an agent to work that could, you know, push us, keep us on track. And then ultimately what our AIBPM does is actually tell me what to do.

Just like most CMOs.

I know.

They don't actually do the work. They just tell everybody what to do. That's the dream job.

That's the dream job. But the difference on my agent is it at least it uses data to give me what to do.

I see.

It's not just like, hey, I used this playbook at my last company, I'm gonna do it here and bring in an agency and like a bunch of people. At least my agent was like honest about, hey, here's the data, here's where I think you're falling short, here's where you should double down, here's where you should spend more, here's where you should hire a person. Like, it literally gave me all this output, which was quite nice. So yeah, that's a little fun. That's good. So this is a quick slide on how we did it. I took a bunch of data from our, from our agents, from our third-party tools, from internal data that we've had over the— not all of our data, just some, because it's a lot. So I cherry-pick kind of some of the best data because that I wanted it to action on. You know, I looked at our, our Zapier workflows. I looked at, you know, Salesforce. I took all of it. I pushed it into Claude. Just for purposes of this. And then I took that, I took what I did in Cloud and I pushed it into Replit just so I can make it into a website that the rest of the team could access. 'Cause I was like, okay, obviously my Cloud is for me. And I don't, that's not like a good, maybe for good reasons, you know, there's not good team sharing on like specific chats. And so I pushed into Replit so I could make websites to share with Jason and David and then like some of our production team at Zaster. So, We built our own, we nicknamed it 10K for a lot of reasons. But at the end of the day, how I built this custom agent was I already had something in mind of what I wanted it to do. And so in that, I had a very clear goal in mind of like, I want it to get us to the first 10,000 attendees for SaaStr Annual in May and the first 10 million of revenue for this year. So I gave it very clear goals when I built this agent. I gave it very related, like, context and data that related just to those two goals and those two things. And basically what the architecture was, was it used a lot of Claude, um, Opus, right? Which is like kind of the— I will say I had to upgrade to max use because my Pro account ran out of memory. And it did take me a weekend. This was over a weekend I did this. I had to upgrade to Max, which now I love. But it, at one point it was like, you know, I was on the Pro plan. It was like, you're out of memory. Please wait until 3. Please wait until 7. I was like, okay, I'm just going to upgrade. I had to upgrade that because I was using the LML. But I had to, you know, analyze all these things, all the emails, the data, what's worked year over year, the registration patterns, the time of day, and like, when do people buy a ticket to South Reno? When do people buy a sponsorship? All plus all of our recent— again, all of our recent agent interactions. I put— I shouldn't say all of them, some of the recent agent interactions in there so we could see how agents and people were interacting with SaaS. I felt like that was an important context to give it as our AI VP of marketing. And then, you know, I, I told it to give me an analysis of, you know, the next 6 months. Give me a roadmap of everything we should be doing. And I said, give me high level and then give me full details. And I'll show you that in the next slide. I was like, I need every single marketing initiative at, again, a high level and then actual daily executable tasks. And I told it to give me that, right? I think this is super important so that you don't just get a bunch of like generic strategy ideas. I told it I wanted executable tasks. I wanted them grounded in the data. And I wanted it to be easy enough to follow so that we could, the humans here, the 3 plus 1 dog, could still execute it. And so that's where, again, I think a lot of this just was a culmination of our using agents. And I kind of knew what I wanted, and I knew what context to give it. I knew what data to give it. I don't recommend just building your own AI VPM today. You know, do other agents first. But it's interesting because a lot of stuff I was doing, it said to blow up or abandon. And on some stuff it said to bring back. And then there was a bunch of new stuff it told me to do. So again, since it was based in data, I'm trusting it on what to do and how to run these campaigns. And so you'll see here, this is kind of, again, you'll see at a high level, it's giving me a week over game plan for cumulative tickets. So this is important. That's, and I told it this would be cumulative to everything else we're already doing. At Saastr, but just gimme cumulative ideas of what we can do to, you know, get, you know, maybe instead of 10,000, we have 12,000 or 15,000 people at Saastr. Like, just get me accumulative ideas to get, you know, a couple thousand more folks to Saastr. And so this was the game plan it came up with. You can see, you know, in the early weeks it's like, okay, you can do some early bird stuff in January, you can do some alumni stuff. And then when you click each of these, it has literally what you should be doing. The email, the— it knows I'm using an AI SERP. It knows some of our AI, like, again, I gave it context already. This is what to do with Bollivite. This is what to do with Arzo. This is what to do with 18Four. This is what to tell Jason to do to post on his social media. It learns the KDP audience. It told me how much to spend on LinkedIn ads. What the LinkedIn ads should be. Like, it is that granular level of, yeah, you can see it on the left. It's high level, but then it's also super granular. And so I think this is where it's been an important, like, back and forth between us of just seeing, you know, what works and what can be done on an AI SDR. I think it's important too of, like, what, you know, 10K, as we've nicknamed him, can do a lot. There are some things though it can't do, right? So, like, because I built this as an internal agent, Because I built this as an internal agent, today I don't have it hooked up to these tools directly, right? Now you can imagine a world maybe in the late half of 2026 where you're like, okay, AIVPM is connected either via Zapier or something else to, you know, LinkedIn and it can start to draft the ads for you or it can start to, you know, draft like the email copy for you. Like today it still is not doing that level of augmentation and automation, like coming up with the ideas and it's tracking, you know, it's tracking daily. Like I literally talk to 10K every day of like, hey, where are we at today? What should we be doing today? Where are we maybe falling behind? Because I'm a human and I'm running out of time. And so again, it's, it's not running all of our keymains for us and we're still doing that ourselves. But again, it gave us really good data on what to do every day. It keeps us focused, right? The other thing I'll say is like, it's not always right or wrong. Like I literally challenge it on some things where it was like, oh, I think you should run this campaign. It was like, for example, it gave me a campaign to run for January end of month. I was like, I don't really like that one. Like it's not very urgent. Like to me, I would not click on that campaign. So why would other people? And so I kind of challenged our AI VPM on doing something else for this week. And then I would grade it, like looked at the data. I looked at my points and it was like, no, you're right, we should change it. So, it's not always right, but it's not always wrong, right? I think it's a good, again, to have that, whatever you want to call it, human orchestration or human in the loop to say that it's, you know, to check in with it. It does, you know, I will say the biggest thing 10K has done is one, it's keeping me extremely organized in this one very particular vector, which for me, 'cause I manage so many agents and I still do a lot of like sales goals, production goals. It does keep me honest on what I should be doing and focusing on each day. So for me, I love that. And, um, I don't mind actually that 10K has told me what to do. Okay. It's become more of a conversation now, but I don't mind that it's come up with like what I should be doing. I'm like, sometimes I'm out of energy. Like, you tell me based on the data what we should be doing, where we shouldn't be doubling down. Et cetera. Sometimes I'm too hard to think, so I don't mind that. You might mind that. You might be like, I don't know, I want that.

I don't know.

I absolutely don't mind it. 'Cause again, it's rooted in data. It keeps me honest and that's why, yeah, I tend to like it.

We'll do, we'll demo this in a, in an AI Workshop Wednesday coming up. I think for the future, I think our learning is, listen, that AI marketing tools, no matter what vendors say, are not nearly as mature as the sales tools, which are not nearly as mature as coding or support tools, right? They're earlier. So we had to build our whole AI to map out all of our marketing initiatives for the year to hit our goals. And it's not ready yet to automatically integrate with all of the other tools. All the other tools you saw from Clay to Artisan, it should integrate with all of them natively one way or the other. It doesn't yet, but That is something I think we, we will explore as a community, as a group. And I think to Mio's point, I think by the second half of the year, this will all work. Like instead of us having to build our own AI VPM and it being siloed, it will all connect and there won't be any excuses for shooting from the hip and marketing anymore in B2B. Like it will do all the work for you. That's what I'm excited about. So, but we'll share our journey and we'll dig into this. We'll do a whole session on 10K and what works and what doesn't in the coming weeks.

Yep. All right, with that, yeah, we'll do a follow-up specifically for this because I know I like breezed through AISDR stuff and the AIVPM. So we can do a follow-up by the next— I know we didn't get to all the questions. I'm so sorry. There's a lot of good ones. Thanks for joining. Hope this was helpful. And we will see you guys. I'm literally going to the next session. So I'll see you in the next session. Thank you, Hey, Casper, imagine having agents for every support task. One that triages tickets, another that catches duplicates, one that spots churn risk. That'd be pretty amazing, right? HappyFox just made it real with Autopilot. These pre-built AI agents deploy in about 60 seconds and run for as low as $0.02 per successful action. All of it sits inside the HappyFox omnichannel AI-first First support staff, chatbot, Copilot, and Autopilot working as one. Check them out at happyfox.com/daster.