AI Agents & the Future of Work with LangChain’s Harrison Chase | AI Basics with Google Cloud

all right everybody welcome back to Startup Basics this weeken startups. com Basics is the URL where you can find these videos and the series that we've been doing for over 5 years now what do we do in startup Basics we look at things that Founders you know need to get right and that they might make a mistake on sometimes or they might not know about an opportunity that's why we call it the basics legal with our friends over at Wilson cini we do accounting with our friends over at Cruz and we're really excited because one of the basics you have to get right today is AI every startup I see whether they're an AI startup or not they're using AI to run their companies and one of the topics we talk about here on this week in startups all the time is static team size a lot of folks are sticking with five or 10 or 100 people at their startup and instead of hiring people just running up the head count they're deciding hey maybe I could automate stuff maybe I can use AI to figure things out and so it is now one of the best practices that you got to get right just like you got to get your legal just like you got to get your accounting right you got to get your AI right inside your startups don't I know it when people email us their decks and they apply for funding from us you know what we do Zip Zip Zip people didn't know this I don't want to say cuz I'm gatekeeping here one of the things things we do is we send all that information to Gemini I don't know if you know Google's really amazing large language model in service and then Gemini spits out this great report based on our criteria and this analysis of the startup so we can just read that short summary based on all the information we have and you know what I have some researchers and analysts who write short summaries and I get the Gemini one I think the Gemini one's a little better up totally honest than the humans then the humans can go do more important work so there's like an example for you of how you know this stuff is impacting everything and we're so excited we have a partnership with Google Cloud for this series they just published an amazing report it's titled the future of AI perspectives for startups hey that is really on brand and on target for us so what are we going to do here on this series in other series we would have one guest and maybe we do four or five topics here we have so many great opportunities to have a rotation of guests on who are building important Tools in AI and uh you know who our friends at Google may or may not have uh Partnerships with and today we're very lucky to have Harrison chase the CEO of Lang chain on the program they provide a framework for building and designing an llm of your own which sounds like something Harrison I need for maybe uh startups and Pitch decks welcome to the program Harrison thanks for having me it's great to be here tell me a little bit about Lang chain and then let's get right into it what do you think startup founders should be thinking about when it comes to using AI inside their companies to get an advantage to save money and to create better products yeah the the types of applications that we see people building they're starting to be ones that do the work of what humans would do in the past so if they're kind of like functions inside a company that you would hire what I like to say a smart intern to do um those are now functions that can kind of be automated by some of these AI systems so for example with inside L chain we have a few places we use this I have an email assistant that helps respond to all my emails we have a customer support bot that helps with some of the customer support issues we have a marketing bot and uh we have a uh SDR bot and so all these are places where we'd hire maybe like in the past an entry level intern and entry-level person and and you know because we build tools we like to dog food them and so we're dog fooding our own tools by trying to automate some of these processes away and the interesting thing about this dog fooding you're doing is the positions you talk about are not positions people want to stay in for a career they entry level they're the first rung of a career ladder and you know what there used to be when I was coming up I'm a Gen X you're like a I think you're gen Z or Millennial you're gen Z I think right Millennial Millennial you're Millennial okay great you're team Millennial no judgments there you know Gen X we're like the last Freer range generation we're a little crazy on the margins but when I was coming up the reception desk working in the mail room working in the typing pool working as a runner which is basically somebody who would run packages of paper around those were the entry-level jobs you know what happened to those jobs AR email the internet you didn't need a receptionist you put technology in the front people badged in they pressed a number and whatever somebody came and got them you didn't need all of humans doing those and now we have another series of them that are entry-level jobs SDR is a super fascinating one sales development rep a sales development rep for folks who don't know they find leads they get those leads they warm them up perhaps and then they hand them off to an account executive A salesperson uh in plain English tell us a little about about the agent that you created for the SDR role what do they do and um how well does it work and how long have you been deploying your SDR agent that's probably one of the newer ones um basically what it does is we get a lot of inbound leads it does some research on who the people are um and it actually drafts an email to them um if if it thinks they're interesting so does it's you know it uses the reasoning of the models to determine whether it's kind of like an interesting Prospect for us um it does some research on events that have happened to their company recently and then it will draft an email and and uh notably for for all of these positions you're absolutely right that they're kind of like entry-level positions but I want to call out that we have a sales team we have a head of customer support we have a product marketer it's not like we're eliminating these functions completely it's rather like these are doing some of the parts of the job that people don't want to do they're not the creative part they're not the kind of like the value ad part and then they're hooking in they're they're communicating with the kind of like the experts when needed so when it drafts an email we have human in the loop that will go in and kind of like approve the email or something like that so these these like you know we have we have a really good sales team I think they can go in and basically talk to this junior intern and say no this is the wrong email like don't send it to these types of people in the future so there's still this human in the loop component I think that's really important for enabling a lot of these applications I think this is critically important at this stage in 2025 when we're recording this because we do see on the margins a hallucination here or there and um you know you don't want to have a hallucinated mistake in an outbound email to a perspect perspective customer nor do you want it to make a mistake and say this person doesn't need the product we're not going to email them so I like this you know taking those emails that are outbound maybe putting them in the you know in your drafts box you take a look at those 10 you just read them okay maybe we shouldn't talk about I don't know it pulls their high school or something and mentions their High School in the email and that's like the super important part human in the loop reinforcement learning is a very important piece of this as well because over time you know these things could take on more and more work maybe you know you look up hey this person's company has 10 employees this other person has 10,000 maybe the one with 10,000 we should just book a a zoom the person with 10 people hey maybe it's okay to send that one automated you know it could depend on what you're doing there how hard is it to create these agents and then are there situations where these agents have you know gotten a little bit out of control maybe they jumped the fence how do you protect against that because that's everybody's concern right they may not say it to you but people are like oh my God I don't want an agent to go wild just like back in the day we wouldn't want somebody to spam you know and send a hundred accidental emails could be embarrassing could be annoying to our part partners and customers well that's exactly why the human and loop stuff is so important and I'll I'll get to that after I answer your first question I mean we still see that it's still it's still pretty hard to create these agents so we build developer tooling to help people build these agents we see that most of these agents are still being created by developers there's a lot of Integrations to figure out there's a lot of uh what we call kind of like the cognitive architecture of the agent like what information is it looking at how is it processing that information it's still a lot of work to get these agents to work and the ones that we see work um some of the ones that have been built with our tools repet LinkedIn Uber Clara gitlab these are like vertical agents they're not like fully autonomous ones they're vertical ones doing kind of like you know specific domain tasks and then for the for the question around how do you keep these on the rails this is this is why the human in the loop stuff is super important as well and I think there's two there's two big benefits to human in the loop one is what you talked about like it keeps them in check it basically doesn't let them go off the rails you have people not at every step like I think part of the benefit of having agents running in the background is you don't have to be involved at every step you can be involved at the most important step so for example like you can be involved right before an email is sent because that's more important than before a Google search is done like you know it's it's kind of like a read versus right operation so it's more you put them in at kind of like the crucial steps where it actually could do things that would not be good but the second underrated part of human the loop is what you were talking a little bit about earlier is basically aligning the agents with what you want them to do so when they first start working there's probably some prompt um and that prompt is you know like I I have like I I think I'm relatively good at prompting I wrote The Prompt for my email assistant I still forgot a ton of edge cases about who I would want to respond to or what emails I would want to ignore just like didn't come to mind as I was writing that prompt and I don't I don't think it's realistic to ever write like a perfect prompt right off the go and so this human the loop helps you kind of like if you set up the proper kind of like systems it helps you update that prompt and update instructions and basically align these agents with what you actually want them to do and so I think there's two really important benefits to human in the loop let's talk about where this will be next year so we're referring to AI as interns and we probably referred to AI three years ago you know as if you're m in Gmail uh you know guess the next word and then it was like guess the next two words you know we were kind of in that nent phase if you said hey I'd love to invite you say to then it said to lunch and then it said to lunch to discuss and whatever you get the idea and now here we are saying hey read my email and draft something put it there where would we be next year uh and then the year after so let's talk about 2026 2027 if these agents do a good job in 2025 hey they go from being interns maybe you know they get the full-time job entry-level job maybe to the next job and of of course we're giving this a caveat of this is the exoskeleton if you think about this like an Iron Man suit you still need to have humans at your company but they're going to be able to take the grunt work have ai do it or do 80% of it you're going to get that those superpowers as it were right so maybe talk about what your predictions are for 26 and 27 yeah I'd say um within a year we'll probably still have still have interns they'll just be smarter I think the models will get better I think we'll get a little bit better at hooking them up tost but I think they'll still be kind of like smarter interns after that I think there's like two two kind of like steps that will happen one is this like memory component so interacting with these agents and having them learn from from your feedback um I think that'll be really important for aligning them because it doesn't matter how smart the intern is if it doesn't know how you like to do things at your company like you have if you can write down a standard operating procedure for the role that's fantastic um and we we don't have that for all rules and I don't think it's realistic to ask that but people do pick up those those processes that they should be following through memory um that's what we do as humans so I think that'll be something that we start to work on probably towards 2027 and then I think the other thing will be right now these interns are pretty independent they just work by themselves so the agents I talked about like repet has its agent that's pretty separate from Clara's customer support agent what happens when these start being able to talk to each other and hand off things and so multi-agent systems are probably something that will also pop up in like 2027 multi-agent so you got the SDR you know processing the inbound leads drafting the emails and then you're going to have a CRM agent cleaning up the database over there and saying hey we just updated everything over here about our customer and let's say the customer was I don't know McDonald's or Starbucks and it's like oh if you see anything from Starbucks or McDonald's on the inbound please take the account executive listed in our Salesforce HubSpot whatever and um check with them first or CC them or put it in their outbox wow that's kind of dope when you start thinking about how these things might work together uh it could become really interesting when will they be sort of working next to you I've always envisioned like these things having a bit of a Persona maybe we give it a name hey this this is jcal my SDR and uh you know this is Harrison or this is Chase my you know CRM manager and they keep the database up to date' be kind of cool if they were like in the slack or they were in your teams or sitting in a little window here while we're on this Zoom call and maybe listening in contributing on the margins hey you know I was on the sale standup we heard you talking about Starbucks and so we wrote A little update on the latest news from Starbucks there's a new CEO here's what's going on there so we just took the liberty of writing a dossier to uh educate everybody and then we did a quiz where we quizzed all the sales team who are associated and the customer support people on the history of Starbucks so they know they have a little bit of small talking banter they can do why aren't they hanging out with us yet and when will they hang and be like peers in these spaces so we call our customer support bot Carl and Carl hangs out in our slack um so I in the slack now Carl's in the slack yeah he's not sending dank memes right you talked to him about the Dank memes do not send don't bring up politics at work tell him we're focused I don't that's when you know we hit the singularity Carl starts sharing memes Carl's the only one that's in the slack so so there's four Carl's the only one that's in the slack why is that the case I I think like the big thing um or a big thing to figure out is like what these human agent interaction patterns look like um and I think we have some idea and I think the idea of treating them as like a coworker in in slack or teams or something like that makes a lot of sense but it's still really early and so I think I I think one of the best spaces that companies can be spending time is thinking about what does this like human agent collaboration pattern look like if you look at like a lot of the companies that have kind of taken off I mean like chat GPT chat GPT changed the ux that we used to interact with llms uh you know turned it into a chat bot like doesn't seem like a big thing now but like that was a in the ux I think cursor for coding has done a fantastic job at nailing the ux for developers in the IDE or Google search has the snippet up top and I have to say exactly changed my behavior really because now I get my behavior was you know bifurcating okay I I want to talk and do a chat interface on an llm sometimes and other times I kind of like the presentation of let's say Google flights or Google local or shopping like there's like a lot of like intricate uh things that Google provides Maps uh Etc images and now you kind of have both and so that's become super powerful sometimes they go to do a search and the snippet up top or whatever they call that it used to be called the one box snippet I don't know what they call the little chat window up there but boy is that helpful because you get both and I was wondering when they would do that because that would take a lot of servers but yeah I do believe the ux is going to be quite interesting final question for you used to have to hire a developer to do anything and maybe a script Kitty on the margins or whatever now I'm seeing a lot of people using you know pick a platform uh notion Koda slack and then they use something like zapier or if this than that kind of glue some workflow together and I think some of those other products are starting to add a little bit of workflow here on the margins you know simple stuff but when will a non-developer be able to do the coding for agents because we are seeing you know in the startup Community I've had three or four startups come to me with no developer and they built MVPs and I'm like well that's pretty impressive so can they do you do you have that on your road map English language agent creation is it on your road map at Lang chain I think um it's it's not super close on our road map the agents that we see being built that are the most like um intern like they're all built by pretty strong kind of like developer teams so so repet has a very strong developer team gitlab does this well clar does this well and I think the reason for this is is a few fold one I think the best practices for building these agents it's still super early on like llms have really only been a thing for in the Public's mind for about two years um and agents for maybe like a year and so we're still figuring out what the best practices are and so there's a lot of control that you want to be able to have and then another big part is giving these uh systems access to all the uh you know other systems that exist within a company and this is very heavy on Integrations and and that's a place where there's a lot of Need for coding and data engineering at the moment so at the moment to be honest I'm a little bit skeptical that we'll see that anytime soon most of the most of the most impressive agents we see are being built by strong developer teams still small price to pay good use of developer hours to make an agent that then you know takes out I don't know if it's like 2 hours a day and you're working 50 weeks a year times 5 you know you're talking about 500 hours one of the nice things too is these things can be working 247 uh that's why they're agents and they're running in the background so I think it's like uh a super fascinating concept uh so well done where can people find out more about your company if they want to use your solution you can find us at Lang chain. com or on Twitter or LinkedIn awesome everybody go check out Lang chch and uh see if that's the tool right for you if you want to save uh you know a couple thousand hours of work every year in uh The Intern jobs and not have interns doing grunt work have them do something more interesting in your company all right thank you to Harrison Chase for joining us here on the AI basic series on this week in startups you can see all the this week in startups basic series at thisweek in startups.

com it's a long URL I know slash Basics and if you want to check out Google Cloud's awesome the future of AI perspectives for startup report go to go. GLE future of AI that'll be in the show notes as well for predictions real world examples and tons of startup advice once again the URL you can write it down right now go.