24/7 autonomous agents for everything with Induced AI’s Aryan Sharma | E1854

0:00number one you consumed a bunch of information to you did the work and learned how to code and build products and number three you got on a plane and you went to where the action was happening and you met people you networked yeah this is so easy and the industry is so wide open so for people in america who are saying oh my god i don't know how to break into tech consume every bit of content you can about what you want to do and and being an entrepreneur number two learn how to build products so all the information is on the internet and number three go to wherever the act most action is it happens to be the bay area but there's also stuff happening in dubai there's stuff happening in tokyo there's stuff happening in sydney and melbourne sometimes you just got to get on a plane and meet people in network it's very simple one two three you figured it out kid i love it this week in startups is brought to you by miro helps take ideas from in your head to out there in the world with its ability to democratize collaboration and input.

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1:51nuts.com is offering new business customers a free gift with purchase and free shipping on orders of 125 or more at nuts.com slash twist all right everybody welcome back to this week in startups we're covering this ai trend well for over 10 years but the last year has been absolutely extraordinary since chat gpt launched specifically 3.5 and then later 4.0 everybody else has gotten in on the action and auto gpts and baby gpts were all the rage a couple of months ago what were those they were like little user agents they would perform tasks anonymously maybe string a couple of them together like go do a search for a flight and then buy it for me this is what the internet uh and intelligent agents were always supposed to do but they never worked and uh it's a brilliant idea obviously a little bit scary to some people but it's the obvious future and as is the case founders uh always try to make the future get here a little bit quicker and today we're having a founder on the show arian sharma he is the ceo and co-founder of induced ai and what they're building is agents that live in a browser and it's called as i said induced ai welcome to the show arian thanks for having me um it's great to be here yeah and now you heard my thanks for coming you heard my intro there explain to the audience what you're building and why it's important yeah i can give you a quick background on why this is even a consideration and why how we came to the concept of agents i think one of the first traces of this the whole agent idea stems from the rl world which is reinforcement learning it's one of the earliest ai research years and what it basically means is you can have these models that are um learning how to do different things and they can they can try a bunch of different patterns and they get rewards when they're doing something right and then they get penalties think of it as penalties when they're doing something wrong so if you're teaching one of these models how to play tennis every time they hit a good shot they get plus one every time do something wrong they get a nice one and they're the way intelligence is baked into these systems is you just allow them to do a bunch of patterns and you keep rewarding and sort of giving them penalties when they're doing wrong and over time they try out a bunch of different patterns and they become smarter that was kind of the earliest traces of what agents meant where they have kind of agency to figure out and then self-develop a little bit of intelligence um we used to do all of this the whole agent premise was restricted to reinforcement learning for a while because you want to try out a bunch of things and that was kind of the only architecture that allowed um people to work and build these agents there's this paper called world of bits that open ai wrote uh in 2016 and it was andri karpathy and a bunch of others that was that was the first version of these agents where um they were teaching agents how to control the web how to play some games minecraft and a bunch of environments and they kind of tried uh building it in a reinforcement learning first way what has happened since then is obviously we've had transformers and we've had these amazing language models come in and they've opened up a new set of capabilities and after chat gpt came out last year it was evident that it's it's it's it's got a bunch of interesting capabilities you can ask questions it will give you responses it can sort of reason on its own you can there was interesting patterns that people the developer community was creating auto gpt baby aj i were versions of this where you have a prompt prompting itself and then chat gpt can talk to itself and have these loops and a little bit of that loop that is important in reinforcement being created.

5:36Give an example of that. Yeah. A great example of that is you, let's say you give chat GPT a task, come up with a copy for Jason's new fund and it comes up, you give it some description, this is the highlight of the funds, this is what we're focused on, et cetera, et cetera. And it comes up with a three-line copy. And then you take the three-line copy and then you just give it back to the system, analyze this copy that you came up with and make it better or give me criticisms. It gives you like three criticisms. These are the things that you can improve. And then you just kind of keep continuing the loop.

6:10It takes those three criticisms, goes back, and it's sort of trying to self-heal or self-correct in some sense. But it's all happening at the prompt level. So this is super easy for anybody to do. Nobody, there's no reinforcement learning. There's no model architecture, nothing involved. It's just smart use of language models. You're stringing them together in interesting ways to create this sort of agentic behavior. And what was interesting for a lot of people in the developer community was that now that we have these language models and we have these interesting new capabilities, maybe we can go back to the premise of agents, but design them in a language model first manner instead of reinforcement learning or some of the older versions of agents that were created.

6:48and auto gpd baby hi were first versions of that where you have a language model sitting at the center so you go and give them a command maybe you know go to google search up for something and then give me a summary and extract top three highlights from the summary and then that model that is sitting and taking in your input has access to a bunch of tools it can choose which tools it wants to use you can decide and go to this website perform this action capture this data processes this data and other. So it has a little bit of this reasoning engine built in to itself. And you can execute the task using those tools.

7:21So it's not the language model is no longer restricted to its training corpus. It's no longer restricted to just giving you text responses. It has a little bit of agency to use just text input output to interact with the outside world to interact with the internet leverage APIs as different tools, and do more stuff. You can't do that currently, with chat GPT, if you try to get it to do things on the internet. I did the other day I said hey tell me the best pairs of boxer shorts It gave me six different types of boxer shorts and I said, okay buy me one pair of each in medium 32 waist And it was like I can't do that So in your world it could do that it could go search the web for those six brands put it in the cart Check it out use my credit card Know my address and then ship it.

8:06That's what you're building, correct? Essentially we are building we kind of think of them as mini digital workers. They are these language models that are sitting and you can give them instructions and they have full access to a browser. We're only doing browser stuff. Now there's different ways of doing this where you can have it at desktop level, you can have it at mobile level, you can have it at the ADI level. But we sort of think that the browser is the most exciting and the broadest way to do it. So I could create a personal shopper using your software. I could tell the personal shopper, your job is to find the best item make a list of those report back on this and then I'll tell you which ones I want to order and then you go order them and make sure they get shipped based on this criteria.

8:53Or I could even take that out just say get me the three best coffees the highest rated ones and ship them to my home address and it would actually do it. Yeah, so the one caveat there is there are two ways to think of these agents. One very exciting premise of these agents is running them fully autonomously, which is what AutoGPT and a bunch of other agents originally started with. You're just giving a text description like this, go find me shoes or go find me flight tickets. And then it automatically figures out where it needs to go. It'll ask you questions to figure out what you want and then automatically kind of figure out whatever needs to be done to get you your output.

9:26That is great. That's kind of the future that we are building towards. But in the near term, the next four, five, six months, based on the current capabilities of the models that we have, it is a great like vision it's a great demo but when you actually implement it there's problems with reliability because fundamentally these models are non-deterministic and they come up with new outputs every time so you cannot be sure that every time you ask you know go and purchase shoes for me it's going to get you shoes from the exact same place or get it from and more likely than not it'll get lost because if these there's no rules that are surrounding them there's no guardrails around these models.

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11:11So to access our new Miroverse template and thousands of others, sign up today for a free Miro account at miro.com slash startups. M-I-R-O dot com slash startups. That's miro.com slash startups to sign up for free. So you're trying to do this with repetitive tasks like SDR, a sales development rep is a perfect example of a job people hate. It's repetitive, you go and find targets to sell some SaaS software to everybody gets these annoying, you know, email sequences, but they obviously work, people are still doing them. So that's one of the first use cases that you're building. yes so that's the the simplest analogy that this example of that is it's all stuff that was done previously with rpa software so uipath and you know assume these like large rpa companies that have existed for several years and decades um their idea was let's bring together tools that don't have apis and we can connect them together and build these bots what did you refer to those as what kind of companies it's called robotic process automation it's the rpa industry robotic robotic process automation, as opposed to business process automation, RPA is these repetitive tasks, robotic process automation.

12:23Interesting. Yeah, and there it's just you have these robots that are created mini digital scripts and workers that do a bunch of repetitive tasks. And that has existed for a long time. So it's one of it's a last span category for a lot of enterprises. That's how enterprises automate work, especially when they're dealing with tools that don't have APIs and that you cannot just, you know, string together to zapier or existing API tools, come to mind, right? Lincoln slows you down, it doesn't have an API, everybody wants an API, they don't want to give an API because they know that go fast. But with robotic process automation, you set up a browser, you search, it goes and does these things automatically looking for I don't know, CTOs, chief technology officers, puts them into a database, you know, and sends them a link and email whatever tries to guess the tries to validate gets their email and then validate it with an email validation service.

13:16And that's how people have these databases. If they ever try to sell you databases, based on LinkedIn data, it's these RPAs that have done that searching. And of course, they get turned off if they load too many pages. So it's a bit of a cat and mouse thing, correct? LinkedIn is a great example. You can look at a bunch of legacy industries like healthcare that have insurance platforms and claims processing platforms, all of these don't have apis a lot of real legacy industries rely on rpa the the problem is that rpa has existed for a long time it's just it used to be done in a very manual way where even though it's the eventual goal is you want to automate a workflow it takes a lot of effort to kind of set up these processes because these rpa companies go top down uh it's almost like for every dollar you spend on implementing rpa you have to spend five or six dollars on consultants who will actually come and understand your process they will you know buy one of the software from one of the vendors And then the reason it's so expensive and time consuming is that traditionally, if you want to string together and automate a workflow on the browser, you have to script every step.

14:16So simple Google search for launch or this week in startups is go to Google. You will have to click on the field, get the selector, the HTML selector, which is behind the scenes, the DOM or whatever of the web page. Then click on that field, type in your text, get the identifier of the button, then go to the page. It's just every field button element that you'll interact with on the web in completing your workflow, you have to manually go and script it. And the problem with that is scripting takes time, but also these scripts can break. Because if these websites keep changing layouts all the time, they keep changing selectors and class names all the time.

14:51And because you're hard coding it to selectors and class names from the HTML, if any of that changes, your scripts will break. So you have to constantly keep maintaining these scripts. um so that's kind of one and this is where a language model might come in because the language model would look at the page if your profile page gets updated or i should say linkedin changes profile pages and they just move the html around and people were looking for what city you're in and they called it location instead of city well that breaks the rpa right and so now i just said what's the location the language model should be able to figure out what's the location in the first, I don't know, 500 words of text on the page.

15:30Yeah, the language model is doing real-time inference on every run, so it can basically handle these changes. You don't have to spend as much time separating just broad directional input of the city. You don't need to pinpoint where the city is, look at the page, get the thing reliably. You can obviously set guardrails around that as well. That's kind of one problem of RPA that solves in this new world. The other problem is that with traditional RPA, you can only because it didn't have any reasoning skills you can only automate things that are you know rule set based let's go to google put in this exact text click on the first link they go this exact page you can it's you cannot do things like you know go to this linkedin analyze if it fits my ideal customer persona or analyze if this falls into the five cities that i want to target and then basis that you know do this action or draft a custom message so any any level zero cognitive reasoning tasks that you language models can be pretty good at um you cannot do them with traditional rps so we've we've kind of taken this this whole industry of how rpa was done and designed in an ai native way of how can we make setup and maintenance easy you can show us actually how this works yeah yeah i can pull up a quick demo it should give you a good idea of how um the base version works um so for people who are listening we'll describe for you what's happening on the screen?

16:50Yeah. So you just go to the Induce platform. You have... It's just an empty screen with no workflows right now. I'm going to click on new workflow on the top right. And it just asks you... You can either design the whole workflow from scratch, where you give step-by-step input, or you can use AI assistance. And I'll go through what that means. But just ask for a workflow name. I'm going to put in employee timesheet. And I'll run through what workflow I'm making. So I just put in an employee timesheet. And then I'm going to put in a step-by-step, this is just English description of a workflow that I want to design.

17:25And for context, the workflow is basically, think of a construction company or a company that has warehouses or physical centers across the country, and they have employees coming in and filling in paper timesheets. that is basically a log of when they're coming into work how many hours they're working what are the breaks they're taking etc etc and this company takes in all of these paper timesheets puts them on an air table and they have to manually calculate payroll for every employee because it's all on paper so you have to take whatever is on paper understand it then run it against a company policy doc to calculate you know this guy worked five hours this is a deductions this is overtime and then go and enter whatever payroll you've calculated back into an air table So this was we did this for one of our early pilot customers, they have a physical, they have a real back office of 15 people in their finance team that finance and ops team that does this.

18:12But with kind of in the new world with some of these agents, you can just describe this on our platform. crazy. So it has a identify, access the air table payroll, navigate to the employee payroll base from stored variables, identify a relevant employee, search for employee with the payment status, review timesheet data, open the employees timesheet, etc. And then it says calculate the payment, access the employment payout info sheet on Google Docs. Using the timesheet data, note the total hours work and time minus start time deduct any breaks, multiply the hours work by the hourly rate mentioned in the Google Doc to get the gross amount deduct any break time expenses, or other deductions.

18:54And so this is the step by step process that some human did, you're just describing it in essentially plain English, not code. Yeah. And then you just click create workflow, what we'll do with the AI systems is unlike a lot of traditional, traditional is a bad word to use, because it's all very new. But unlike a lot of other autonomous agents, we don't directly start running it based on English. And this is actually the first time we are ever showing this product on a media, like a podcast or a video stream, but this is this week in Startups exclusive. Thank you. But it takes in whatever input you've given.

19:30It compiles it. We have this middle layer in between, which is just the input that you've given, but structured into smaller steps. So all of the steps that I took in, it just broken them down, chunked them in. So it's just basically access a table, then loop through the entries, pick one entry, just smaller, smaller chunks of whatever I described. And the reason this is useful is one for visibility for who's designing this workflow. They can see whatever input, even what's the final translation. And then what you can also have visibility into is we split it into a bunch of different action types.

20:01So it's going to the web page is one action, clicking, filling, all of those are standard web actions. Then there's a bunch of data actions like looping, identifying, filtering that you can do on the page. and then we have the agent blocks which are all of the smart actions so once you go to an employee's timesheet any calculation that you want to do you can use the agent block to delegate and get inputs from a model so it's basically a bunch of different block types depending on what your workflow step is that we automatically identify and put in you you can obviously edit you can obviously make changes to this and at the end you can have standard you know elt or outputs in whatever format so after the workflow is run you know you can have triggers that if you have api call, put it in a Google sheet or anything that you need.

20:44So it just puts an end into these formats. And if you notice for those, you can see the screen. It automatically puts in these variables across the steps. So if your workflow involves capturing data from one place and then using it in another step later on, it's basically, I'll talk more about the browser environment, but this is basically a runtime that is designed for agents. So it has access to its own file system, its own memory, it can store data, retrieve data. So it's basically firing up a computer in the cloud, essentially, or a browser session. I don't know if you're using Chrome or Chrome OS or Windows, you can light up a virtual browser or a virtual machine anywhere, AWS, etc.

21:25So you're basically popping up a desktop and then running this stuff. Yeah. Yeah. So we spin up a Chromium fork on the cloud. So it's a virtual machine with a Chromium fork. It's a custom browser that we've designed specifically for running bots and autonomous agents and systems like that, which we can go into. But it's been set up, which is why it has access to all of these tools that we can use in the flow. And then once you're happy with the workflow, just click run. And this is interesting because the way it runs is the browser that has spun up on the cloud, you can see a real-time live stream of the browser when the workflow is running.

21:59So it's as if you're watching a team member's screen. You can see the stuff that's happening on the remote browser, streamed live to you on the left. And on the right, you can see step-by-step what's being done. So you can see the stream goes to Airtable. It loops through the entries. It picks one of the employees, opens the employee's data, opens up this timesheet. And then now it's going to run OCR. And it can use tools on its own as long as you're giving it step descriptions. I'll use OCR document processing to capture text input from this document, store it in memory. Then I'll go to this Google Doc, which has information about how you calculate payroll based on the timesheet data.

22:36It'll capture this in memory as well. And then once it has both of these things in memory, it's going to compare both the data points on a reasoning step, which is why reasoning steps usually take longer. But it's going to take both of those things in, calculate a final payroll amount, and you can see the variables being referenced here. We'll go back to our table, and it's just going to fill in. We've got a bunch of fields that were to be calculated. We're just going to come back, fill in. Total hours worked is 9, break duration is 1, overtime is 0, total payable is 7. You'll put in the hours, you'll put in total amount.

23:12And the interesting thing is because this is all running remotely, we kind of call this the mission control view of the platform where you can have a bunch of different browser instances doing different things, all the same thing, running at the same time. So it's like a real back office. you can scale up, scale down. You can have 50 instances running at once. You can have five. You've got one that's, you've got the Airtable instance. You've got one running LinkedIn. We've got one on AWS. There's a tech crunch thing here and you can kind of see live streams of all of them. So for the task of a manager who's managing a 15 member back office team goes from actually delegating tasks to real people to just sitting and watching 15 of these screens.

23:48And if something goes wrong, just go in and flag it. But otherwise you can kind of have a top level view of everything that's happening. So if I'm running some insurance company, sales team, customer support, whatever, my startup has these six or 15 agents running, doing the tasks that humans were previously done, and you just watch them and make sure it's doing it correct. And it feels like you've been working on this for less than a year. How many months into this are you? We started working on this around April of this year. So March, April is when we started. So you're six months into this process.

24:20Yes. And we've we've kind of identified. So just just as a as a disclaimer for this this run was very well described so i i gave you know explicit input i described everything and that's how it run it it doesn't require me to manually skip the whole thing and it runs reliably once i've given input but it requires explicit input and i think we we took that uh the the approach that we've taken architecturally which i was talking about uh was was actually it's like the most important thing in this six-month process that i think has helped us a lot where instead of designing an automation product that sits at a chrome extension and then you use it synchronously on our computer where I have an extension, I record something and then I can replicate.

25:01But I have an extension that is an assistant and I give it commands on my Chrome and it's doing things. We design everything to be remote and have its own environment. So we took Chromium, focused it, made some changes to it of how the DOM comes out, how the HTML comes out. Has anybody started using this yet in like a real world situation or are you still in the laboratory? We launched early October and then we went live with a couple of folks we are live with about 15 uh now and that is across different sizes small companies mid-sized companies we've kind of found our sweet spot in these mid mid market to mid to up market companies that are operating in sort of older industries like healthcare um you know financial services etc um so it's live with with a few of them we are constantly we're changing the form factor a little bit because with the text input that you saw where You have to describe your workflow and then it translates to the actual workflow that's running.

25:58It's the easiest way to start. That's how most AI language model products start, but still not very intuitive if you think of it from the user's perspective. Because when you're typing the text description for your workflow, you basically have to have a browser screen that's open and then you're typing out every step. I'm going to this and I was just like, type out, okay, go to this. Then what am I clicking on? I just type out, click on this. and what we are designing now is it's sort of like and this is the video that i i'll share so you can probably edit in because it's it's not even an alpha right now we're going to put it out in a few weeks where it's sort of like think of a collab notebook a google collab notebook where you have a bunch of um cells and if you want i can show you google collab as a reference but it just we have so this is this is how collab works it has these bunch of cells and then you You can basically give in, like you can run each cell.

26:45So I can be like, you know, print, hello world. This is how regular colab works. And then you can add, like each cell can be run and then you can have a bunch of more cells that come together and you can string together cells to create something. We are creating a new interface for how we set up workflows where instead of you having to type everything at once and then edit them and then run it and then come back and debug, we have this environment that is set up for you to create workflows. so you kind of just come in and instead of typing out code snippets you have you just type out your english steps and we have in the place of this section on the right we have the stream that we had opened up so you just come in and go to google and then you can actually see it go to google and you're like click on like search for this we can start up and you actually see it happen um so it's basically real time you know setting it up once you're happy with every step just click confirm and then it automatically migrates to workflow it's much more intuitive it's easier if you had a press monitoring service you could say search the web for this person's last name or this person's name uh go to google news click on the link summarize it email it to this person let them know they were in the news uh you know or mentioned in a news story that's a job that pr people uh do for a living now are you going to get the you know you'll probably do some mistakes and probably will put old stories and it's gonna you know not be perfect in your world but it would certainly make the person who does PR clipping, and what they call media monitoring or web monitoring, it would make them bionic, they would be able to do 100 times what they do every day.

28:16So essentially, that's what you're doing is then anybody who has a business process can basically script this. Starting a business used to be such a painful process, you needed to get a lawyer, there were tons of fees, it was a mess, but not anymore. Just check out Northwest registered agent. They're going to help you form your company fast. Remember, speed matters. And then they're going to get you the docs you need to open a business bank account instantly. Then they're going to provide you with mail scanning and a business address. And they're going to do all that keeping your personal privacy intact.

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29:28northwestregisteredagent.com slash twist. So what happens to the business processing outsourcing industry in India, where you're from? And I know you have investment from Sequoia India, or what is now there's a new name for that firm. But peak 15. Yeah, so you have an investment from them. When you show this to the people who are in the business process outsourcing, do their heads just blow up and go oh my god what's going to happen to these 100 million people employed in the business processing uh world or has this just been a continuation and there's always going to be more business to process yeah i think there's one it's it's an extremely large market where this where outsourcing happens um and it's just increasingly we're all the tech is growing but it's like the the legacy industries and there's all sorts of unique things that keep coming up which keeps growing the business process, outsourcing of the back office, or the Philippines, India, all these things that get delegated outside.

30:28And you have these economies that are created for people that are remotely doing these tasks. So I think the market is huge. They always need more automation. So every time we go, and we've had a lot of people who run these global, they're called GCC. So they're a global back office centers and there's a bunch of different words for them. We got a lot of them reach out and they're always looking for automation, even though they have 50 ,000 people sitting and doing these things because they want to make it more efficient. They want to be able to take on more business. So a lot of them want to be customers of this so they can improve internal processes.

31:00But I think in general, the trend that we're seeing is this will kind of take low-level things first. So the extremely repetitive things that have very little cognitive involvement are almost always easy to automate even with traditional RPA. With these models coming in, we'll be able to do a little bit of cognitive tasks as well, where a little bit of thinking, filtering, profile validation, lead enrichment, things like that will go in. But then there's always complex tasks or sensitive tasks that you want these back offices to do like payments, et cetera. So I think it'll be a slow migration, but this kind of just trajectory of tech where - Incredible.

31:36Yeah, GCC stands for Global Capability Centers. This is India. When you want to outsource a back office, you were Uber or Airbnb and you have a huge amount of, I don't know, So refunds to process or claims to process or complaints, whatever. You would hire a GCC to work with you on the best practice, hire some people in a lower per hour location and to just get it off your plate in America where, you know, people candidly don't even want to take these jobs. No matter what you pay them, they just wouldn't take them. And, you know, that's what's been happening for, I don't know, for all time, but certainly in the last 30 or 40 years, this process of outsourcing.