0:03 Hi, I'm David from Retool. Large 0:07 language models are really smart. They 0:09 chat, reason, and help you code. LMS can 0:12 talk, but they can't act. To act, LM 0:16 need tools. That's why we've introduced 0:19 tool agents. We give LMS the tools to 0:22 execute real work inside your business. 0:25 Let's go see how it works. 0:28 On your left side, you see a typical 0:30 office workflow. And on the right side, 0:32 in the virtual office, you see agents 0:34 doing the same kinds of tasks, coding, 0:37 messaging, resolving issues. Much of 0:40 this work is repetitive and can be 0:41 automated, but only if LMS have 0:44 powerful, specific, and customized 0:46 tools. That's the key. So, let's zoom 0:50 in. On the left, our accountant 0:52 performing a multi-step manual process 0:54 to fight a chargeback in Stripe. takes 0:57 five minutes. So long, we had to in fact 1:00 speed it up by 10 times. And on the 1:03 right, retail agent number 17 is doing 1:05 the same task, but in real time. The 1:08 secret powerful predefined tools we gave 1:12 the LLM to go get chargeback from Stripe 1:14 to go gather evidence from your 1:16 Postgress database and submit it back to 1:18 Stripe. In those 5 minutes, agent number 1:21 17 finds 50 chargebacks. 1:24 Wow, so much 1:26 faster. That's the power of LM with the 1:29 right tools. And with Retool, you can 1:32 manage your agent and replay all the 1:33 work he's doing as if you were watching 1:36 over his shoulder. Or you can build Reut 1:38 agents to automate work all across your 1:41 company. create one to handle daily 1:43 project management tasks by listening to 1:45 standups. creating and assigning tasks 1:47 in Jira, following up on blockers, 1:50 syncing updates across different data 1:51 sources, and keeping everyone aligned 1:53 without lifting a finger, or another 1:56 agent to prepare materials for sales 1:58 calls by researching attendees on 2:00 LinkedIn, checking product usage from 2:02 your internal databases, pulling 2:04 internal notes from Salesforce, drafting 2:06 talking points, and generating a 2:08 personalized pitch deck in Google Slides 2:10 in just 2:12 minutes. or one to act as an executive 2:15 assistant to find time across packed 2:17 calendars, coordinating across different 2:19 time zones, rescheduling conflicts, 2:21 booking meetings with full context, and 2:23 assuring your calendar is always 2:25 perfect. With Retool agents, LMS finally 2:28 have specific custom tools to tackle 2:32 your business problems. Join the 2:35 thousands of other companies using 2:36 Retool to automate real work today. Our 2:40 customers have already automated over 2:43 100 million hours of labor using AI in 2:46 retool. That's a whole 5,000 personsized 2:49 company working for an entire decade. 2:53 That's nearly 5 billion in value. But 2:56 with rutual agents, we're setting an 2:58 even more ambitious goal. Automate 10% 3:01 of US labor by 2030. Rutual agents are 3:04 available now. Can't wait to see what 3:07 you build. 3:11 [Music] 3:18 You've just seen a glimpse of what we 3:19 think software could look like, not in 3:21 some distant future, but right now, our 3:24 goal is ambitious, but straightforward. 3:28 We are aiming to automate 10% of US 3:30 labor with retail agents by 2030. 3:34 Now, that might sound a little crazy, 3:37 but our customers have already automated 3:39 over a 100 million hours of real work 3:42 with Retool. To put that into 3:44 perspective, that's around 5 billion of 3:47 actual concrete value unlocked. That's a 3:51 whole 5,000 personsized company working 3:54 for an entire 3:56 decade. AI has had a weird few years. 3:59 Companies have poured billions of 4:01 dollars into AI, but largely speaking, 4:03 we've got chatbots. They're impressive, 4:05 but limited. They talk, but they don't 4:08 add. They don't actually integrate into 4:10 your business systems, trigger your 4:12 workflows, or perform real tasks. MCP is 4:15 great, but it's just getting started 4:17 right now. Chatbots haven't 4:18 fundamentally automated work yet. And as 4:21 a result, we have this enormous 4:23 disconnect between investment and 4:24 outcomes. In fact, there's roughly a 4:27 trillion dollar gap between we've put 4:29 into AI and we've gotten out of it so 4:31 far. So, what's missing? ALM can 4:35 conclusively pass the touring test, but 4:37 the problem is that they just can't do 4:39 anything yet. Even with MCP, which we 4:41 love and support with our product, many 4:43 of the tools just aren't specific enough 4:45 to your business. And so, if we're 4:47 serious about using AI to fundamentally 4:49 reshape productivity, we need more than 4:51 just sophisticated conversations. We 4:53 need an app layer for AI. A practical, 4:57 reliable, and scalable way to convert 4:59 powerful LLMs into software that does 5:01 actual work into software that solves 5:04 real business problems. This app layer 5:07 would bridge the gap between what an LM 5:09 can do and what a business needs done. 5:12 It's the layer that transforms AI from a 5:14 promising technology into measurable, 5:17 actionable productivity. 5:20 Now, some of you have already started 5:21 building AIdriven apps and retool. 5:23 That's great. But what we're introducing 5:26 today takes it further. Agents represent 5:29 a completely new type of software. They 5:31 automate reasoning at scale, safely, 5:34 unpredictably. They meaningfully expand 5:36 the scope of what software can do by 5:38 giving LMS real tools connected to your 5:41 actual data and 5:43 processes. It turns out there's a pretty 5:45 simple formula here. If you take 5:47 powerful LLMs, pair them with hypersp 5:49 specific, carefully designed tools, and 5:52 deploy them on retool, you get agents 5:54 that deliver meaningful impact to your 5:56 organization. Every retool query or 5:59 workflow you've written can now be used 6:01 by an 6:02 LLM. That is the secret to unlocking 6:05 value from LMS. LMS that don't just 6:07 chat, but actually 6:09 do. This isn't an incremental update. 6:12 This is Retool's next major chapter. 6:15 Today's launch marks the beginning of 6:17 our path towards a fully AI native 6:20 engine. A secure, reliable, 6:23 enterprisegrade and developer friendly 6:25 platform that allows you to leverage AI 6:28 everywhere across your company. And the 6:30 reason we're leading this effort is 6:32 because we've already built a 6:33 foundation. RTOL already powers mission 6:35 critical software at thousands of 6:37 companies. We're already the app layer, 6:39 the connective tissue between your data, 6:41 your logic, and your workflows. Every 6:44 retool primitive you've built is now a 6:46 tool an LLM can use. 6:49 Wow. And this is why you, the builders, 6:52 creators, and engineers are essential. 6:55 You are the ones who will take AI from a 6:57 series of impressive demos and transform 6:59 it into something tangible, powerful, 7:01 and actually useful. The real story here 7:04 isn't about AI. It's about what you can 7:06 actually achieve with 7:07 it. So, let's dive in. Kent will now 7:11 show you exactly how to build your first 7:13 retool agent right now and demonstrate 7:16 this is already changing what software 7:18 can accomplish. Hey all, I'm 7:21 Kent. Let's learn more about retool 7:24 agents. At its core, an agent is simple. 7:28 You give an LLM some input, access to 7:32 tools, and you let it run until the task 7:34 is done. 7:37 Although agents are simple, there are an 7:39 enormous number of decisions to make if 7:42 you're building agents from 7:43 scratch. Everything from the framework, 7:46 the cognitive architecture, providing a 7:48 cohesive model layer to the more 7:51 traditional considerations of 7:53 deployment, infrastructure, and 7:57 scalability. We saw our customers 8:00 rebuilding this AI orchestration layer 8:03 over and over. So we built retail agents 8:07 to take care of the undifferiated work 8:10 and give you a flexible powerful system 8:14 to automate real business processes 8:16 without reinventing the wheel every 8:19 time. So how does this work? Let's have 8:22 a closer look at the project manager 8:24 agent that we saw in the previous 8:27 clip. As the description says, this 8:30 agent helps manage projects for an 8:33 engineering team. 8:34 It listens to our team standup and keeps 8:37 our project tracker docs and 8:40 stakeholders up to 8:42 date. It saves a lot of time we'd 8:44 otherwise spend tracking down and 8:46 copying information across different 8:50 systems. This is exactly the kind of 8:52 thing that's suited to an agent rather 8:54 than a standard predefined workflow. 8:58 I needed to operate across multiple 9:00 contexts and make judgment calls like 9:03 interpreting something my teammate said 9:05 in standup and turning it into the 9:07 appropriate action item or 9:10 contextualizing exactly what to share 9:12 with different 9:15 stakeholders. Now this agent usually 9:18 runs programmatically. It gets triggered 9:20 from a web hook but we can also chat 9:23 with it directly. 9:25 For example, we might say, "Make sure 9:28 everything discussed today is attracted 9:30 linear and an update is sent 9:33 out." We can see the agent get to work. 9:36 First, it reads our standup transcript. 9:39 Then, it pulls in linear tickets to 9:41 understand the current project 9:43 state. At any time, we can inspect its 9:46 thoughts and see what it's doing and 9:49 why. 9:50 We can view the tools it's using and see 9:53 the exact inputs and outputs from each 9:55 one. The agent isn't a black box. You 9:58 can follow along at every 10:01 step. Here it's retrieved some data, 10:04 created a few tickets, and paused before 10:08 sending a project update. It's waiting 10:10 for my 10:11 approval. This looks good, so I'll 10:13 approve it, allowing it to send that 10:15 email and wrap up its task. 10:19 These agents are powerful. So let's have 10:21 a look at how you set one 10:23 up. On the configuration page, we define 10:26 their behavior. As a builder, this is 10:29 totally customizable. So you can adapt 10:32 agents to any scenario from simple 10:35 chatbots to systems that manage complex 10:38 processes. You can choose which models 10:40 to use. We provide OpenAI, Anthropic, 10:44 Llama, and Deepseek out of the box. or 10:48 you can connect to any other model 10:49 you've set up in 10:51 retool. We can also set model parameters 10:54 like temperature which affects the 10:56 creativity. And importantly, we can set 10:58 a maximum number of iterations to 11:01 prevent agents from getting stuck in 11:02 loops and burning a bunch of 11:05 tokens. Now, let's talk tools because 11:08 tools are actually what make agents 11:10 powerful. To do anything useful, the 11:12 agent needs secure access to your data 11:15 and systems. 11:17 Interestingly, the types of tools we 11:19 give agents are basically the same 11:21 things we've been building retool apps 11:23 on top of for 11:25 years. Agents come with lots of 11:27 pre-built tools. Google calendar, Docs, 11:31 retail storage, email, web search, code 11:34 execution, data visualization, and more. 11:38 We can easily add any of them to our 11:39 agent by selecting them here. 11:43 For the use cases that are specific to 11:44 you and your business, you'll want to 11:47 build custom tools. These can connect to 11:50 anything you've integrated with Free 11:51 Tool, giving you the ability to create 11:54 tools on top of almost any database API 11:58 or third party SAS 12:01 tool. Existing workflows and other 12:04 agents can also be used as tools. This 12:06 allows you to create multi-agent 12:08 systems. And even better, we also 12:11 connect to any remote MCP server. So any 12:14 MCP tool like GitHub or Cloudflare can 12:18 immediately be imported and used in 12:20 retool as a 12:23 tool. Looking at this agent's tools, we 12:26 have a REST query that pulls Zoom 12:28 transcripts, a set of linear tools for 12:31 getting and creating issues, and an 12:34 email tool for sending project updates. 12:38 Some tools read data, some tools write 12:40 data. For example, the create issue 12:43 tool, which writes data, expects some 12:46 inputs. We've defined those here. A 12:49 title, a description, and an assigne 12:53 ID. You want tools to be tightly scoped 12:56 with clean interfaces, and clear 12:59 descriptions. This helps the LM pick the 13:02 right tool and provide the right inputs. 13:07 This is the tool definition. The 13:09 implementation lives inside of a 13:11 function which is a lot like a retool 13:13 workflow and lets you implement custom 13:16 logic that interacts with all your 13:19 systems. Here we're making a GraphQL 13:21 mutation to create the 13:23 issue. Custom tools let you combine the 13:26 flexibility of LMS with the 13:29 trustworthiness of tested code. 13:32 The LM probably could generate this 13:34 query, but we found that getting out of 13:37 the LM as fast as possible and into our 13:40 deterministic code makes the agents much 13:43 more reliable. If we know the query 13:46 works, we want the agent to use it every 13:49 time. We let agents use most tools 13:52 autonomously. But for the send email 13:55 tool, we've required user confirmation 13:57 first. I want to double check anything 14:00 that's going to public 14:02 channels. By specifying this on the tool 14:04 level, the platform ensures that your 14:07 agents never do anything without you 14:09 first reviewing 14:11 it. So, we've seen an agent run, how 14:14 it's configured, and how it's connected 14:17 to tools. But how do we make sure it 14:20 stays reliable over 14:22 time? Agents and retool log every step 14:25 they take. You can see what happened, 14:27 what tools were used, the inputs, 14:30 outputs, and the thought process at 14:32 every 14:34 stage. This shows us the past, which is 14:37 very handy for debugging and 14:39 understanding the system. But to iterate 14:42 with confidence and track quality over 14:44 time, we use eval. 14:49 We've set up a few eval on this agent to 14:52 check that it keeps working the way we 14:54 expect, especially when we make big 14:56 changes like adding tools, switching 14:59 models, or modifying the 15:02 prompt. You can run an eval against the 15:05 data set here. I'll use our sample 15:08 inputs data set. 15:10 Each row is scored based on a reviewer, 15:13 which could be something simple like an 15:16 exact string match or more complex like 15:19 using an LM as a judge scoring 15:22 system. Once you've run a few evals, you 15:25 can compare them. After a big change, I 15:28 might compare today's version against 15:30 last week's to see where the scores 15:32 improved or regressed. 15:36 This level of observability and control 15:39 lets you confidently deploy agents 15:41 across your systems without sacrificing 15:44 trust and 15:46 reliability. As you build more and more 15:49 agents, it becomes imperative to have 15:52 insight into their behavior at scale. 15:55 With the monitoring page, you have a 15:57 full real-time view of how your agents 16:00 interact with each other, the tools they 16:02 have access to, along with other metrics 16:05 you need to make sure things are staying 16:07 on 16:08 track. That was just one example of what 16:11 you can now build with agents. 16:14 Retool Agents allows you to combine 16:16 state-of-the-art AI patterns with the 16:19 customizability of Retool, giving you a 16:22 flexible, intelligent decision-m system 16:25 on top of the trusted platform you're 16:27 already using 16:28 today. The best way to understand the 16:31 power of agents is to build one. To get 16:35 started, grab a template or build one 16:38 from scratch today. 16:41 What we've demonstrated today is the 16:43 power of building on a true application 16:45 layer. Virtual agents is the missing 16:47 piece that transforms impressive large 16:49 language models into complex automations 16:52 and real business 16:54 impact. Just as AWS became the 16:57 infrastructure layer for cloud 16:59 computing, retool is becoming the 17:01 definitive app layer for AI. And you 17:05 don't have to take my word for it. 17:07 Here's what customers have to say. At 17:09 ClickUp, we fundamentally believe in 17:12 efficiency and getting more done faster. 17:16 And this is one of the reasons that we 17:19 invested so heavily in AI from the 17:21 beginning because we saw that it was 17:24 this force multiplier that could really 17:28 really uplevel our execution 17:30 capabilities uh both internally and for 17:32 our customers. One of the first use 17:34 cases that we built out for sales was 17:37 this inbound AI agent to uh intake, 17:42 evaluate, qualify, and route and 17:45 potentially even transact uh these kind 17:48 of inbound inquiries that we have. And 17:51 over time, that's saved us, you know, 17:53 hundreds of thousands of dollars in 17:55 terms of headcount costs or and just 17:59 speed to lead. uh as well as actually 18:02 made us revenue. you know, when you 18:03 build with AI, there's, you know, 18:07 actually often 18:08 times the actual AI component of 18:11 whatever you're building is quite small, 18:13 but the business context that needs to 18:15 go into those prompts, the structure of 18:18 the outputs, the, you know, taking those 18:22 outputs and putting them somewhere 18:23 useful or or kicking off other 18:26 automations or decisioning based on 18:28 that, all of that is is what it takes to 18:32 really get value out of an AI 18:34 application. And that's the thing that 18:36 retool gives us the scaffolding and the 18:39 framework to do. And so that's where we 18:41 we really see retool as our application 18:44 layer for for AI is it really is a full 18:48 partnership across all parts of of 18:50 building an application out uh versus 18:53 just you know feeding things to AI and 18:55 and just that little microcosm. Our 18:58 vision is clear. The tools are ready. 19:00 The platform is here and the future of 19:02 work is being reimagined today. It's how 19:06 using Retool, our customers have already 19:08 automated 100 million hours of work and 19:12 how together we'll reimagine what's 19:14 possible in the AI era. Can't wait to 19:17 see what you all build.
