## Background
This video was published on YouTube by Ai4 on August 24, 2025, featuring Emir Karabeg, Co-Founder and CEO of Sim Studio. According to the page description, the talk covers real-world enterprise applications of AI agents, sharing best practices for deployment, security considerations, and ROI based on insights from over 50,000 agents running on Sim.
## TL;DR
Emir Karabeg addresses the common pitfalls enterprises face when attempting to build and deploy AI agents, where projects often stall out or end in underwhelming proofs-of-concept. He introduces a mental framework for evaluating agentic workflows before committing engineering resources. Drawing from Sim Studio's experience scaling to over 7,000 GitHub stars and supporting 50,000 agents across diverse organizations like the Department of Defense and startups, he offers a blueprint for successful enterprise AI deployment.
## Atmosphere & style
The presentation carries an authoritative yet pragmatic tone, aimed at dissecting corporate inefficiencies with clear analytical insight. Karabeg's dynamic narrative style transforms typical enterprise software hurdles into a compelling diagnostic walkthrough, grounding high-level strategy in real-world deployment data. Watching the session feels direct, educational, and grounded in practical experience rather than hype.
## Key takeaways
- [00:04](https://www.youtube.com/watch?v=P6QBg05fS3s&t=4s) The typical process of building AI agents in an enterprise involves pitch decks, prototyping, and long waiting periods that often result in a quiet abandonment or forgotten project.
- [00:04](https://www.youtube.com/watch?v=P6QBg05fS3s&t=4s) Emir Karabeg, Co-Founder and CEO of Sim Studio, aims to provide a mental framework for evaluating agent projects before launching an enterprise initiative.
- [00:04](https://www.youtube.com/watch?v=P6QBg05fS3s&t=4s) Sim Studio's open-source platform sim.ai grew from 50 GitHub stars to over 7,000 within four months of its launch.
- [00:04](https://www.youtube.com/watch?v=P6QBg05fS3s&t=4s) Over 50,000 AI agents have been built on Sim Studio's platform to power critical workflows.
- [00:04](https://www.youtube.com/watch?v=P6QBg05fS3s&t=4s) Production users of Sim Studio range from major government institutions like the Department of Defense to lean, scaling startups.
## Detailed walkthrough
### [00:04](https://www.youtube.com/watch?v=P6QBg05fS3s&t=4s) The Enterprise Agent Building Pitfall and Sim's Background
Inside a typical enterprise today, the agent building process follows a predictable, often disappointing path. It usually starts when someone spots a promising workflow—either an internal process to automate or a customer-facing workflow to streamline using AI agents. A business case is put together with a presentation deck to showcase the vision and secure stakeholder buy-in. An engineering team kicks off the build and prototypes an agentic system, leading to a long period of waiting. Months later, one of two things happens: the project delivers a proof of concept (PoC) that generates little excitement and gets quietly buried, or executives are left wondering what happened to the ambitious agentic system promised months ago.
Emir Karabeg, Co-Founder and CEO of Sim Studio (sim.ai), introduces### The Enterprise Agent Pitfall & What Sim.ai Has Built
The standard process for deploying AI agents within a typical enterprise follows a predictable, often disappointing path. It begins when someone spots a promising internal or customer-facing workflow to automate. A business case is assembled—complete with a slide deck to win over stakeholders—and an engineering team is tasked with building a prototype. Then comes a period of waiting, which usually ends in one of two ways: months later, the team delivers a disappointing proof-of-concept (PoC) that fails to generate excitement and quietly fades away, or leadership is left wondering what happened to the ambitious agentic system promised months prior. The goal here is to provide a mental framework for evaluating agent projects *before* assembling a team or launching an enterprise initiative.
Sim Studio co-founder and CEO Emir Karabeg shares the rapid growth and real-world deployment scale behind their open-source framework:
* **Growth Metrics**: Launched `sim.ai` seven months prior; grew from 50 GitHub stars to over 7,000 within four months.
* **Scale of Deployment**: Over 50,000 AI agents have been built on the platform.
* **Production Users**: Ranging from major institutions like the Department of Defense down to lean, scaling startups.
---
## Notable quotes
> AI agents are revolutionizing the way enterprises automate complex workflows—but are you harnessing their full potential? — Emir Karabeg
> Over 50,000 AI agents are already running on Sim, powering critical workflows at the Department of Defense, Epiq Global, Mobile Health, and other major enterprises. — Emir Karabeg
> [00:04](https://www.youtube.com/watch?v=P6QBg05fS3s&t=4s) Inside a typical enterprise today, the agent building process follows a predictable, often disappointing path. — Emir Karabeg
> [00:04](https://www.youtube.com/watch?v=P6QBg05fS3s&t=4s) A business case is put together with a presentation deck to showcase the vision and secure stakeholder buy-in. — Emir Karabeg
> [00:04](https://www.youtube.com/watch?v=P6QBg05fS3s&t=4s) Months later, one of two things happens: the project delivers a proof of concept (PoC) that generates little excitement and gets quietly buried, or executives are left wondering what happened to the ambitious agentic system promised months ago. — Emir Karabeg
> [00:04](https://www.youtube.com/watch?v=P6QBg05fS3s&t=4s) The goal here is to provide a mental framework for evaluating agent projects before assembling a team or launching an enterprise initiative. — Emir Karabeg
## Mentioned
Emir Karabeg, Sim Studio, Sim, sim.ai, Ai4, Department of Defense, Epiq Global, Mobile Health, GitHub
## Video description
Emir Karabeg, Co-Founder / CEO, Sim Studio
Abstracts
AI agents are revolutionizing the way enterprises automate complex workflows—but are you harnessing their full potential? In this session, I'll explore real-world applications of AI agents within leading organizations, highlighting how they streamline operations, enhance productivity, and unlock new business opportunities. Over 50,000 AI agents are already running on Sim, powering critical workflows at the Department of Defense, Epiq Global, Mobile Health, and other major enterprises. I'll demonstrate best practices for deploying agents effectively, navigating security considerations, and maximizing ROI. You'll walk away with actionable insights on integrating AI agents seamlessly into your existing infrastructure. Join me to discover how AI agents can transform your enterprise strategy today.
Sim.ai CEO称缺乏恰当评估会导致企业级AI Agent失败
企业级AI Agent项目之所以沦为失败的概念验证，是因为团队在建立明确的评估框架之前就启动了开发。
Emir Karabeg剖析了企业在尝试构建和部署AI Agent时常面临的误区，即项目往往中途搁浅或以差强人意的概念验证草草收尾。他提出了一套思维框架，建议在投入工程资源前先对Agent工作流进行评估。结合Sim Studio在GitHub突破7000星标、并在美国国防部及各类初创企业等组织中支持了5万个Agent的实际经验，他为企业成功落地AI部署提供了蓝图参考。
企业构建 AI Agent 的典型流程往往陷入困境：从识别工作流、制作幻灯片争取支持，到工程团队研发原型，数月后交付的概念验证（PoC）往往反响平平并被悄然放弃，或项目直接不了了之。 (0:04)
Sim Studio 联合创始人兼 CEO Emir Karabeg 提出了一个在组建团队或启动企业项目前评估 Agent 项目的思维框架。 (0:04)
Sim Studio 的开源平台 sim.ai 发布 7 个月，在上线后的 4 个月内 GitHub Star 数便从 50 飙升至超过 7,000。 (0:04)
目前已有超过 50,000 个 AI Agent 在 Sim 平台上构建，生产环境用户既包括美国国防部等大型政府机构，也涵盖各类精益型初创公司。 (0:04)
Reading summary
Emir Karabeg, Co-Founder and CEO of Sim Studio, argues that enterprise AI agent initiatives routinely stall after months of prototyping and proposes evaluating projects before committing engineering resources.
The Predictable Failure of Enterprise Agent Prototypes (0:04)
According to Karabeg, enterprise AI agent projects typically follow a flawed trajectory. The process begins when someone identifies an internal process or a customer-facing workflow to automate, builds a business case with a slide deck to secure stakeholder buy-in, and assigns an engineering team to build a prototype.
After months of waiting, Karabeg notes that these initiatives usually end in one of two disappointing outcomes: either the engineering team delivers an underwhelming proof of concept (PoC) that fails to generate excitement and is quietly abandoned, or the project stalls so completely that leadership is left wondering what happened to the promised system.
Upfront Evaluation and Sim Studio's Scale (0:04)
To avoid these stalled prototypes, Karabeg advocates using a mental framework to evaluate agent projects before assembling a team or launching an enterprise initiative. He draws on observations from Sim Studio (sim.ai), an open-source framework his company launched seven months prior.
According to Karabeg, the platform grew from 50 GitHub stars to more than 7,000 within four months of launch. He reports that over 50,000 AI agents have been built on the framework, with production users ranging from lean, scaling startups to major institutions such as the Department of Defense.
Emir Karabeg: AI agents are revolutionizing the way enterprises automate complex workflows—but are you harnessing their full potential?
Emir Karabeg: Over 50,000 AI agents are already running on Sim, powering critical workflows at the Department of Defense, Epiq Global, Mobile Health, and other major enterprises.
[0:04] Emir Karabeg: Inside a typical enterprise today, the agent building process follows a predictable, often disappointing path.
[0:04] Emir Karabeg: A business case is put together with a presentation deck to showcase the vision and secure stakeholder buy-in.
[0:04] Emir Karabeg: Months later, one of two things happens: the project delivers a proof of concept (PoC) that generates little excitement and gets quietly buried, or executives are left wondering what happened to the ambitious agentic system promised months ago.
[0:04] Emir Karabeg: The goal here is to provide a mental framework for evaluating agent projects before assembling a team or launching an enterprise initiative.
Original transcript
[0:04] Let me describe what an agent building
[0:06] process looks like inside of your
[0:09] organization today or inside of a
[0:10] typical enterprise. First, um you spot a
[0:14] promising workflow. Either this is an
[0:16] internal process you're looking to
[0:17] automate with AI agents or it's a
[0:20] customerf facing workflow that you
[0:21] believe you can automate and streamline
[0:23] with AI agents.
[0:25] Then you build a business case. This
[0:28] typically involves crafting some kind of
[0:29] deck, showcasing the vision, and then
[0:32] winning stakeholder interest.
[0:34] You kick off the build. So, you have an
[0:36] engineering team that prototypes some
[0:38] agent system. And then you wait and
[0:41] wonder. Uh, and either one of two things
[0:44] happens. Um, months later, you deliver
[0:47] some P that doesn't really deliver much
[0:50] excitement. Um, and then it's buried and
[0:52] it kind of fades. um or you're left
[0:55] wondering where did those presentations
[0:57] go months ago where we kind of promised
[0:59] this agentic system uh and now we don't
[1:02] have it. And so by the end of this
[1:04] 10-minute talk, I'm going to leave here
[1:07] um with you all an agent framework and
[1:09] not ones that you are going to build
[1:12] your agents on top of, but actually how
[1:13] you should think uh about building
[1:15] agents before you actually put together
[1:17] a team or start an initiative um to
[1:20] build an agentic system either customerf
[1:22] facing or for your own internal
[1:24] workflows. Um first of all, who am I?
[1:27] Why should you care about my talk? Um
[1:29] well 7 months ago uh we launched sim.ai.
[1:34] Um and four months ago uh we pretty much
[1:37] had 50 GitHub stars and today that's now
[1:40] over 7,000. Um over 50,000 AI agents
[1:44] have been built on our platform. Uh and
[1:47] they've been deployed in production from
[1:49] large enterprises and organizations like
[1:51] the Department of Defense all the way
[1:53] down to small and lean and scaling uh
[1:56] startups. Um here is a graph of our
[2:01] open-source um repository. So you can
[2:04] see here that things have been going
[2:06] well. Um and before we get started, I
[2:08] just want a really really brief uh
[2:11] introduction into what is an AI agent.
[2:13] I'm sure you guys have kind of all
[2:14] talked about it, but I just want to
[2:15] solidify the terminology here before we
[2:17] kind of continue on with the rest of the
[2:18] presentation. An AI agent is just an LLM
[2:22] call, which is OpenAI, Anthropic, Gemini
[2:25] augmented with capabilities. So tool
[2:27] calling uh and memory. And a workflow is
[2:30] just a series of AI agents that are
[2:32] either running in parallel or
[2:34] sequentially. Now to make it even more
[2:37] confusing, AI agents and workflows are
[2:39] kind of interchangeable um as we use
[2:41] them in the industry. Um but you can
[2:43] think of a workflow as a series of AI
[2:45] agents and perhaps an AI agent is also a
[2:49] task manager for sub AI agents. So you
[2:52] can all get confused, but I guess this
[2:53] is the official terminology here. So, if
[2:55] you hear me say AI agent or workflow,
[2:57] they're sort of interchangeable. Um, but
[3:00] the official definitions um I've laid
[3:02] out here. Uh, disclaimer, I'm going to
[3:05] talk about AI agents and generally what
[3:07] works in the enterprise and what
[3:09] doesn't. We've seen a lot of cases. Um,
[3:11] but just one disclaimer is that there's
[3:13] no universal agent, at least today. Uh,
[3:16] we haven't found AGI. So, there's no
[3:18] agent that you can just point at your
[3:19] application and say, "Fix this problem
[3:21] for me." Uh, hopefully that's coming
[3:23] soon. Um, but I'm just going to outline
[3:25] a couple uh ways to think about how to
[3:28] build agents.
[3:30] Um, so there's a three-part framework
[3:32] I'm going to talk about today. And step
[3:34] one is that the data comes first. Um, an
[3:38] AI agent is only as good as the data it
[3:41] can reach. Um, and either this can be in
[3:44] the form of an API or an MCP um that
[3:47] you're accessing externally or
[3:49] internally.
[3:51] Um, I'm going to put this little note
[3:53] here that browser agents or computer use
[3:56] agents might work. This is a topic in
[3:58] the industry that a lot of people are
[4:00] talking about today and they're excited
[4:01] about the prospects of browser use or
[4:03] computer use agents. Just to give an
[4:06] example of when you might use this
[4:08] versus an API or MCP, perhaps you're
[4:10] accessing some internal software that is
[4:13] not able um to give data through some
[4:16] API or some MCP. Uh, and so that way
[4:19] you're going to need to access it
[4:20] through a browser agent or a computer
[4:21] use agent. I'm going to have a next
[4:23] slide that talks about the reliability
[4:25] of these um agent workflows. Um, but the
[4:28] last point on the slide is that if
[4:29] there's no programmatic access to your
[4:31] data, your agent will fail before it
[4:34] starts. So before you start and pitch
[4:36] the business uh use case to the
[4:38] stakeholders who are going to use your
[4:40] agentic workflow, uh make sure that you
[4:42] understand what data you actually have
[4:44] access to. uh and that should be your
[4:46] grounding point before you go and you
[4:48] build your agentic system. Um so here's
[4:51] the chart I talked about. Um so this is
[4:55] uh a chart of different browser use
[4:57] agents um that we use uh in the industry
[5:00] today. So some of the leading
[5:02] state-of-the-art browser use agents. Uh
[5:04] the chart shows two different eval
[5:06] frameworks um for evaluating the
[5:09] reliability of actually accessing data.
[5:11] So again, this is if you have a software
[5:14] or some uh internal tool that you're
[5:16] using that cannot expose data through an
[5:18] API. Um you might use something like
[5:21] this, but just beware um that the
[5:23] reliability or at least the ability to
[5:26] access information in that platform
[5:28] might range anywhere between 60 to 90%
[5:31] in the best cases. Um so uh just beware
[5:35] before you move forward with these
[5:37] solutions. Again, it's definitely
[5:38] possible. Um, but just understand that
[5:40] there are definitely limitations with
[5:42] these types of solutions. Um, step two
[5:45] is your instruction manual. Um, I'm sure
[5:47] you've heard a people talk about agents
[5:50] a lot today, so I'll kind of just run
[5:52] through this briefly. Um, but you want
[5:53] to start with simple and repeatable
[5:55] tasks. Um, you want to create
[5:57] unambiguous instructions that the agent
[6:00] can follow. So again, don't include any
[6:02] types of uh, you don't want the agent to
[6:04] um, try to reason through any nuances in
[6:06] your application. You want to be very
[6:08] explicit with each task that you give
[6:10] each of your sub agents. And the best
[6:13] way to do this um inside of your company
[6:15] is if you have um process documentation
[6:18] that you can then take and then break it
[6:20] down into system prompts that you'll
[6:22] then give into sub agents. So I have
[6:25] here a couple of examples. Um this is an
[6:29] agent that's going to extract out
[6:30] information from an invoice uh and then
[6:33] log them in an ERP. Um, so you can see
[6:36] here that this is a very clear-cut
[6:38] explicit uh task for an agent to then go
[6:42] and do um versus a bad example which is
[6:45] grab the invoice data. Um, and this is
[6:48] an example of some system prompt or
[6:50] perhaps um some process documentation
[6:52] that you then documentation that you
[6:54] then convert into a system prompt. Um,
[6:56] and you can see here that the second
[6:57] example the agent might return wildly
[6:59] different responses. So you definitely
[7:01] want to make sure that you're explicit
[7:03] um in your instruction manual. Okay. So
[7:05] we have the data. Now we have the steps
[7:08] on that the agent is going to take when
[7:10] it goes and actually executes that task
[7:12] in your production environment. The
[7:14] third step is the final step is actually
[7:16] taking action. Um and so you want to
[7:19] make sure that you're first validating
[7:21] your agentic results in a safe um and a
[7:25] test first uh environment. So you want
[7:28] to version each of your workflow changes
[7:31] and preserve a record of outputs. This
[7:33] is crucial uh in the beginning stages of
[7:36] developing an agent framework. The
[7:38] reason is because um you might
[7:40] originally have some uh idea and you
[7:44] build your agent workflow accordingly.
[7:47] Um, and then you want to maintain one
[7:50] spreadsheet with the different uh inputs
[7:52] and outputs of your first aentic
[7:55] workflow. And then you want to modify
[7:58] your aentic workflow and um use um
[8:02] separate sheets every single time you
[8:04] modify um your workflow.
[8:08] Um so this is comparing different
[8:10] versions to track progress and refine
[8:12] your performance. Um so again this is
[8:14] crucial in the beginning stages of
[8:15] agentic development. Often time what
[8:17] happens is we have an idea for an agent
[8:20] that we want to put into our production
[8:21] system. We go and we build it has some
[8:24] good ideas but then we come back and we
[8:26] say okay actually let's refine a couple
[8:28] things around the edges perhaps adjust
[8:29] the system prompt in this way. Um but
[8:32] because we didn't version our workflow
[8:34] we're now getting outputs that might be
[8:36] better on certain tasks and worse on the
[8:39] tasks that we were performing better on
[8:40] initially. Um, so this is why it's
[8:42] definitely crucial uh to compare
[8:44] versions. And a really really easy way
[8:46] to do that is with that idea here where
[8:48] you just have one spreadsheet, you have
[8:50] input and you have output data and then
[8:51] you have different versions of that
[8:53] spreadsheet where you can then track,
[8:55] okay, this version of the workflow did
[8:57] well on these tasks and then my new
[8:59] version of the workflow does better on
[9:01] some other tasks. Um, for the final
[9:03] segment of this talk, I'm just going to
[9:05] talk about high ROI versus low ROI
[9:08] agents. So just giving you a general
[9:09] framework to understand which agents are
[9:11] actually capable in production uh and
[9:14] which ones have um low reliability or
[9:17] perhaps are probably not good ideas to
[9:19] pitch in front of people before you
[9:21] start building them. Um one is
[9:24] multimodal intelligence. So AI agents
[9:26] are especially good at processing mixed
[9:30] uh qualitative and quantitative data and
[9:33] then producing insights, summaries and
[9:36] action plans. um because of recent
[9:38] advancements in multimodal intelligence
[9:41] um starting you know with open AI and
[9:43] then moving on to uh Gemini and and now
[9:45] anthropic um mixed qualitative and
[9:48] quantitative data um and also uh
[9:51] multimodal uh inputs um AI agents are
[9:55] especially good at developing uh
[9:57] insights and reasoning um through each
[9:59] of these. So this can be something in a
[10:01] production environment like processing
[10:03] PDFs, audio sources, um documents, maybe
[10:06] even emails. Um so having all of this
[10:09] inside of one agent system and then
[10:11] developing some insights from that,
[10:12] they're really capable. Um deep
[10:14] research. Um so identifying what's
[10:17] signal and what's noise if you have lots
[10:19] of data. Um so doing things like um
[10:22] competitive analysis um market research.
[10:26] These are great examples of HAR uh high
[10:29] sorry ROI uh workflows. Um and then this
[10:32] can be used both internally and
[10:34] externally. So if you have internal
[10:35] documents that you need to reason
[10:36] through or external um you know uh data
[10:39] sources that you're looking to search
[10:41] through um AI agents will excel uh in
[10:44] both of those domains. And here's an
[10:47] example just briefly of a low ROI
[10:49] workflow. Um, which is a technical uh
[10:53] visual content interpretation. Um, so if
[10:56] you have things like complex diagrams or
[10:59] blueprints, this typically will involve
[11:01] uh human context. Uh, and so you would
[11:04] should generally try and stay away from
[11:06] these types of workflows. Um, but the
[11:09] first ones I mentioned are definitely
[11:10] good examples. There's a million other
[11:12] examples. So I just kind of wanted to
[11:13] give a brief overview of the things that
[11:15] you should think about before actually
[11:16] going and developing a system here. Um
[11:19] so quickly just to recap, you should
[11:21] start with your data, understand what
[11:22] you have access to. You should turn your
[11:24] process documents into system prompts.
[11:27] Um and start with simple and repeatable
[11:29] steps. And finally version your agent
[11:31] system in prototyping and production um
[11:34] so that you can iterate on your agentic
[11:37] workflow and understand which versions
[11:39] um perform best on certain tasks. uh and
[11:42] then come out with your final agentic
[11:44] system. Thank you all. Uh you can find
[11:46] us at sim.ai.
[11:48] Um and so we'll also be outside if you'd
[11:50] like to chat with us. Thanks so much.
