I Built an AI Organization With AI Employees... It Actually Worked

[00:00:00]Instead of giving you another chatbot to talk to,  it allows you to create multiple AI employees, assign them roles, and let them collaborate  together to complete real work. So, let's see what happens when we build  an AI company from scratch. [music] When I first open Clawith, one thing immediately  stood out. The platform introduces itself as Open Clawith for teams，（now "Reinvent Your  Organization for the AI Era"）, wihich already tells you where its focus is. This isn't a system  built around individual chats or isolated prompts.

[00:00:38]It's designed around collaboration between  AI employees working together inside a shared environment. What makes this even more interesting  is that the project openly connects to GitHub, allowing anyone to explore how the system  works behind the scenes. That transparency fits perfectly with the idea of building  customizable AI organizations rather than relying on a closed blackbox solution.  [music] I'll include the GitHub link in the description and pinned comment if you'd like  to explore it yourself. For this demonstration, I'm [music] going to build a small AI powered  design company. Instead of hiring employees, I'll create digital workers that can handle  different responsibilities inside the business.

[00:01:18]The goal is simple. find design contests, generate  logo concepts, and prepare everything needed for submission. Normally, this process would require  a researcher, a designer, and an administrative assistant. With Clawith, we're going to recreate  those positions using [music] AI employees. This is exactly the type of workflow that shows how a  single person or a small team can get started and scale the system across an entire organization.  And as your company grows, it can seamlessly scale across the entire enterprise. Once you log in,  you don't land inside a typical chat interface like most AI products. Instead, claw with asks  you to create or join a company workspace.

[00:01:59]That small difference immediately changes the  way you think about the platform. You know, rather than interacting with a single assistant,  you're entering an environment designed to manage teams. [music] After creating the workspace, the  dashboard is ready for me to begin building my digital workforce. Everything feels structured  around company operations rather than simple conversations, reinforcing the idea that these  agents are meant to function as employees rather than chatbots. Before creating any employees, I  head into the company setting section. Here I can define the company name, time zone, and [music] a  shared introduction that provides context for all future agents. There's also a shared knowledge  area where files and information can be stored, creating long-term memory that can be shared  across teams and the entire organization. Now it's time to build the actual workforce. Instead  of creating a single assistant, I'm creating three employees that will work together as a team.  The first employee is a market researcher whose responsibility is identifying design contests and  evaluating which opportunities have the highest winning potential. [music] The second employee  is a graphic designer who will generate logo concepts based on contest requirements. [music]  The third employee is a submission secretary who will handle organization, file preparation, and  final submissions. At this point, we're no longer building a chatbot. We're building departments  inside a company. Each employee receives different skills and responsibilities based on their role.  The researcher focuses on gathering information and evaluating opportunities. The designer  specializes in creative generation and concept development. The secretary handles organization  and execution tasks. After assigning the appropriate capabilities, I configure permissions  so the employees can collaborate across the company environment. Different employees  can also have different permission levels, making the workflow suitable for organizations  of any size. I also review available integrations that allow the agents to connect with external  tools and communication platforms. This is where Clawith begins to feel much closer to a real  business infrastructure than a traditional AI application. With the team ready, it's time to  put them to work. I asked the market researcher to search for active logo design contests that  offer strong prize potential while maintaining reasonable competition levels. The researcher  begins collecting opportunities and analyzing requirements before selecting the most promising  contest. Once a contest has been chosen, the brief is automatically forwarded to the graphic  designer. The designer reviews the requirements and generates multiple logo concepts that fit the  contest criteria. After selecting a final design, all files and supporting information are passed to  the submission secretary who prepares everything for the next stage of the process. This is where  the platform [music] starts to feel fundamentally different from most AI tools. Normally, you  would manually switch between multiple chats, copy information from one place to another,  and coordinate every step yourself. Here, the employees communicate through the  workflow. One employee completes a task, another receives the output, and the process  continues automatically. Instead of managing prompts, you're managing a team. The focus  shifts away from individual conversations and towards [music] structured collaboration, which  is exactly what Cloth is designed to enable.

[00:05:16]Throughout the process, humans remain responsible  for the final decisions and approvals while the AI employees handle the execution. What we've built  here is only a small example of what's possible.

[00:05:27]The same concept could be applied to marketing  agencies, research organizations, customer support departments, content creation teams,  or even trading operations. Imagine analysts gathering market information, risk managers  evaluating opportunities, committee members reviewing recommendations, and traders executing  decisions inside a connected workflow. As more employees are added and more responsibilities  are distributed, the system becomes increasingly capable of handling larger and more sophisticated  projects with minimal human supervision. If you'd like to explore Clawith, you'll find all the  links in the description and pinned comment below.

[00:06:06]It's an interesting opportunity to experiment  with building your own AI powered company and discover how far collaborative AI employees can  take [music] a workflow. And before we finish, there's one more thing worth mentioning.  Case running a full design to submission cycle [music] without manual handoffs. A small  design operation needed to find logo contests, create designs matching each brief, and prepare  final submissions. The hard part wasn't the work itself. It was the constant manual handoff between  steps. With cloth, three role-based agents were set up. one to research and evaluate contests,  one to generate logo concepts from each brief, and one to prepare and submit final files. Output  from each agent flows automatically to the next.

[00:06:54]No copy pasting, no switching between chats.  Result: One connected team running the process start to finish instead of a person managing  multiple disconnected conversations. Ace a project status without manual follow-up project  leads often lose hours chasing team members for status updates leaving the real picture always  a step behind. With Clawith, a coordinator agent breaks down and assigns tasks. Execution agents  update progress as work completes and a tracking agent consolidates everything into one live  view. Updates surface automatically instead of being chased down. Result, an always current  project status with zero manual check-ins. Case B, coordinating parallel deadlines across a team.  When multiple people or agents work on different parts of a [music] project at once, a delay in  one area can silently break the whole timeline.

[00:07:49]With Clawith, agents share a common workspace  and knowledge base. So when one part of the work shifts, connected agents are notified instantly  and adjust [music] accordingly. No single person manually recocoordinating everyone. Result, high  efficiency collaboration across interdependent, time-sensitive work streams. Organizational  collaboration in the AI era isn't about people [music] juggling separate tools anymore.  It's about agents with shared roles, memory, and communication working together with minimal  human oversight. Cloth is built for exactly that future. Not a tool that replaces one task, but  the infrastructure for a new kind of organization, AI native, where a human team leads a team of  agents. If you enjoyed this video and found the concept useful, make sure to like and subscribe.  Thanks for watching and I'll see you in the next