About this Video

• Video Title: How to Build Your Entire AI Workforce in One Afternoon (Live Demo)
• Channel: Greg Isenberg
• Speakers: Greg Isenberg, Flo
• Duration: 00:29:33

Introduction

This video demonstrates Lindy AI's new capabilities, focusing on its agent builder and computer use features. The speakers showcase how users can create AI agents for various tasks, including customer service, sales, and LinkedIn outreach, using plain English instructions. The video aims to illustrate how this technology allows for the creation of fully autonomous companies run by AI agents.

Key Takeaways

• Agent Builder and Computer Use: Lindy AI's new agent builder simplifies the process of creating complex AI agents using natural language. Computer use allows these agents to interact with various computer applications.
• AI Agents for Different Roles: The video demonstrates the creation of AI agents for customer service (automating tasks like checking order status and issuing refunds), sales (supplementing human SDRs with multi-channel outreach), and LinkedIn outreach (automating personalized direct messages).
• Iterative Development: Building effective AI agents involves iterative refinement. The video highlights the importance of starting with simple tasks, observing agent performance, and adjusting instructions as needed.
• Autonomous Companies: The technology showcased suggests the potential for creating fully autonomous companies where AI agents handle most operational tasks, allowing human founders to focus on strategy and growth.
• Human-in-the-Loop: While powerful, the AI agents still require some human oversight, particularly for subjective tasks or those requiring common sense judgment. The human acts as an editor and improves agent performance over time.

The video demonstrates creating a LinkedIn outreach agent by using Lindy AI's agent builder. The process begins with a conversational prompt instructing the agent to send personalized DMs to LinkedIn profiles provided. The agent is instructed to tailor the DM to the individual's profile. The agent uses the "computer use" feature to directly interact with the LinkedIn interface, send the DM, and handle potential issues (such as the user having DMs disabled). The process isn't instantaneous; the agent's actions are shown live, demonstrating the need for user observation and iterative refinement of instructions within the agent builder to optimize its performance.

There isn't a single, perfectly defined "prompt chain" presented as a cohesive, numbered list. The instructions to the AI agent are given conversationally, and across multiple segments of the video. However, here's a compilation of the relevant phrases that constitute the instructions given to build the LinkedIn DM outreach agent:

First, the initial instruction sets the core function:

"When I send you a message with a LinkedIn profile URL, I want you to send them a DM on LinkedIn asking if they would be interested in AI agent services. Tailor your DM to the person's profile."

Then, further clarification and refinement:

"And I'm going to be, I want you to use computer use to send a DM on LinkedIn asking if they would be interested in the agent sources."

Later, as the agent is being built, further specifications are made: The agent's code shows the condition that triggers the DM ("If the message contains a LinkedIn profile URL") and the agent's action ("I'm going to start a computer session. And then you are helping to send a personalized LinkedIn DM to a prospect about AI agent services").

Therefore, the complete "prompt chain" is not explicitly presented as a formal sequence, but rather emerges conversationally throughout the agent's creation.

The two major features of Lindy AI highlighted are the Agent Builder and Computer Use.

• Agent Builder: This feature allows users to create AI agents using plain English instructions. Instead of coding, users describe the desired actions and workflows for the agent.

• Computer Use: This feature enables the AI agents created by the Agent Builder to interact directly with various computer applications and websites. This extends the capabilities of the agents beyond simple text-based responses, enabling them to perform tasks within different software environments.

The video demonstrates Lindy AI's ability to interact with and utilize computer applications, implying it can generate code implicitly to achieve a desired task within those applications (e.g., sending a LinkedIn message). However, the video does not explicitly show Lindy AI generating standalone code independent of such application interaction. Therefore, based solely on the provided transcript, we cannot definitively conclude whether Lindy AI can build code as a standalone function.