Build the next Billion $ Agent 🚀

Key Concepts

• Cursor for X: Vertical agentic software designed to streamline end-to-end processes for specific knowledge work.
• Agentic Software: Software powered by AI agents that can complete specific tasks and workflows.
• Vercel AI SDK: An open-source package by Vercel for building AI-powered software, including backend and frontend components in TypeScript.
• AI SDK Core: Part of Vercel AI SDK for setting up large language models (LLMs), building agents, and connecting to tools and APIs.
• AI SDK UI: Part of Vercel AI SDK for efficiently streaming results (text, structured output) to the frontend.
• Stream Text/Object: Functions in AI SDK that allow streaming text or structured data (JSON) from LLMs.
• Tools: Functions or APIs that an AI agent can use to perform specific tasks (e.g., weather lookup, PRD generation).
• Two Invocations: Represent the calls to external tools or functions made by the AI agent.
• Playground/Canvas: A specialized UI where users can review, track progress, and collaborate with the AI agent.
• State: Short-term memory shared across different UI components, enabling real-time updates and interaction.

Building a Cursor for X: An Agentic Software Approach

1. Introduction to Vertical Agentic Software

• The video focuses on building "Cursor for X," which refers to creating specialized AI agents tailored for specific verticals (e.g., writing, video editing, PRD planning).
• The core idea is that vertical agentic software will be a significant area for value capture in the AI space, following the initial success of AI coding tools.
• Examples of potential "Cursor for X" applications include:
  • Cursor for Slice
  • Cursor for Spreadsheets
  • Cursor for Design
  • Cursor for Video Editing

2. Core Components of a Cursor for X System

• Agent with Tools and Assistance: The AI agent needs access to various tools and APIs to complete valuable tasks.
  • Example: Cursor can write code, create files, run command-line commands, perform Git operations, and search documentation.
• Specialized Playground/Canvas: A UI where users can:
  • Review the agent's work.
  • Track progress.
  • Collaborate with the agent.
  • Example: In Cursor, the code editor serves as the playground.
• Contextual Awareness: The agent should have access to the context of what the user is seeing in the playground.
  • Example: Cursor automatically brings the context from the code editor into the agent chat.

3. Building the Agentic System with Vercel AI SDK

• Vercel AI SDK Overview:
  • An open-source package for building AI-powered software in TypeScript.
  • Used by companies like Perplexity and Vzero.
  • Consists of two main parts: AI SDK Core and AI SDK UI.
• AI SDK Core:
  • Allows connecting to different LLM providers (e.g., Anthropic, OpenAI).
  • Provides functions for:
    • generateText: Generates text from a prompt.
    • generateObject: Generates structured output (JSON) based on a schema.
    • streamText: Streams text from a prompt.
    • streamObject: Streams structured output (JSON) based on a schema.
    • Tool calling: Enables the agent to use external tools.
• AI SDK UI:
  • Enables streaming results to the frontend efficiently.
  • Provides components for building chat interfaces.

4. Step-by-Step Implementation with AI SDK Core

• Setting up a TypeScript Project:
  • Initialize a TypeScript project using npm init.
  • Install the AI SDK: npm install ai.
  • Install the desired LLM provider package (e.g., @ai-sdk/anthropic).
• Generating Text:
  • Import the necessary functions from the AI SDK.
  • Define the model (e.g., Anthropic).
  • Use the generateText function to get a text response from the model.
• Streaming Text:
  • Use the streamText function instead of generateText.
  • Iterate through the resulting text stream to print the output in real-time.
• Generating Structured Output (JSON):
  • Install Zod for schema definition: npm install zod.
  • Define a Zod schema for the desired JSON structure.
  • Use the streamObject function, passing the schema as a property.
• Building an Agent with Tools:
  • Define tools with descriptions, parameters, and execution functions.
  • Pass the tools to the streamText function.
  • Set the maxSteps property to enable agentic behavior (the agent will run in a loop, calling tools as needed).

5. Building a Web Application with AI SDK UI

• Setting up a Next.js Project:
  • Create a Next.js project using create-next-app.
  • Install a UI library (e.g., Shadcn UI).
• Creating an API Endpoint:
  • Create an API endpoint in the pages/api directory (e.g., pages/api/chat/route.ts).
  • Use the streamText function in the API endpoint to create the agent backend.
  • Return a stream of data from the API endpoint.
• Building the Frontend:
  • Use the useChat hook from @ai-sdk/react to connect to the API endpoint.
  • The useChat hook returns:
    • messages: An array of messages in the chat history.
    • input: The current value of the input field.
    • setInput: A function to update the input field.
    • append: A function to send a new message to the agent.
  • Render the chat interface using the data from the useChat hook.
• Rendering Tool Invocations:
  • The messages array contains information about tool invocations (e.g., tool name, arguments, results).
  • Use this information to display the tool invocations in the UI.

6. Streaming Results from Nested Tool Calls

• Problem: Vercel AI SDK doesn't natively support streaming results from tools that call other LLMs.
• Solution:
  • Use the createDataStreamResponse function from Vercel AI SDK.
  • This function allows merging multiple streams together and pushing custom data to the frontend.
  • Create a function that calls the nested LLM and streams the results.
  • Use the dataStreamWriter to send new data to the frontend.
  • Merge the original stream with the new stream using result.mergeIntoDataStream.
  • Access the custom data on the frontend using the data property of the result.

7. Building the Playground/Canvas

• Requirements:
  • A UI component for the playground (e.g., a text editor).
  • A way for the agent chat to communicate data back to the playground.
  • A state management system to share data between the chat interface and the playground.
• Implementation:
  • Restructure the page into different components (e.g., chat panel, content panel).
  • Create a shared state using useState to store the data.
  • Pass the state and the setState function to the chat panel and the content panel.
  • In the chat panel, update the state when new data is received from the agent.
  • In the content panel, display the data from the state.
• Backend Integration:
  • Connect to a backend database (e.g., Supabase) to persist the data.

8. Conclusion

• The video provides a comprehensive guide to building "Cursor for X" applications using Vercel AI SDK.
• It covers the core components of such a system, including the agent, the playground, and the communication between them.
• It also provides step-by-step instructions on how to implement these components using Vercel AI SDK.
• The video emphasizes the importance of streaming results to the frontend for a better user experience.
• The video also mentions a 30-day challenge in the AI Builder Club to build a Cursor for X application, with weekly guidance and a GitHub repository.
• A new version of the SAS launch kit with built-in authentication and usage-based pricing is also mentioned.