The Future of AI and SaaS is Agentic Experiences (Here's How to Build Them)

Key Concepts

• Agentic Experiences: Embedding AI agents into applications to create a natural and integrated product experience, rather than building standalone AI agent platforms.
• AGUI (Agentic UI): A protocol that standardizes how front-end applications connect to AI agents, enabling seamless communication and simplifying the development of agentic experiences.
• Copilot Kit: A React library that facilitates building user-facing agentic applications and integrates natively with AGUI.
• Pydantic AI: An AI agent framework that has a direct, first-party integration with Copilot Kit through AGUI.
• State Sync: The synchronization of data and information between the front-end application and the back-end AI agent, allowing for real-time updates and collaboration.
• MCP (Model Context Protocol): A protocol that connects agents to tools, similar to how AGUI connects agents to applications.
• Vibe Coding MCP Server: An MCP server provided by the Copilot Kit team that allows AI coding assistants to access Copilot Kit and AGUI documentation and best practices.
• RAG (Retrieval-Augmented Generation): An AI approach that combines information retrieval from a knowledge base with generative models to provide more informed and context-aware responses.

Building Agentic Experiences with AGUI

The Shift Towards Agentic Experiences

• The future of AI lies in embedding agents into existing applications, creating a natural part of the product experience.
• Standalone AI agent platforms are likely to become irrelevant as general agents like ChatGPT become more powerful.
• Agentic experiences involve deeply integrating AI agents into products that deliver unique value.

Introducing AGUI

• AGUI standardizes how front-end applications connect to AI agents.
• It acts as a middleman, providing a standard of communication between the front-end and the AI agent.
• AGUI enables building full applications with fewer lines of code.
• It supports various front-end libraries and AI agent frameworks.

Tech Stack: Copilot Kit and Pydantic AI

• Copilot Kit: Used as the front-end library for building user-facing agentic applications.
• Pydantic AI: Used as the AI agent framework, offering a direct integration with Copilot Kit through AGUI.
• The integration between Copilot Kit and Pydantic AI allows for building user-interactive applications powered by Pydantic AI agents.

AGUI Demos and Examples

• The Copilot Kit documentation provides demos showcasing the power of AGUI.
• Recipe Builder Demo: Demonstrates state sync between the front-end and back-end.
  • Adding ingredients in the front-end updates the agent's knowledge.
  • The agent can update the front-end by generating a recipe.
  • Users can collaborate with the agent to refine the recipe.
• Other demos include human-in-the-loop collaboration and tool-based generative UI.
• The code for both the front-end (Copilot Kit) and back-end (Pydantic AI) is available for review.

Getting Started with AGUI

• The Copilot Kit documentation provides a quick start guide for building agentic experiences with AGUI.
• The process involves using npm to set up a new project with Copilot Kit and AGUI.
• Users can select their preferred AI agent framework (e.g., Pydantic AI).
• The quick start provides a basic application that can be customized and expanded upon.

Building on Top of the Starter Application

• The Copilot Kit team provides a Vibe Coding MCP server that allows AI coding assistants to access Copilot Kit and AGUI documentation.
• The MCP server can be integrated with AI coding assistants like Claude Code.
• The MCP server enables AI coding assistants to understand how to build features using Copilot Kit and AGUI.

Example: Adding a "Clear All Proverbs" Tool

• The video demonstrates adding a new front-end tool to clear all proverbs from the front-end.
• The AI coding assistant uses the MCP server to access Copilot Kit documentation and implement the tool.
• The new tool is added to the UI, allowing users to clear all proverbs with a button click.
• The agent can also clear the proverbs based on a text request.

Key Principles for Building Agentic Experiences

• State Sync: Maintaining synchronization between the front-end and back-end.
• Front-End Tools: Providing the agent with tools to interact with the front-end.
• AGUI simplifies the process of implementing these principles.

Case Study: Building a RAG Application with AGUI

• The video showcases building a full application around an existing Pydantic AI RAG agent.
• The RAG agent retrieves information from a knowledge base about AI startups.
• The application displays the agent's answer in a chat interface and populates the front-end with the retrieved chunks.
• Users can view the chunks, their match percentage, and metadata.
• The application demonstrates how AGUI can enhance a typical RAG agent by providing a more interactive and informative user experience.
• The process of building the application involved following the Copilot Kit documentation and using the Vibe Coding MCP server.

Backend Simplicity

• AGUI simplifies the backend development process.
• The Pydantic AI agent can be turned into a fully working API endpoint with a single function call (.to_agui).
• The backend code is minimal, typically consisting of a single file.

Conclusion

• AGUI is a powerful protocol that simplifies the development of agentic experiences.
• It enables seamless communication between front-end applications and AI agents.
• Copilot Kit and Pydantic AI provide a robust tech stack for building agentic experiences with AGUI.
• The key principles for building agentic experiences include state sync, front-end tools, and human-in-the-loop collaboration.
• AGUI makes it easier to build interactive and informative AI applications.
• The video encourages viewers to focus on the higher-level principles of agentic experiences and to use AGUI as a tool to achieve them.

Technical Terms and Concepts

• AI Agents: Software entities designed to perceive their environment and take actions to achieve specific goals.
• SAS Platforms: Software as a Service platforms, which are applications delivered over the internet.
• Front-End: The user interface of an application, responsible for displaying information and interacting with the user.
• Back-End: The server-side logic of an application, responsible for processing data and managing the application's functionality.
• API Endpoints: Specific URLs that allow different software systems to communicate with each other.
• Middleware: Software that acts as a bridge between different applications or components.
• System Prompt: The initial instructions given to an AI agent to guide its behavior and responses.
• Tool Calls: Requests made by an AI agent to use external tools or functions.
• Streaming: Transmitting data in a continuous flow, allowing for real-time updates and responses.
• Conversation History: The record of past interactions between a user and an AI agent.
• Semantic Search: A search technique that focuses on the meaning and context of the search query, rather than just matching keywords.
• Hybrid Search: A search technique that combines semantic search with keyword search to improve accuracy and relevance.
• Metadata: Data that provides information about other data, such as the source, author, or creation date of a document.