We've Been Building AI Agents WRONG Until Now
Summary
The video introduces the concept of language models and discusses advancements in frameworks like Langchain crew aai and swarm. It touches on the limitations of current frameworks for production use and highlights the features of Pantic AI that simplify developing production-level models, such as management, retry logic, testing capabilities, and model output validation. The importance of validation in frameworks like Pantic AI is emphasized, particularly in building agents, where validation serves as a core aspect of model development, aiding in the ease of creating and testing agents. The walkthrough of building a web search agent using Pantic AI includes coding steps and the integration of chat history, showcasing a streamlit version for user interaction with the AI model. The video concludes with a reminder of the importance of staying informed about AI advancements, utilizing new features in frameworks, and exploring AI agent possibilities for the future.
Introduction to Language Models
Introduction to the concept of language models and the advancements in frameworks like Langchain crew aai and swarm. Mention of the limitations of current frameworks for mature and production use.
Pantic AI Features
Overview of the features of Pantic AI that make it easier to develop production-level models. Features include management, retry logic, testing capabilities, and model output validation.
Validation in Pantic AI
Explanation of the importance of validation in frameworks like Pantic AI, highlighting structured output and validation layers for language models.
Building an Agent with Pantic AI
Discussion on building agents using Pantic AI, focusing on validation as a core aspect of model development and highlighting the ease of creating agents.
Setting up a Web Search Agent
Walkthrough of building a web search agent using Pantic AI, including prerequisites, coding steps, and running the agent with a Python script.
Enhancing the Agent with Chat History
Explanation of adding chat history to the web search agent, demonstrating the integration of chat features with the existing agent.
Testing a Streamlit Version of the Agent
Testing a streamlit version of the agent that facilitates interaction with the AI model through a user interface, showcasing its functionality.
Conclusion and Future Directions
Summary of the importance of staying updated with AI advancements, leveraging new features in frameworks, and exploring the possibilities of AI agents. Ending remarks on the content and future interactions.
FAQ
Q: What are the limitations of current frameworks for mature and production use?
A: Current frameworks often face limitations in aspects like management, retry logic, testing capabilities, and model output validation for developing production-level models.
Q: Why is validation important in frameworks like Pantic AI?
A: Validation is crucial in frameworks like Pantic AI because it ensures structured output and provides validation layers for language models, enhancing the reliability and accuracy of the models.
Q: How does Pantic AI make it easier to develop production-level models?
A: Pantic AI simplifies model development by offering features like management tools, robust retry logic, testing capabilities, and model output validation, which collectively streamline the process of creating and deploying models.
Q: What is the core aspect of model development when building agents using Pantic AI?
A: Validation is a core aspect of model development when utilizing Pantic AI for building agents, ensuring the accuracy and reliability of the models being created.
Q: Can you provide a walkthrough of building a web search agent using Pantic AI?
A: Building a web search agent with Pantic AI involves prerequisites setup, coding steps implementation, and ultimately running the agent with a Python script for interaction and functionality.
Q: How is chat history integrated into the web search agent developed with Pantic AI?
A: The web search agent developed using Pantic AI can have chat history integrated, showcasing the ability to add chat functionalities seamlessly to the existing agent for enhanced user interaction.
Q: What is the significance of testing a streamlit version of the agent?
A: Testing a streamlit version of the agent is essential as it enables user interaction with the AI model through a user interface, proving the functionality, usability, and effectiveness of the developed agent.
Q: What is the importance of staying updated with AI advancements in the context of building AI agents?
A: Staying updated with AI advancements is crucial for leveraging new features in frameworks like Pantic AI, exploring AI agents' possibilities, and ensuring that the developed models remain competitive and relevant in the rapidly evolving AI landscape.
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