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Abstract

2f7">Agent Architecture</h2><h2 id="dcff">Input</h2><p id="5a1d">The first step is to receive and parse the incoming email. The email is cleaned and routed in the receive route using MailParser, and additional checks are made to ensure the email is from a valid Cal.com user. Once verified, the email is passed to the agent loop.</p><h2 id="7483">Agent</h2><p id="67dd">The agent is an OpenAI functions agent that uses fine-tuned versions of GPT models. It requires pre-defined functions or tools and is able to detect when a function should be called, specifying the required inputs and desired output. The agent loop is where the agent is documented.</p><h2 id="5dc0">Prompt</h2><p id="a39d">A prompt is used to provide context to the model. It includes user-specific information and is constructed and passed to the agent.</p><h2 id="8799">Chat Model</h2><p id="07b4">The chat model used is GPT-4 by OpenAI, accessed using an OPENAI_API_KEY. The temperature is set to 0 for consistent output.</p><h2 id="9e8b">Tools</h2><p id="d0f7">Tools are Javascript functions designed to work with structured data. These tools are used to interact with the Cal.com API by performing CRUD tasks and handle the response.</p><h2 id="3472">Executor</h2><p id="6e49">The executor initializes the agent and runs through a loop utilizing different tools and the LLM model to generate a response.</p><h2 id="837e">Tech Stack</h2><p id="c578">The tech stack used

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for this project includes Typescript, Next.js 13, Vercel Serverless Functions, LangChain, OpenAI GPT-4, Zod, Sendgrid, and the Cal.com API.</p><h2 id="e567">Conclusion</h2><p id="2408">Combining the knowledge of an LLM model with structured tools helps tackle natural language problems with structured data, increasing the reliability of the agents. This approach shows the power of AI agents with scoped tools and we’re excited for the future of AI agents as they continue to evolve.</p><div id="c45d" class="link-block"> <a href="https://readmedium.com/langchain-query-transformations-0f9b0963c530"> <div> <div> <h2>LANGCHAIN — Query Transformations</h2> <div><h3>I’m not a great programmer; I’m just a good programmer with great habits. — Kent Beck.</h3></div> <div><p>medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*nu7ZXSdSXeo6aCLEJYoZpg.jpeg)"></div> </div> </div> </a> </div><p id="5a34">In this tutorial, we’ve outlined the key components involved in designing an AI agent for production using LangChain and OpenAI. By understanding the agent architecture, tech stack, and the role of each component, you can create powerful and effective AI agents for various applications.</p></article></body>

LANGCHAIN — How to Design an Agent for Production

Real artists ship. — Steve Jobs

When designing an agent for production, several key components need to be considered to ensure the agent functions effectively. In this tutorial, we’ll walk through the steps and code snippets to design an AI agent for production using LangChain and OpenAI. The agent we’ll be designing is a scheduling assistant called Cal.ai, which will be capable of managing calendar bookings and responding to natural language emails.

Agent Architecture

Input

The first step is to receive and parse the incoming email. The email is cleaned and routed in the receive route using MailParser, and additional checks are made to ensure the email is from a valid Cal.com user. Once verified, the email is passed to the agent loop.

Agent

The agent is an OpenAI functions agent that uses fine-tuned versions of GPT models. It requires pre-defined functions or tools and is able to detect when a function should be called, specifying the required inputs and desired output. The agent loop is where the agent is documented.

Prompt

A prompt is used to provide context to the model. It includes user-specific information and is constructed and passed to the agent.

Chat Model

The chat model used is GPT-4 by OpenAI, accessed using an OPENAI_API_KEY. The temperature is set to 0 for consistent output.

Tools

Tools are Javascript functions designed to work with structured data. These tools are used to interact with the Cal.com API by performing CRUD tasks and handle the response.

Executor

The executor initializes the agent and runs through a loop utilizing different tools and the LLM model to generate a response.

Tech Stack

The tech stack used for this project includes Typescript, Next.js 13, Vercel Serverless Functions, LangChain, OpenAI GPT-4, Zod, Sendgrid, and the Cal.com API.

Conclusion

Combining the knowledge of an LLM model with structured tools helps tackle natural language problems with structured data, increasing the reliability of the agents. This approach shows the power of AI agents with scoped tools and we’re excited for the future of AI agents as they continue to evolve.

In this tutorial, we’ve outlined the key components involved in designing an AI agent for production using LangChain and OpenAI. By understanding the agent architecture, tech stack, and the role of each component, you can create powerful and effective AI agents for various applications.

Langchain
Agent
ChatGPT
Design
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