Custom AI Agent: how to build an AI Agent
Published: 2024-06-11 14:29:22

---
title: Custom AI Agent: how to build an AI Agent
published: true
date: 2024-06-06 05:20:58 UTC
tags: 
canonical_url: https://blog.composio.dev/custom-ai-agent/
---

 ![Custom AI Agent: how to build an AI Agent](https://blog.composio.dev/content/images/2024/06/Image-showing-Custum-AI-Agent-Builder.webp)

The advent of Large Language Models (LLMs) has revolutionized the field of artificial intelligence, introducing new ways to interact with software. These models excel in reasoning, mathematics, programming, summarizing, and more. They can comprehend complex problems, decompose them into simpler sub-problems, and provide solutions. This makes them ideal for automating tasks that require logical and situational reasoning and decision-making capabilities. The systems that enable LLMs to understand and solve problems using tools are known as custom [AI agents](https://dev.to/sohamganatra/ai-agents-101-types-examples-and-trends-4jcf-temp-slug-8170323).

This article will explore the concept of custom AI agents, their applications, and the steps involved in creating them.

## Learning Objectives while Building Custom AI Agents

- Understand what custom AI agents are.
- Learn when to use custom AI agents.
- Learn how to build custom AI agents.
- Discover the benefits and drawbacks of using custom AI agents.
- Explore how Composio can help you build custom AI agents.
- Build a to-do list to Google calender AI agents using LangChain and Composio.

## **What are Custom AI Agents?**

AI agents are systems powered by AI models that autonomously perform tasks, interact with their environment, and make decisions based on their programming and the data they process. These custom agents can handle tasks requiring reasoning and decision-making abilities, such as scheduling meetings, managing emails, reading from files, and determining subsequent actions.

For instance, you can use an automation tool to sync your project tasks between GitHub and a project management platform like Trello or Asana. Alternatively, you can develop a custom AI agent to send personalized sales emails crafted specifically for your customers. These are just a few examples. You can automate more complicated tasks with the right [AI agent tool](https://dev.to/sohamganatra/making-the-most-of-llms-with-ai-agent-tools-5g02-temp-slug-7459543) integrations and LLMs like GPT-4.

## **When to Create Custom AI Agents?**

Creating custom AI agents becomes particularly valuable when you need to automate complex workflows that involve multiple decision points and require high adaptability. Here are some scenarios where developing custom AI agents can be beneficial:

### 1. Custom AI Agent for Personalized Customer Interaction

Building custom AI agents can be highly beneficial for businesses that need to interact with customers in a personalized and efficient manner. They can handle a variety of tasks, including:

- **Customer Support** : AI agents with access to user data can provide automated user-specific tailored assistance.
- **Personalized Recommendation** : By analyzing customer data, AI agents can offer product or service recommendations that cater to individual preferences and needs.

### 2. Custom Sales and Marketing Agent

This custom AI agents can automate many routine tasks to help focus the sales and marketing team on what is important. They can automate tasks like:

- **Lead Scoring** : The agent can score sales leads based on custom criteria like company size, industry, likelihood of conversion, etc. The agent can ingest lead data from CSVs and score them.
- **Sales Forecasting** : The agents can process a dataset of historical data, analyze the data, and provide future sales insights with necessary plots, graphs, and textual summaries.

### 3. Make your own Media and News Agent

Custom AI agents can streamline the creation and distribution of content across websites. Here are a few examples of how agents can automate your social media strategy.

- **Podcast-Tweet Writer** : The agent takes in a URL to a podcast and a topic. The agent will find the interesting part of the podcast related to the topic and post a tweeter thread on behalf of the user. This can be extended to writing an article or Instagram post.
- **Subreddit Analysis** : The AI agent can take a subreddit (r/shopify) and analyze user posts to create a report on trends, general sentiment, etc.

### 4. Building HR/Hiring Agent

AI agents can efficiently handle many routine tasks from shortlisting candidates to conducting employee surveys. Here are some use cases where agents can aid HR processes:

- **LinkedIn Profile Processing** : The agent can process potential LinkedIn profiles, score candidates, summarize their work experiences, and put them in the organization’s database for manual evaluation.
- **Employee Survey** : HR professionals can streamline the survey process to gauge employee sentiment and gather relevant metrics, ultimately improving workplace productivity.

### 5. Administration Custom Agent

An intelligent agent can automate many tedious workflows in administrative processes. This subsequently frees personnel to devote more time to improving the quality of services.

- **Event Creator** : An intelligent agent can read emails, and extract relevant information like date, event name, time, date, and participants to create a calendar event.
- **Response Scorer** : An AI agent with Typeform integration can read user surveys to evaluate user preferences, monitor customer satisfaction, and prioritize product features based on user responses.

## How to Build your Own AI Agents?

Building custom AI agents for solving unique problems can be interesting and challenging at the same time. Building a custom AI agent involves several steps, each critical to the agent's functionality and effectiveness. Here's a high-level overview of the process:

1. **Goal Initialization** : The AI models used in most software-based agents are LLMs (Large Language Models) or LMM (Large Multi-modal Models). To perform tasks, you need to give agents an objective. The model can understand objectives and will further proceed to solve them.
2. **Choosing Models** : Choosing models is an important step. While GPT-4 and Claude Opus are excellent at solving problems, they can be expensive. For less complicated tasks, models like GPT-4o, Llama 3, and Claude Sonnet are better suited. Factors such as cost, inference speed, model capability, and the nature of the model (whether open-source or proprietary) need to be considered.
3. **Tool Integration** : The custom ai agents require access to the appropriate tools to perform meaningful tasks. For instance, an agent needs a web search tool to browse the web and to execute code, it needs a code interpreter. These tools are software components that encapsulate the functionalities of external applications.
4. **Develop the Logic and Workflow:** Design the logic that governs how the AI agent interacts with its environment and makes decisions. This involves creating algorithms, setting up rules, and defining workflows.
5. **Test and Refine:** Thoroughly test the AI agent to ensure it performs as expected. Collect feedback, identify issues, and refine the agent’s algorithms and workflows to improve accuracy and efficiency.
6. **Deploy and Monitor:** Once the AI agent is ready, deploy it in your desired environment. Continuously monitor its performance and adjust as needed to ensure it remains effective and aligned with your objectives.

## Benefits of Using Custom AI Agents

There are many advantages to using custom AI agents in your workflows that can significantly impact business operations.

- **Improved Efficiency:** Custom AI agents can manage tedious and repetitive tasks such as data entry, scheduling, and basic analysis. This allows companies to free up time and resources for more demanding and creative projects. Businesses can allocate their resources more effectively by delegating these routine tasks to AI agents,
- **Enhanced Personalization:** Custom AI agents excel at delivering personalized experiences by analyzing customer data. By integrating AI agents into their products, companies can provide tailored interactions based on customer data and browsing history. This enables AI agents to offer customized solutions to customer queries, enhancing overall satisfaction.
- **Higher Availability:** In scenarios requiring 24/7 availability, Building custom AI agents can complement human staff to improve the overall customer experience. They can handle simpler tasks and queries, allowing human staff to focus on more complex issues that require a human touch. This ensures continuous service and support.
- **Scalability:** Own Custom AI agents are highly scalable. The agents can be scaled to meet surging demands without requiring additional human resources. The scalability ensures that businesses can continue to deliver quality services even during peak times.

## Drawbacks of Building Custom AI Agents

While custom AI agents offer numerous benefits, there are also several drawbacks to consider:

- **Reliability** : One of the biggest issues with current AI agents is reliability. The AI models that power these agents are stochastic, it is hard to get consistent results in every agent run. It is necessary to employ state-of-the-art models and extensive prompt engineering to ensure their reliability and usefulness,
- **Integration Challenges:** Integrating custom AI agents with existing systems and workflows can be complex and time-consuming. Compatibility issues may arise, requiring additional customization and development efforts to ensure seamless integration.
- **Complexity and maintenance** : custom AI agents for automating complex tasks can be difficult to build, deploy, and maintain. They require ongoing updates and maintenance to ensure they function correctly and efficiently. This can demand significant technical expertise and resources.
- **Cost** : While custom AI agents can automate numerous tasks, it is crucial to consider the cost-to-efficiency ratio. A complex multi-agent setup requires constant back-and-forth communication between different agents, which will use a lot of tokens. Powering these agents with frontier AI models can rack up bills easily.
- **Security** : When custom AI agents require access to external tools and APIs managing user authorization and authentication is crucial. Ensuring secure access involves implementing robust authentication mechanisms and safeguarding sensitive credentials.

## **How Can Composio Assist with Your Custom AI Agent Needs?**

Composio offers a comprehensive tooling solution for custom AI agents, empowering developers to create custom AI agents for production use cases. These tools allow custom AI agents to interact with external systems dynamically. For example, to use Slack, or Discord functionalities in an agentic workflow, you would need integration modules that allow the AI agents to send messages, manage channels, or perform administrative tasks within these platforms.

These integrations are essential for own AI agents to perform meaningful actions based on real-time data and interactions. Composio offers a range of pre-built integrations that facilitate seamless connectivity between AI agents and various external applications. This allows developers to focus on building intelligent workflows without worrying about the complexities of interfacing with third-party services.

Furthermore, Composio implements robust security measures. The developers can manage user authentication and authorization efficiently, protecting sensitive information and maintaining compliance with industry standards.

## Custom AI Agent Tutorials - Composio

Composio has native support for popular AI Agent AI-building frameworks like LangChain, AutoGen, CrewAI, and more. You can add Composio tool sets to your new AI agent or existing agents by adding a few lines of code. This seamless integration ensures secure access to tools through robust authorization and authentication mechanisms.

So, let’s build a custom AI agent that converts Todo lists to Google Calendar events.

### To-do to Google Calender Custom Agent

We need CrewAI, a framework for building collaborative multi-agent systems, access to an LLM API, and Composio SDK to create the AI agent, For this project, we will use Gemini Flash. So, get the API keys for Gemini from Google AI Studio. Save the API key in a ._env_ file.

As with any Python project create a virtual environment and install the below dependencies.

```
composio_core
composio-crewai
crewai
python-dotenv
langchain-google-genai

```

Now log in to your Composio account and add the Google Calendar integration by running the below commands.

```
composio update
composio login
composio add googlecalendar

```

This will prompt you to grant access to the integration, once approved you can use it in the magnetic workflows.

Create a Python file and add these import statements.

```
# Import base packages
import os
import dotenv
from datetime import datetime
from crewai import Agent, Task
from composio_crewai import ComposioToolSet, App
from langchain_google_genai import ChatGoogleGenerativeAI

```

Now, configure the LLM and Composio Google calendar tool.

```
dotenv.load_dotenv()
llm = ChatGoogleGenerativeAI(google_api_key=os.environ["GEMINI_API_KEY"], model="gemini-1.5-flash")

composiotoolset = ComposioToolSet()
tools = composiotoolset.get_tools(apps=[App.GOOGLECALENDAR])

date = datetime.today().strftime('%Y-%m-%d')
timezone = datetime.now().astimezone().tzinfo

```

Define a sample to-do list that you want to add to your calendar.

```
todo = '''
    1PM - 3PM -> Code,
    5PM - 7PM -> Meeting,
    9AM - 12AM -> Learn soemthing
    8PM - 10PM -> Game

'''

```

Now, define the CrewAI agent with the LLM, a task, a goal, and a backstory.

```
def run_crew():
    gcal_agent = Agent(role='Google Calendar Agent',
    goal="""You take action on Google Calendar using Google Calendar APIs""",
    backstory="""You are an AI agent that is responsible for taking actions on
    Google Calendar on users' behalf. You need to take action on
    Calendar using Google Calendar APIs. Use the Correct tools to run
    APIs from the given tool-set""",
    verbose=True,
    tools=tools,
    llm=llm)
    task = Task(
    description=f"book slots according to {todo}. Label them with the work provided to be done in that time period. Schedule it for today. Today's date is {date} (it's in YYYY-MM-DD format) and make the timezone be {timezone}.",
    agent=gcal_agent,
    expected_output="if a free slot is found"
    )
    task.execute()
    return "Crew run initiated", 200

run_crew()

```

Run the code.

```
python todo.py

```

This will trigger the agent and you can observe the logs on your terminal.

![Custom AI Agent: how to build an AI Agent](https://blog.composio.dev/content/images/2024/06/Screenshot-from-2024-06-05-21-52-36.webp)

Once the agent run is finished, you can visit your Google calendar and see the to-dos.

![Custom AI Agent: how to build an AI Agent](https://blog.composio.dev/content/images/2024/06/Screenshot-from-2024-06-05-22-24-16-1.png)

You can also monitor the live tools on Composio’s dedicated [dashboard](https://app.composio.dev/your_apps?ref=blog.composio.dev). You can explore the available actions, triggers, and logs of past runs on the dashboard.

![Custom AI Agent: how to build an AI Agent](https://blog.composio.dev/content/images/2024/06/Screenshot-from-2024-06-05-23-14-58-1.png)

GitHub link for the code: [https://github.com/anonthedev/composio-todo-to-calendar](https://github.com/anonthedev/composio-todo-to-calendar?ref=blog.composio.dev)

Another examples of custom AI Agents

1. [Code Agent](https://dev.to/sohamganatra/code-agent-revolutionizing-coding-with-ai-2amp-temp-slug-3941956)
2. [PR Agent](https://dev.to/sohamganatra/crewai-pr-agent-accelerate-pull-requests-with-code-pr-3mo3-temp-slug-4297203)

## Conclusion

The custom AI agents are here to stay and with the improvement in LLMs, especially in tool-calling capacities, the potential for automating complex workflows and enhancing decision-making processes will only continue to grow. As these models become more sophisticated and capable, the range of tasks AI agents can handle will expand.

Custom AI agents, powered by these LLMs and tool integrations, can handle complex workflows and make intelligent decisions based on real-time data. Composio stands out as a comprehensive solution for developing these agents, offering seamless integration with popular frameworks and robust support for various tools and APIs. Additionally, Composio provides efficient user authorization and authentication management, ensuring secure access to integrated tools and data. This allows developers to build efficient, reliable, and production-ready AI agents with confidence.

## Frequently Asked Questions

### 1. What are custom AI agent solutions?

Custom AI agent solutions are tailored systems powered by AI models that autonomously perform specific tasks, interact with their environment, and make decisions based on programmed instructions and data processing. They are designed to automate complex workflows and enhance operational efficiency for unique business needs.

### 2. How can custom AI agents be developed rapidly for individual use cases?

Custom AI agents can be rapidly developed by leveraging platforms like Composio, which offer pre-built integrations with popular AI frameworks and provide tools and APIs for quick and seamless integration, requiring minimal code.

### 3. What are the benefits of using custom AI agents for individual use cases?

Custom AI agents improve efficiency by automating repetitive tasks, enhance personalization through data analysis, provide 24/7 availability, and easily scale to meet increasing demands.

### 4. Can custom AI agents be tailored to specific industries or sectors?

Yes, custom AI agents can be tailored to specific industries by leveraging industry-specific data and integrating relevant tools, ensuring they address unique challenges and requirements.

### 5. How much does a custom AI agent cost?

The cost of custom AI agent development varies based on task complexity, model used, tool integration, and scale of deployment. While it is easy to build agents for simple use-cases, complex multi-agent systems may require more engineering hours.