#21 - How to Create an AI Agent
Richard Saunders II
Assistant Project Manager | Prompt Engineering | Data Analyst | Six Sigma Yellow Belt
2025 has been the year where we have seen an emergence of new tools and concepts with AI. The talk of the town recently is AI Agents. Everyone from businesses to average joe developers have been seeking ways to make them to help automate their processes.
By the end of this post, you'll know how to create a simple AI Agent. Let's dive in! ??
?? What is an AI Agent?
An AI Agent is an autonomous system that can perceive its environment, make decisions, and take action to achieve specific goals. These agents use machine learning, natural language processing, and automation frameworks to assist with tasks like:
? Customer support automation
? Data analysis & reporting
? Personalized recommendations
? Workflow automation
Now, let's build a simple AI Agent! ??
??? Step 1: Define the Purpose
Before coding, decide what your agent should do. Ask yourself:
Example: Let’s create a chatbot that automates email responses based on customer inquiries.
?? Step 2: Choose the Right Tools
You'll need:
Before diving into code, selecting the right tools is critical to ensure your AI Agent functions efficiently and meets your needs. Here’s a structured approach to choosing the right tech stack:
1?? Programming Language: Python vs. Others
While multiple languages can be used for AI Agents, Python is the most popular due to: ? Its vast AI & ML libraries (TensorFlow, PyTorch, OpenAI API) ? Ease of integration with APIs and automation frameworks ? Strong community support
Other options:
Best Choice: For most AI Agents, Python is the best bet. ??
2?? Choosing the Right AI Model
Your agent needs an intelligent core to process information and generate responses. The type of AI model you choose depends on the task:
?? Natural Language Processing (NLP) Tasks (Chatbots, AI Assistants) ?? OpenAI’s GPT-4 – Best for human-like conversations ?? Google’s Bard (Gemini) – Strong for real-time web access ?? Hugging Face Transformers – Open-source, customizable models
?? Task Automation & Decision-Making ?? Reinforcement Learning (RL) – If your AI needs to improve over time ?? Rule-Based Systems – When logic is fixed (e.g., simple FAQ bots)
?? Computer Vision (Image Recognition, OCR) ?? OpenCV – For image processing ?? YOLO – For real-time object detection
Best Choice: For most AI Agents dealing with text, GPT-4 or an open-source NLP model works best.
3?? Frameworks for Execution
To structure and run your AI Agent efficiently, consider these frameworks:
?? LangChain – Best for chaining multiple AI calls (great for chatbots) ?? FastAPI – High-performance API deployment ?? Flask/Django – For web-based AI services ?? Selenium – If automating browser tasks
Best Choice: LangChain + FastAPI for AI Agents with APIs, Selenium for automation.
4?? Data Sources & APIs
Your AI Agent may need external data to make better decisions. Here are key ways to integrate data:
? Web Scraping – Use BeautifulSoup or Scrapy to gather live data ? APIs – Fetch real-time data from services like Twitter, Google, or company databases ? Databases – Store previous interactions for personalized responses
Best Choice: APIs for live data, Databases for historical data.
5?? Deployment Options
Once your AI Agent is ready, you'll need to deploy it for real-world use. Here are your options:
?? Cloud-Based Deployment ?? AWS Lambda – Serverless execution, cost-effective ?? Google Cloud Functions – Scalable for enterprise solutions ?? Azure AI Services – Pre-built AI integrations
?? Self-Hosted Deployment ?? Flask/FastAPI – Simple APIs for small-scale use ?? Docker + Kubernetes – If scaling across multiple systems
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Best Choice: Cloud-based if scalability is needed, self-hosted for smaller projects.
Choosing the right stack ensures efficiency, scalability, and performance. For a basic AI Agent, a strong setup looks like this:
?? Python + GPT-4 API for AI processing
?? FastAPI for deployment
?? LangChain for chaining logic
?? PostgreSQL for storing interactions
?? AWS Lambda for serverless execution
?? Step 4: Deploy & Test
Once built, deploy your agent using cloud services like:
?? AWS Lambda – Serverless execution ?? Google Cloud Functions – Scalable and lightweight ?? Flask/FastAPI – Web-based interaction
Test your AI Agent with real-world scenarios, tweak responses, and refine based on user feedback.
??? Step 3: Build the AI Agent
Here’s a basic Python script for a chatbot AI Agent:
This script takes user input, processes it with an AI model, and generates a response.
Debugging Errors
When working with code on any project, there is a chance that errors and bugs will occur. When trying to run the code, I received this error.
This is when we have to take time to stop and find the root of the problem.
Use pip to install open AI and the code should run smoothly. Then you can deploy your agent and test to see if it works.
?? Step 4: Deploy & Test
Once built, deploy your agent using cloud services like:
?? AWS Lambda – Serverless execution
?? Google Cloud Functions – Scalable and lightweight
?? Flask/FastAPI – Web-based interaction
Test your AI Agent with real-world scenarios, tweak responses, and refine based on user feedback.
?? Next Steps
Now that you have a basic AI Agent, you can:
?? Expand its functionality (e.g., voice integration ???) ?? Connect it with databases for smarter decision-making ?? Implement reinforcement learning for improved automation
AI Agents are transforming industries. Whether you're automating emails or building the next-gen virtual assistant, the possibilities are endless!
?? What AI Agent are you thinking of building? Let’s discuss in the comments! ????
#AI #MachineLearning #Automation #AIAgents #TechInnovation