Create a simple AI agent using sequential LLM calls to explore commercial applications. This hands-on tutorial demonstrates core agentic principles through practical implementation.
This hands-on tutorial introduces you to building AI agents through a practical example. You'll create a simple agent that explores commercial applications of AI by making sequential LLM calls to identify business opportunities, problems, and solutions.
A 3-step AI agent that explores business opportunities automatically
Sequential LLM calls, prompt chaining, and agentic workflow patterns
Basic Python knowledge and OpenAI API access
This agent demonstrates the core principles of agentic AI through three sequential LLM calls. Each step builds upon the previous one, showing how agents can maintain context and make autonomous decisions.
# Commercial Application of Agentic AI Agent
import openai💡 Import the OpenAI library to interact with GPT models
# Step 1: Ask the LLM to pick a business area for Agentic AI opportunity
print("Step 1: Identifying a business area for Agentic AI...")
messages = [{
"role": "user",
"content": """Pick a specific business area or industry that might be worth
exploring for an Agentic AI opportunity. Focus on an industry where automation
and intelligent decision-making could have significant impact. Provide just the
industry name and a brief 2-sentence explanation of why it's promising."""
}]
response = openai.chat.completions.create(
model="gpt-4o-mini",
messages=messages
)
business_area = response.choices[0].message.content
print(f"Selected Business Area: {business_area}")
print("\\n" + "="*50 + "\\n")🎯 Step 1 explanation:
• Create a message asking the LLM to autonomously choose a business area
• Send the request to GPT-4o-mini model
• Store the response for use in the next step (context preservation)
• The LLM makes its own choice - demonstrating agent autonomy
# Step 2: Ask for a pain-point in that industry
print("Step 2: Identifying a pain-point in the selected industry...")
messages = [{
"role": "user",
"content": f"""Based on this business area: {business_area}
Now identify a specific pain-point or challenge in this industry that is
currently difficult, time-consuming, or expensive to solve. Describe a real
problem that businesses in this sector face regularly. Be specific about the
challenge and why it's problematic."""
}]
response = openai.chat.completions.create(
model="gpt-4o-mini",
messages=messages
)
pain_point = response.choices[0].message.content
print(f"Identified Pain-Point: {pain_point}")
print("\\n" + "="*50 + "\\n")🔗 Step 2 explanation:
• Uses the business_area from Step 1 as context (chaining)
• Asks LLM to identify specific problems in that chosen area
• Demonstrates sequential LLM calls building on previous output
• Agent maintains context and builds complexity
# Step 3: Propose an Agentic AI solution
print("Step 3: Proposing an Agentic AI solution...")
messages = [{
"role": "user",
"content": f"""Business Area: {business_area}
Pain-Point: {pain_point}
Now propose a specific Agentic AI solution that could address this pain-point.
Describe how autonomous AI agents could work together to solve this problem.
Include:
1. What types of AI agents would be involved
2. How they would collaborate
3. What specific tasks each agent would handle
4. How this would improve upon current solutions
Be creative but realistic about the capabilities."""
}]
response = openai.chat.completions.create(
model="gpt-4o-mini",
messages=messages
)
agentic_solution = response.choices[0].message.content
print(f"Proposed Agentic AI Solution: {agentic_solution}")
print("\\n" + "="*50 + "\\n")
print("🎉 Exercise Complete! You've successfully explored a commercial application of Agentic AI!")🚀 Step 3 explanation:
• Combines context from both previous steps (business_area + pain_point)
• Asks for a comprehensive agentic AI solution
• Demonstrates complex reasoning using accumulated context
• Shows how agents can perform sophisticated analysis by building on prior work
Here's an example of what your agent might produce when you run it:
Selected Business Area:
Healthcare diagnostics is a promising industry for Agentic AI opportunities because it requires rapid, accurate analysis of complex medical data to improve patient outcomes. Automated intelligent decision-making can assist clinicians by identifying patterns in imaging and test results, enabling earlier diagnoses and personalized treatment plans.
Identified Pain-Point:
A specific pain-point in healthcare diagnostics is the time-consuming and error-prone interpretation of medical imaging, such as radiology scans (e.g., X-rays, MRIs, CT scans).
The Challenge: Radiologists must manually review a large volume of complex imaging data daily to detect anomalies like tumors, fractures, or signs of degenerative diseases...
Proposed Agentic AI Solution:
A multi-agent AI system for automated medical imaging analysis with the following components:
1. Image Processing Agent: Pre-processes and enhances medical images
2. Detection Agent: Identifies potential anomalies using deep learning
3. Validation Agent: Cross-references findings with medical databases...
This simple agent demonstrates several key agentic AI concepts:
Each step uses the output from the previous step as input, creating a chain of reasoning that builds complexity over time.
The agent maintains context across multiple interactions, allowing it to build upon previous decisions and maintain coherent reasoning.
The LLM makes its own choices about which business area to explore and what problems to identify, demonstrating agent autonomy.
The three-step process shows how agents can follow structured workflows while still maintaining flexibility in their outputs.
Now that you've built your first agent, here are some ways to extend and improve it: