Building Your First Agentic AI Workflow: A Complete Beginner's Guide
- Revanth Reddy Tondapu
- Aug 7
- 13 min read
This comprehensive guide will walk you through creating your first agentic AI workflow in Python. You'll learn to build an intelligent system that can make multiple LLM API calls, plan its own actions, and solve complex problems autonomously.
📌 Introduction
What Are Agentic AI Workflows?
An agentic AI workflow is a process where AI agents operate autonomously to accomplish complex tasks without constant human intervention. Unlike traditional chatbots that simply respond to single queries, agentic systems can:
Plan their own approach to solving problems
Use tools and make multiple API calls
Reflect on their progress and adjust strategies
Execute multi-step processes independently
Think of it like having a smart assistant that doesn't just answer questions, but actually goes out and completes entire projects for you. Instead of you having to break down a complex task and guide the AI through each step, an agentic system figures out the steps itself and executes them.
Why This Matters for Beginners
Agentic workflows represent the next evolution of AI applications. While basic chatbots can answer questions, agentic systems can actually do work. According to industry experts, agentic AI could make 15% of all day-to-day work decisions by 2028. Learning to build these systems now puts you ahead of the curve in understanding the future of AI development.
What You'll Build
In this tutorial, you'll create a system that can:
Take a user query (like "Find me information about a company and suggest business opportunities")
Break that complex task into smaller steps automatically
Make multiple API calls to gather information
Generate a comprehensive response
Learn from its actions to improve future performance
Agentic AI Workflow Process - Step-by-step flow showing how an AI agent processes tasks autonomously
🧱 Step-by-Step Implementation
Step 1: Set Up Your Development Environment
Before we start coding, we need to create a proper Python environment. This is like setting up a clean workspace before starting a project.
Create a Project Directory:
# Create a new directory for your project
mkdir agentic-workflow
cd agentic-workflow
# Create a virtual environment (think of this as an isolated workspace)
python -m venv .venv
# Activate the virtual environment
# On Windows:
.venv\Scripts\activate
# On Mac/Linux:
source .venv/bin/activateInstall Required Packages:
pip install openai python-dotenv requestsHere's what each package does:
openai: Official library for connecting to OpenAI's GPT models
python-dotenv: Safely manages API keys and secrets
requests: Makes HTTP requests to various APIs
Step 2: Secure Your API Keys
Never hardcode API keys directly in your code! This is a major security risk. Instead, we'll use environment variables.
Create a .env file:
# Create the .env file (this stores your secrets safely)
touch .envAdd your OpenAI API key to .env:
OPENAI_API_KEY=sk-proj-your-actual-api-key-hereImportant Security Notes:
Never share your .env file
Add .env to your .gitignore file so it's not uploaded to GitHub
Treat API keys like passwords - keep them secret
Step 3: Build the Basic AI Agent
Let's start with a simple agent that can make API calls to OpenAI:
# agent.py
import os
from dotenv import load_dotenv
from openai import OpenAI
import json
# Load environment variables from .env file
load_dotenv()
class SimpleAgent:
def __init__(self):
# Initialize the OpenAI client with your API key
self.client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
def call_llm(self, prompt, system_message="You are a helpful assistant."):
"""
Make a call to the OpenAI API
Args:
prompt (str): The user's question or task
system_message (str): Instructions for how the AI should behave
Returns:
str: The AI's response
"""
try:
# This is the standard way to call OpenAI's chat API
response = self.client.chat.completions.create(
model="gpt-4o-mini", # Using the fastest, cheapest model
messages=[
{"role": "system", "content": system_message},
{"role": "user", "content": prompt}
],
temperature=0.7 # Controls creativity (0.0 = very focused, 1.0 = very creative)
)
return response.choices[0].message.content
except Exception as e:
return f"Error calling LLM: {str(e)}"
# Test the basic agent
if __name__ == "__main__":
agent = SimpleAgent()
response = agent.call_llm("What is 2 + 2?")
print(f"Agent response: {response}")What's happening here:
We create an Agent class to organize our code
The call_llm method sends requests to OpenAI's API
We use try/catch to handle any errors gracefully
The messages format is how OpenAI expects conversation data
Step 4: Add Planning Capabilities
Now let's make our agent "agentic" by giving it the ability to plan multi-step tasks:
# agentic_agent.py
import os
from dotenv import load_dotenv
from openai import OpenAI
import json
import time
load_dotenv()
class AgenticAgent:
def __init__(self):
self.client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
self.conversation_history = [] # Keep track of the conversation
def call_llm(self, prompt, system_message="You are a helpful assistant."):
"""Make an API call to OpenAI"""
try:
response = self.client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": system_message},
{"role": "user", "content": prompt}
],
temperature=0.7
)
return response.choices[0].message.content
except Exception as e:
return f"Error: {str(e)}"
def create_plan(self, user_query):
"""
Break down a complex query into smaller, manageable steps
This is where the 'agentic' behavior begins!
"""
planning_prompt = f"""
You are an AI planning assistant. Break down this complex task into 3-5 specific, actionable steps.
Task: {user_query}
Return your response as a numbered list of steps. Be specific about what needs to be done in each step.
For example:
1. Research information about [specific topic]
2. Analyze the findings to identify [specific patterns]
3. Generate recommendations based on [specific criteria]
Your plan:
"""
plan = self.call_llm(planning_prompt)
print(f"🧠 Agent Planning:\n{plan}\n")
return plan
def execute_step(self, step_description, context=""):
"""
Execute a single step of the plan
"""
execution_prompt = f"""
You are executing this step of a larger plan: {step_description}
Context from previous steps: {context}
Complete this step and provide a detailed response. If you need more information to complete this step,
explain what additional information would be helpful.
"""
result = self.call_llm(execution_prompt)
print(f"⚡ Executing: {step_description}")
print(f"Result: {result}\n")
return result
def reflect_on_results(self, original_query, plan, results):
"""
Analyze the results and determine if the original query was answered satisfactorily
This is the 'reflection' part of agentic behavior
"""
reflection_prompt = f"""
Original query: {original_query}
Plan that was executed:
{plan}
Results from execution:
{results}
Analyze whether the original query was answered completely and satisfactorily.
If not, suggest what additional steps might be needed.
"""
reflection = self.call_llm(reflection_prompt)
print(f"🤔 Agent Reflection:\n{reflection}\n")
return reflection
def process_query(self, user_query):
"""
Main method that orchestrates the entire agentic workflow
"""
print(f"📝 User Query: {user_query}\n")
# Step 1: Create a plan
plan = self.create_plan(user_query)
# Step 2: Extract individual steps (simple parsing)
steps = [line.strip() for line in plan.split('\n') if line.strip() and any(char.isdigit() for char in line)]
# Step 3: Execute each step
results = []
context = ""
for step in steps:
result = self.execute_step(step, context)
results.append(result)
context += f"Step result: {result}\n\n"
time.sleep(1) # Brief pause to avoid rate limiting
# Step 4: Reflect on the overall results
reflection = self.reflect_on_results(user_query, plan, "\n".join(results))
# Step 5: Generate final response
final_prompt = f"""
Based on this analysis and reflection, provide a comprehensive final answer to the user's original query: {user_query}
Plan executed: {plan}
Results: {results}
Reflection: {reflection}
Final answer:
"""
final_response = self.call_llm(final_prompt)
print(f"✅ Final Response:\n{final_response}")
return final_response
# Test the agentic agent
if __name__ == "__main__":
agent = AgenticAgent()
# Try a complex query that requires multiple steps
query = "I want to start a small online business. Help me identify a good business opportunity and create a basic plan."
agent.process_query(query)Key Agentic Features Implemented:
Planning: The agent breaks down complex tasks automatically
Execution: It works through each step systematically
Reflection: It analyzes its own work to check for completeness
Context Awareness: Each step builds on previous results
Step 5: Add Tool Usage (External APIs)
Real agentic systems can use external tools and APIs. Let's add some basic tool capabilities:
# enhanced_agent.py
import os
from dotenv import load_dotenv
from openai import OpenAI
import requests
import json
import time
load_dotenv()
class EnhancedAgenticAgent:
def __init__(self):
self.client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
self.tools = {
'web_search': self.simulate_web_search,
'data_analysis': self.simulate_data_analysis,
'content_generation': self.generate_content
}
def call_llm(self, prompt, system_message="You are a helpful assistant."):
"""Standard LLM API call"""
try:
response = self.client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": system_message},
{"role": "user", "content": prompt}
],
temperature=0.7
)
return response.choices[0].message.content
except Exception as e:
return f"Error: {str(e)}"
def simulate_web_search(self, query):
"""
Simulate a web search (in a real implementation, you'd use a real search API)
"""
print(f"🔍 Searching the web for: {query}")
# In a real implementation, you might use:
# - Google Custom Search API
# - Bing Search API
# - Serper API
# - Tavily Search API
search_prompt = f"""
Simulate the results of a web search for: "{query}"
Provide realistic, helpful information that would typically be found in search results.
Include specific details, statistics, or examples where appropriate.
"""
result = self.call_llm(search_prompt)
print(f"Search results: {result[:200]}...\n")
return result
def simulate_data_analysis(self, data_description):
"""
Simulate data analysis capabilities
"""
print(f"📊 Analyzing data: {data_description}")
analysis_prompt = f"""
Perform a data analysis on: {data_description}
Provide insights, trends, patterns, and actionable recommendations.
Include specific metrics or statistics where relevant.
"""
result = self.call_llm(analysis_prompt)
print(f"Analysis complete: {result[:200]}...\n")
return result
def generate_content(self, content_type, requirements):
"""
Generate specific types of content
"""
print(f"✍️ Generating {content_type}: {requirements}")
content_prompt = f"""
Create {content_type} that meets these requirements: {requirements}
Make it professional, engaging, and actionable. Include specific details and examples.
"""
result = self.call_llm(content_prompt)
print(f"Content generated: {result[:200]}...\n")
return result
def select_and_use_tools(self, task_description):
"""
Determine which tools to use for a given task and execute them
This is a key agentic behavior - tool selection and usage
"""
tool_selection_prompt = f"""
You need to complete this task: {task_description}
Available tools:
- web_search: Research information online
- data_analysis: Analyze data and identify patterns
- content_generation: Create documents, plans, or other content
Which tool(s) should be used for this task? Respond with just the tool name(s), separated by commas.
For example: "web_search" or "web_search, data_analysis"
"""
selected_tools = self.call_llm(tool_selection_prompt).strip().lower()
print(f"🛠️ Selected tools: {selected_tools}")
results = []
if 'web_search' in selected_tools:
search_result = self.simulate_web_search(task_description)
results.append(f"Web Search: {search_result}")
if 'data_analysis' in selected_tools:
analysis_result = self.simulate_data_analysis(task_description)
results.append(f"Data Analysis: {analysis_result}")
if 'content_generation' in selected_tools:
content_result = self.generate_content("plan", task_description)
results.append(f"Content Generation: {content_result}")
return results
def enhanced_process_query(self, user_query):
"""
Enhanced workflow that includes tool usage
"""
print(f"📝 User Query: {user_query}\n")
# Step 1: Create a plan
plan = self.create_plan(user_query)
# Step 2: For each step, determine if tools are needed
steps = [line.strip() for line in plan.split('\n') if line.strip() and any(char.isdigit() for char in line)]
all_results = []
context = ""
for step in steps:
print(f"\n--- Processing Step: {step} ---")
# Determine if this step needs tools
tool_results = self.select_and_use_tools(step)
# Execute the step with tool results as context
step_context = context + "\n".join(tool_results)
result = self.execute_step(step, step_context)
all_results.append({
'step': step,
'tool_results': tool_results,
'result': result
})
context += f"Step: {step}\nResult: {result}\n\n"
time.sleep(1)
# Step 3: Generate final comprehensive response
final_prompt = f"""
Original query: {user_query}
Here's what was accomplished:
Plan: {plan}
Detailed results:
{json.dumps(all_results, indent=2)}
Provide a comprehensive, actionable final response to the user's query.
"""
final_response = self.call_llm(final_prompt)
print(f"\n✅ Final Comprehensive Response:\n{final_response}")
return final_response
def create_plan(self, user_query):
"""Create a plan for the given query"""
planning_prompt = f"""
Break down this task into 3-5 specific, actionable steps: {user_query}
Return as a numbered list. Each step should be clear and specific.
"""
plan = self.call_llm(planning_prompt)
print(f"🧠 Plan Created:\n{plan}\n")
return plan
def execute_step(self, step_description, context=""):
"""Execute a single step"""
execution_prompt = f"""
Execute this step: {step_description}
Available context: {context}
Provide a detailed result for this step.
"""
result = self.call_llm(execution_prompt)
print(f"⚡ Step Result: {result[:200]}...\n")
return result
# Test the enhanced agent
if __name__ == "__main__":
agent = EnhancedAgenticAgent()
# Test with a complex business query
query = "Help me research and create a marketing plan for a new eco-friendly water bottle startup targeting college students."
agent.enhanced_process_query(query) Step 6: Add Memory and Learning
Let's add basic memory so our agent can learn from previous interactions:
# memory_agent.py
import os
import json
from datetime import datetime
from dotenv import load_dotenv
from openai import OpenAI
load_dotenv()
class MemoryAgent:
def __init__(self, memory_file="agent_memory.json"):
self.client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
self.memory_file = memory_file
self.memory = self.load_memory()
def load_memory(self):
"""Load previous experiences from file"""
try:
with open(self.memory_file, 'r') as f:
return json.load(f)
except FileNotFoundError:
return {"interactions": [], "learned_patterns": []}
def save_memory(self):
"""Save experiences to file"""
with open(self.memory_file, 'w') as f:
json.dump(self.memory, f, indent=2, default=str)
def add_interaction(self, query, plan, results, success_rating):
"""Store an interaction in memory for future learning"""
interaction = {
"timestamp": datetime.now(),
"query": query,
"plan": plan,
"results": results,
"success_rating": success_rating # 1-10 scale
}
self.memory["interactions"].append(interaction)
self.save_memory()
def learn_from_memory(self):
"""Analyze past interactions to identify successful patterns"""
if len(self.memory["interactions"]) < 3:
return "Not enough interactions to learn from yet."
learning_prompt = f"""
Analyze these past interactions and identify patterns of what works well:
{json.dumps(self.memory["interactions"][-5:], indent=2, default=str)}
What patterns do you notice in successful approaches? What should be avoided?
Provide specific insights about effective planning and execution strategies.
"""
insights = self.call_llm(learning_prompt)
self.memory["learned_patterns"].append({
"timestamp": datetime.now(),
"insights": insights
})
self.save_memory()
return insights
def call_llm(self, prompt, system_message="You are a helpful assistant."):
"""Standard LLM call"""
try:
# Include learned patterns in the system message
enhanced_system_message = system_message
if self.memory["learned_patterns"]:
latest_patterns = self.memory["learned_patterns"][-1]["insights"]
enhanced_system_message += f"\n\nBased on past experience: {latest_patterns}"
response = self.client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": enhanced_system_message},
{"role": "user", "content": prompt}
],
temperature=0.7
)
return response.choices[0].message.content
except Exception as e:
return f"Error: {str(e)}"
def process_with_memory(self, user_query):
"""Process a query while leveraging memory from past experiences"""
print(f"📝 Processing: {user_query}")
print(f"💭 Memory contains {len(self.memory['interactions'])} past interactions")
# Learn from past experiences before processing
if len(self.memory["interactions"]) >= 3:
print("🧠 Learning from past experiences...")
insights = self.learn_from_memory()
print(f"Insights gained: {insights[:200]}...\n")
# Create plan (now informed by past experiences)
plan = self.call_llm(f"Create a detailed plan to address: {user_query}")
print(f"📋 Plan: {plan}\n")
# Execute (simplified for demo)
execution_result = self.call_llm(f"Execute this plan step by step: {plan}")
print(f"⚡ Execution: {execution_result}\n")
# Get user feedback (in a real app, this would come from user interaction)
feedback_prompt = f"""
Rate the effectiveness of this response from 1-10:
Query: {user_query}
Plan: {plan}
Result: {execution_result}
Consider: Was the plan logical? Was the execution thorough? Was the result helpful?
Return only a number from 1-10.
"""
success_rating = int(self.call_llm(feedback_prompt).strip()[:2]) # Extract just the number
# Store interaction in memory
self.add_interaction(user_query, plan, execution_result, success_rating)
print(f"💾 Interaction saved with rating: {success_rating}/10")
return execution_result
# Test the memory agent
if __name__ == "__main__":
agent = MemoryAgent()
# Test multiple queries to see learning in action
queries = [
"Help me plan a healthy weekly meal prep routine",
"Create a study schedule for learning Python programming",
"Design a morning routine to improve productivity"
]
for query in queries:
print("\n" + "="*60)
agent.process_with_memory(query)
print("="*60)🛠️ Complete Working Example
Here's a simplified but complete example that demonstrates all the key concepts:
# complete_agent.py
import os
from dotenv import load_dotenv
from openai import OpenAI
import time
load_dotenv()
class CompleteAgenticAgent:
def __init__(self):
self.client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
def call_llm(self, prompt):
"""Make API call to OpenAI"""
response = self.client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}],
temperature=0.7
)
return response.choices[0].message.content
def run_agentic_workflow(self, user_query):
"""Complete agentic workflow in one method"""
print(f"🚀 Starting agentic workflow for: {user_query}\n")
# Step 1: Planning
print("🧠 PLANNING PHASE")
plan_prompt = f"Break down this task into 3 specific steps: {user_query}"
plan = self.call_llm(plan_prompt)
print(f"Plan created:\n{plan}\n")
# Step 2: Execution
print("⚡ EXECUTION PHASE")
execution_prompt = f"""
Original task: {user_query}
Plan to execute: {plan}
Now execute each step of this plan thoroughly and provide detailed results.
"""
execution_results = self.call_llm(execution_prompt)
print(f"Execution results:\n{execution_results}\n")
# Step 3: Reflection
print("🤔 REFLECTION PHASE")
reflection_prompt = f"""
Original task: {user_query}
Plan: {plan}
Results: {execution_results}
Analyze: Did we successfully complete the original task? What could be improved?
"""
reflection = self.call_llm(reflection_prompt)
print(f"Reflection:\n{reflection}\n")
# Step 4: Final Response
print("✅ FINAL RESPONSE")
final_prompt = f"""
Provide a comprehensive final answer for: {user_query}
Base your answer on:
- Plan: {plan}
- Results: {execution_results}
- Reflection: {reflection}
"""
final_response = self.call_llm(final_prompt)
print(f"Final response:\n{final_response}")
return final_response
# Run the complete example
if __name__ == "__main__":
agent = CompleteAgenticAgent()
# Test with a business question
query = "I want to start a food truck business. Help me analyze the opportunity and create a launch plan."
agent.run_agentic_workflow(query)🔄 Process Flow Diagram
Here's how the agentic workflow process works:
AGENTIC AI WORKFLOW - TEXT-BASED FLOWCHART
===========================================
┌─────────────────┐
│ User Input │
│ (Query/Task) │
└─────────┬───────┘
│
▼
┌─────────────────┐
│ Agent Planning │
│ (Break down │
│ complex task) │
└─────────┬───────┘
│
▼
┌─────────────────┐
│ Tool Selection │
│ (Choose APIs │
│ and tools) │
└─────────┬───────┘
│
▼
┌─────────────────┐
│ Execution │
│ (Make API calls │
│ & process data) │
└─────────┬───────┘
│
▼
┌─────────────────┐
│Response │
│Generation │
│(Generate final │
│ response) │
└─────────┬───────┘
│
▼
┌─────────────────┐
│ Output to User │
│ (Final result) │
└─────────────────┘✅ Example Input and Output
Input:
"Help me create a marketing strategy for a new fitness app targeting busy professionals"Agent Planning:
1. Research the target market (busy professionals and their fitness needs)
2. Analyze competitor fitness apps and their marketing approaches
3. Identify key value propositions for busy professionals
4. Create specific marketing channels and tactics
5. Develop a timeline and budget frameworkTool Usage:
🔍 Web Search: Researching fitness app market trends...
📊 Data Analysis: Analyzing busy professional demographics...
✍️ Content Generation: Creating marketing strategy document...Final Output:
# Marketing Strategy for Professional Fitness App
## Target Market Analysis
Busy professionals (25-45) earning $50K+ annually, working 45+ hours/week...
## Key Value Propositions
- 15-minute workouts that fit into busy schedules
- No gym equipment required
- Progress tracking integrated with calendar apps
## Marketing Channels
1. LinkedIn advertising targeting specific job titles
2. Partnerships with corporate wellness programs
3. Content marketing focused on productivity benefits
## 90-Day Launch Timeline
Month 1: Beta testing with 100 professionals
Month 2: Influencer partnerships and PR campaign
Month 3: Full launch with corporate sales outreach
## Budget Allocation
- Digital advertising: 40% ($8,000)
- Content creation: 30% ($6,000)
- Influencer partnerships: 20% ($4,000)
- Tools and software: 10% ($2,000)
🚀 Advanced Features and Next Steps
Multi-Agent Collaboration
Once you master single-agent workflows, you can create multi-agent systems where different agents specialize in different tasks:
class AgentTeam:
def __init__(self):
self.research_agent = ResearchAgent()
self.analysis_agent = AnalysisAgent()
self.writing_agent = WritingAgent()
self.coordinator = CoordinatorAgent()
def collaborative_workflow(self, complex_task):
# Coordinator assigns tasks to specialized agents
plan = self.coordinator.create_team_plan(complex_task)
# Each agent works on their specialty
research_data = self.research_agent.gather_information(plan.research_tasks)
insights = self.analysis_agent.analyze_data(research_data)
final_report = self.writing_agent.create_document(insights)
return final_reportIntegration with Real APIs
For production systems, you'll want to integrate with real APIs:
# Real tool integrations
def real_web_search(query):
# Use Tavily, Serper, or Google Custom Search API
response = requests.get(f"https://api.tavily.com/search?q={query}")
return response.json()
def real_data_analysis(data):
# Use pandas, numpy, or specialized analytics APIs
import pandas as pd
df = pd.DataFrame(data)
return df.describe()
def real_content_generation(prompt):
# Use specialized models like Claude for writing
# or DALL-E for images
passPerformance Monitoring
Track your agent's performance:
class AgentMonitor:
def track_performance(self, query, response_time, success_rate):
metrics = {
'timestamp': datetime.now(),
'query_complexity': len(query.split()),
'response_time': response_time,
'success_rate': success_rate
}
# Store in database or analytics platform
def get_performance_insights(self):
# Analyze patterns in agent performance
return "Agent performs best on queries with 10-20 words..."🎯 Summary
Congratulations! You've learned how to build agentic AI workflows that can:
✅ Plan autonomously - Break down complex tasks into manageable steps
✅ Execute systematically - Work through plans step-by-step
✅ Use tools intelligently - Select and use appropriate APIs and services
✅ Reflect and improve - Analyze results and learn from experience
✅ Handle complex queries - Go far beyond simple question-answering
Key Differences from Traditional AI
===========================
TRADITIONAL vs AGENTIC WORKFLOWS
Traditional Workflow | Agentic Workflow
Fixed steps | Dynamic planning
Human intervention required | Autonomous execution
Rule-based decisions | AI-driven decisions
Single API call | Multiple API calls
Static responses | Adaptive responses
What's Next?
Experiment with different types of queries to see how your agent adapts
Add real APIs like web search, data analysis tools, or specialized services
Build multi-agent systems where different agents specialize in different tasks
Implement better memory using vector databases or persistent storage
Add safety measures like input validation and output filtering
Deploy to production using frameworks like FastAPI or Flask
The future of AI is agentic, and you're now equipped to build systems that don't just respond to queries but actively solve complex problems autonomously. These skills will become increasingly valuable as businesses adopt AI agents for everything from customer service to business analysis to creative work.
Start simple, experiment often, and gradually add more sophisticated features as you become more comfortable with the patterns. The key is understanding that agentic AI is about giving AI systems the ability to think, plan, and act - not just respond.
Comments