Agentic AI: The Future of Independent and Purposeful Intelligence
Artificial intelligence (AI) is growing fast, and “Agentic AI” is a new type that’s changing how we think about technology. It focuses on being independent, flexible, and purposeful. But what does it mean? Let’s explore Agentic AI, its uses, and its future, with a simple example to explain.
What is Agentic AI?
Agentic AI is smarter than regular AI. Instead of just following instructions or crunching numbers, it works toward goals on its own. It can make its own decisions, adapt to changes, and get things done — all without needing much help from humans.
Imagine an AI that solves problems by itself, keeps learning, and always works to meet its goals. That’s Agentic AI!
Key Features of Agentic AI:
1. Autonomy: Agentic AI can make decisions and take actions on its own, without needing constant human control.
2. Goal-Oriented: It is designed to achieve specific goals, like solving problems or completing tasks, with little help from humans.
3. Learning and Adaptability: Agentic AI can learn from its environment and adjust its behavior based on new information or changes around it.
4. Interactivity: It can communicate and work with other AI or humans, making decisions together in real-time.
How Does Agentic AI Work?
1. Data Input: Agentic AI starts by receiving data from its environment, like sensor readings, user inputs, or online information.
2. Processing and Decision-Making: It processes the data using AI models, looking for patterns, and makes decisions based on its goals and the current situation.
3. Action Execution: Once it decides, Agentic AI takes action, whether adjusting a process, interacting with others, or making a recommendation, all on its own.
4. Feedback and Learning: Agentic AI learns from the results of its actions, improving its decision-making over time based on feedback.
Prerequisites to Work with Agentic AI
Before you get started, ensure you have the following installed:
pip install numpy pandas scikit-learn tensorflow torch pytorch torchvision.
Additionally, make sure you have:
1. A basic understanding of Python programming and OOPS.
2. A database like SQL or SQLite is ready for handling data storage.
3. Access to tools like Jupyter Notebook or VS Code for coding and testing. 4. A GPU is available (if you’re working with deep learning tasks) for faster processing and training.
Example: Financial Document Summarization with Groq for Agentic AI
In this example, we’ll explore how to use Groq and a pre-trained model to automatically summarize financial documents. This task is key for processing complex reports like balance sheets, income statements, and earnings calls.
Step 1: Load a Pre-Trained Model
We’ll use a pre-trained model like T5 for text summarization. Hugging Face’s transformers library makes it easy to load the model and tokenizer.
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_name = “t5-small”
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
Step 2: Preprocess the Financial Document
Next, we clean up the text to prepare it for summarization. Here’s a basic example of cleaning a financial report.
# Sample financial report
financial_report = “””Company XYZ has reported a 12% increase in revenue for Q4, compared to the same period last year. Their total revenue reached $1.5 billion, and net profits rose by 8%. The company attributes the growth to improved sales in their North American region, while the European market remains flat. The balance sheet shows total assets of $8 billion, with liabilities of $3 billion, indicating a strong cash flow position.”””
# Clean up the text
def preprocess_text(text):
return text.strip().replace(“\n”, “ “)
# Preprocess the document
financial_report_cleaned = preprocess_text(financial_report)
Step 3: Summarize the Financial Document
Once the document is cleaned, we can use the T5 model to generate a summary.
# Tokenize the cleaned text
inputs = tokenizer(financial_report_cleaned, return_tensors=”pt”, max_length=512, truncation=True)
# Generate summary using the model
summary_ids = model.generate(inputs[‘input_ids’], max_length=100, num_beams=4, early_stopping=True)
# Decode the generated summary
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) print(“Summary:”, summary)
Step 4: Run the Model on Groq
To speed up the process, especially for large volumes of documents, we can run the model on Groq. Here’s how you can integrate Groq’s device to accelerate the summarization.
import groq
import numpy as np
# Assuming the model has been compiled for Groq hardware
groq_device = groq.Device()
# Run the model on Groq hardware
groq_output = groq_device.run(groq_model, np.array([financial_report_cleaned])) # Print the summarized output from Groq hardware
print(“Summary from Groq hardware:”, groq_output)
Real-World Applications of Agentic AI
Agentic AI is changing many industries. Here are some examples:
1. Self-Driving Cars: Cars like Tesla drive themselves safely using AI.
2. Smart Assistants: Siri and Alexa perform tasks and predict needs automatically. 3. Healthcare: AI helps doctors with better diagnoses and patient monitoring. 4. Finance: AI trades, detects risks, and finds fraud quickly.
5. Customer Support: Chatbots solve customer issues and give suggestions easily.
Challenges and Ethical Considerations
1. Accountability and Transparency: It’s hard to track how AI makes decisions. Transparency is crucial, especially in fields like healthcare and finance.
2. Bias and Fairness: If AI learns from biased data, it can act unfairly. Training it properly is important to avoid discrimination.
3. Job Displacement: AI can replace some jobs, so balancing automation and human work is important.
4. Security Risks: AI systems can be hacked or misused. Keeping them safe and secure is essential, especially in critical areas.
Conclusion and The Future of Agentic AI
Agentic AI is a big step forward, allowing machines to think and act on their own. It helps improve industries by making things faster and smarter. In the future, with advanced technology like Artificial General Intelligence, AI could handle even more tasks with greater flexibility. The possibilities are endless, from changing how businesses work to improving daily life.
Payoda, a globally recognized leader in product engineering and other digital solutions, has a proven track record of successful collaborations with renowned brands. Their expertise and experience in working with global brands have enabled them to understand the nuances of different industries and deliver tailored solutions. By leveraging their extensive knowledge and technical prowess, Payoda helps businesses transform their product ideas into reality, driving growth and market success.
With safe execution, Payoda can help utilize Agentic AI with your solutions, ensuring it is fair, safe, and secure. By using Agentic AI responsibly, it can make life better for end users and bring about a real change.
Author: Ramkumar Anbalagan
