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The Future of Automation: Types of AI Agents You Should Know

Covize Solutions > Technology > The Future of Automation: Types of AI Agents You Should Know

The Future of Automation: Types of AI Agents You Should Know

AI Agents are reforming industries by automating tasks, optimising work-flows, and augmenting data-driven decision making capabilities.  These smart systems leverage machine learning and NLP (Natural Language Processing) techniques to process data, engage with users, and respond dynamically to changing conditions.

Take a Scenario: Online Customer Support Chatbot

- Imagine you visit an e-commerce website and see a chat window pop up: Hi! How can I help you today?
You type: Where is my Order?

Here's the AI Agents comes in picture

  • Understands your message using Natual Language Processing (NLP)
  • Fetches order data from the system using your account or order ID
  • Responds with: Your order #4578 is out of delivery and will arrive tomorrow"

If you say: Can I change the delivery address?
It replies: Sure, let me help you with that, and walks you through the steps...

Why this is an AI Agent:

  • It interacts with you like a human
  • It processes language and understands intent
  • It access data, make decisions, and take action
  • It learns from interactions to improve responses over time

Type of AI Agents?

  • Simple Reflex Agent: Simple reflex agents are the most fundamental class of AI agents. They act solely based on the current input, without considering past experiences. Based on If-Then Rules. Their main limitation is the inability to adapt or handle new or unexpected situations. Example: A thermostat turning on/off based on room temperature.

  • Model-Based Reflex Agents: These are smarter than simple reflex agents because they remember things. They don't just react to the current input - they also keep track of what happened before. This memory helps them make better decisions, especially when they can't see the whole environment at once. Example: A robot vacuum that remembers which parts of the room it has already cleaned. If it sees an obstacle, it avoids it and continues cleaning the areas it missed ó because it remembers where it has been.

  • Goal Based Agents: Goal-based agents are designed to achieve specific objectives by planning and taking actions that lead to the desired outcome. They use search and planning algorithms to find action sequences that lead to their goals. Benefit is flexible decision making based on goal evaluation. Example: A self-driving car planning the best route to reach its destination.

  • Learning Agent: A learning agent is an Artificial Intelligence system that enhances its performance over time by interacting with it's environment and gaining knowledge from past experiences. Benefit is adaptable, can improve without human reprogramming. Example ChatGPT learning from conversation patterns to give better answers.

  • Utility Based Agent: lity based agent is an advanced type of AI agent that make decisions by evaluating all possible outcomes of its actions and choosing the one that gives the highest overall utility - which means the most beneficial or preferred result. With the Utility function, we deals with Happy and Unhappy States.

  • Hierarchical agents:
    Hierarchical agents are structured in a tiered system, where higher-level agents manage and direct the actions of lower-level agents. This architecture breaks down complex tasks into manageable subtasks, allowing for more organized control and decision-making.

  • Multi-agent system (MAS): A multi-agent system involves multiple autonomous agents interacting within a shared environment, working independently or cooperatively to achieve individual or collective goals.