Figure 1.1 – Simple Reflex Workflow Agent Architecture<\/figcaption><\/figure>\nUse cases of simple reflex AI Agents<\/strong><\/p>\n\nMotion-activated lighting which turn on and off learning occupancy by sensors.<\/li>\n Thermostats that detect real-world atmosphere to switch heating off\/cooling on.<\/li>\n Traditional assembly line machine which stops operation in full if fault detection is observed.<\/li>\n<\/ul>\n<\/span>2. Model Reflex Agent<\/span><\/h3>\nModel reflex agent have internal memory<\/strong> in its architecture which makes it better than simple reflex model. The internal model has memory that act as a database to store<\/strong> and retrieve<\/strong> information in a state manner.<\/p>\nIt can handle situation where the current input is not enough. Also, determine from past interaction<\/strong> or previous state to provide better action<\/strong> for a particular task.<\/p>\nFor Example., If you said agent to call you Aarohi\u2026 from the next moment, it remember your name and interact with you referring your name.<\/span><\/em><\/p>\nFigure 1.2 – Model Reflex Workflow Agent Architecture<\/figcaption><\/figure>\nUse cases of model reflex AI Agent<\/strong><\/p>\n\nGiving command to Alexa to play song or a particular task process information using reflex model.<\/li>\n Examine deviations in production comparing real-time data to control product quality.<\/li>\n Ring doorbell cameras recognizes people vs packages to decides whether to alert.<\/li>\n<\/ul>\n<\/span>3. Goal-Based Agent<\/span><\/h3>\nGoal-based agent are smarter because they can think, plan, and react intelligently to achieve the end objective or task or goal. While they think before reacting, these agents can planning necessary structure to complete the goal.<\/p>\n
On top of that, these agents can reconstruct structure<\/strong> if the previous state of action fails to complete the goal. Hence, making it flexible for a goal oriented tasks.<\/p>\nThey explicitly ask questions like \u2018what to do to solve the situation?\u2019<\/strong><\/em> or \u2018what to do to get closer to the goal?\u2019<\/strong><\/em><\/p>\nFor Example., GPS system that guides you the best route to reach the destination.<\/span><\/em><\/p>\nFigure 1.3 – Goal Based Agent Workflow Architecture<\/figcaption><\/figure>\nUse cases of goal-based AI Agent<\/strong><\/p>\n\nAutonomous robots in industries to decide how to navigate products in dynamic environment.<\/li>\n Map-based applications that calculates routes to help user to reach to the destination.<\/li>\n Evaluate inventory levels and inform respective departments with necessary information.<\/li>\n<\/ul>\n<\/span>4. Utility-Based Agent<\/span><\/h3>\nThe utility agent is also a goal-based agent but with a smart optimization technique. These agent aims to improve decision making through a quantitative measure<\/strong> (utility score) where each attribute have a number and highest number represent a greater preference.<\/p>\nUtility score is calculated using the mathematical expression. These agents develop utility score<\/strong> towards each possible performance and pick the one with the highest score.<\/p>\nFor Example., An AI agent playing chess and figuring out best move using utility score to finish the playing quickly.<\/span><\/em><\/p>\nFigure 1.4 – Utility AI Agent Workflow Architecture<\/figcaption><\/figure>\nUse cases of utility based AI Agent<\/strong><\/p>\n\nTraining bots in the game to improve its combat skills or particular task.<\/li>\n Balancing delivery speed vs cost or optimizing inventory level vs demand satisfaction.<\/li>\n Calculating mathematical expression or where reasoning are required to get alternative solutions.<\/li>\n<\/ul>\n<\/span>5. Reinforce Learning Agent<\/span><\/h3>\nReinforcement learning agent is a type of agent that learn and improve it response over time. Unlike utility based agent, these agent aims to develop more accurate<\/strong> response<\/strong> through feedback mechanism.<\/p>\nAdditionally, the mechanism have systematic architecture flow<\/strong> where it iterate response through learning element and critics evaluation.<\/p>\nFor Example., An AI Agent playing multiple game but unfortunately keep loosing. After couple of days, it improve its combat skill and winning, thanks to feedback mechanism for iteration.<\/span><\/em><\/p>\nFigure 1.5 – Reinforce Learning Agent Workflow Architecture<\/figcaption><\/figure>\nUse cases of reinforce learning AI Agent<\/strong><\/p>\n\nMovies recommendation on Netflix to improve future predictions.<\/li>\n Drones uses mix of utility based as well as reinforce learning agent to improve aerial tasks.<\/li>\n Improve patient health condition outcomes over time.<\/li>\n<\/ul>\n<\/span>6. Multi-Agent System<\/span><\/h3>\nA Multi-Agent System is a group of AI Models that are excel in their respective tasks. They are trained on huge corpus and fine-tuned for specific task like text generation, image generation, reasoning, coding and so on.<\/p>\n
These agents can cooperate<\/strong>, compete<\/strong>, and work<\/strong> in a parallel toward specific goal. They can bring scalability, flexibility, and resilience to Artificial Intelligence.<\/p>\nFor Example., Robotic Vehicle Coordination to guide best route to the destination by scraping traffic, weather, and road condition. Ecommerce recommendation where one agent analyzes user behavior, another recommends product, another monitors stock levels, and another handles pricing.<\/span><\/em><\/p>\nFigure 1.6 – Multi Agent System Architecture<\/figcaption><\/figure>\nUse cases of multimodal AI Agent<\/strong><\/p>\n\nVirtual shopping assistant where customer input forms of data to get recommendation quickly.<\/li>\n Diagnosis suggestions and treatment plans based on the sensitive data of patient.<\/li>\n Personalized recommendations in app or voice guidance in-store kiosks used by Walmart.<\/li>\n<\/ul>\n<\/span>Methods To Build AI Agents For Your Business<\/strong><\/span><\/h2>\nLow-code AI Agents and Full-code AI Agents are two methods for the development of AI Agents. While low-code platforms are gaining huge traction among individuals whereas full-code development consider as an advanced method to create custom, domain-specific AI Agents.<\/p>\n
In this section, we\u2019ll understand both methods in detail.<\/mark><\/p>\n<\/span>A. Low-Code AI Agents