What Are AI Agents? A Beginner’s Guide to Understanding AI Agents

What are AI agents?

AI agents are software programs or systems that work autonomously (independently) to perform tasks on behalf of users or other systems. These agents do more than just assist you; they can work alongside you. It interacts with the environment and collects and utilizes data to perform self-determined tasks to meet predetermined goals.

These AI agents are large language models (LLMs) that rely on machine learning (ML) and natural language processing (NLP) to handle a wide range of tasks. These tasks can vary from simple question answering to complex multitasking.

For example, you can create an agent to know everything about the pricing of the products or services to generate quotes and send them to your customers automatically. You can also build agents to analyze trends, consumer behavior, and buying patterns and create a report to plan your future business sales.

For example, if the user asks the AI agent, “Which week of next year is best to visit Italy?”
To respond to this request, the AI agent uses certain tools, for example, a weather tool to determine the favorable weather to visit.

AI agents store past interactions in memory and plan future actions, offering a personalized user experience.

The key components of an AI agent

Agents can work in different environments to achieve a specific goal. The basic components of an AI agent are:

Large Language Models (LLM)

They are the linguistic powerhouse behind AI agents capable of reading, interpreting, and generating fluent, human-like text. LLMs are responsible for forming a meaningful, intuitive, and human-like conversation. Process of LLMs in AI agents:

• Input processing – The agent analyzes the user’s input and translates it into a form the system can understand.
• Language interpretation – The LLM interprets the user’s language to understand the user’s intentions.
• Generate response – Based on the analysis, LLM generates an appropriate and meaningful response to form a relevant dialogue or complete a task.

Tools integration

This empowers AI agents to connect with digital work and perform technical tasks. Tools integration makes AI agents more than conversational partners that can think, plan, and execute actions, manage information, and drive results. Types of tools integration:

• Data access tools – Enable AI agents to access data from databases, cloud storage, and even local files. It provides data for agents to make informed decisions.
• Communication tools – You can integrate communication abilities into AI agents to perform tasks such as sending emails, messages, or alerts on various platforms. This facilitates prompt and seamless interactions with the users. For example, sending automated emails to the customers with the solution for their query.
• Analytics tools – The agents can process and analyze the data to generate insightful reports and make data-based decisions. For example, generating sales reports and forecasting future demands.
• Automation tools – Enable AI agents to schedule tasks, trigger automated workflows, and manage repetitive tasks, such as data entry. These streamline business operations and enhance efficiency. For example, you can define min/max inventory levels to trigger purchase order creation.

Memory systems

AI agents store user interactions in their memory to update their database and utilize this information across interactions. This ensures a more personalized experience, enhanced user satisfaction, and engagement rate. Agents’ memory storage ability empowers them to offer consistent, personalized, and context-relevant information. Types of AI agent memory:

• Short-term memory – It keeps the current conversation relevant and keeps the interaction logically within a single interaction.
• Long-term memory – Allows AI agents to store memory of multiple interactions, enabling them to remember your preferences and past queries.
• Episodic memory – It only remembers specific events or conversations and allows the agent to remember the past conversations.
• Semantic memory – This memory system has facts and logic to provide and assist you with making informed decisions.

How does an AI agent work?

AI agents simplify complex tasks and follow a specific workflow while performing assigned tasks. Here is the breakdown of how these AI agents work:

Initiating goals and planning

Though AI agents are independent, they need external stimuli to perform a specific task. The user defines the agent’s goals. Then, the agent plans tasks to make the results relevant and useful for the user. AI agents break the task into various subtasks for complex goals.

For example, a user wants to know, “Which is the best time to visit Milford Track, New Zealand for hiking?” The goal is defined by the user to the AI agent.

Data collection

AI agents may not have complete information about the user’s defined goals. This input is sent to LLM for translation into a form the system understands. It accesses various integrated tools or the Internet to fetch information. These tools can be external databases, APIs, web searches, or even other agents. This way, the agent can also update its database for future use.

Referring to the previous example, the agent can not determine the optimal weather conditions for hiking. To do so, it will create a new subtask to know which weather is best for hiking and forecast when you can go. For this, the AI can access integrated tools or the internet and analyze previous weather trends to forecast the time above weather conditions will be met.

Execute learning

The agent can analyze the collected data and present the outcomes to the user. The LLM forms appropriate sentences so that users understand them easily and the data is presented to the user.

For example – Refer to the earlier scenario. In that case, the agents can access weather data to identify and analyze past weather patterns to forecast when you should visit New Zealand to hike at Milford Track. This information is translated into human-like text and presented to the users.

Learn and adapt

AI agents use feedback mechanisms to improve response precision and accuracy. It gathers the learned information from other tools and minimizes the time consumption during future interactions. The users can also provide feedback to align the outcome with intent. An AI agent’s ability to improve its reasoning and accuracy with a feedback mechanism known as iterative refinement (continuous improvement).

AI Agents Architecture

The architecture of AI agents is not limited to one standard architecture and has various architectures to solve multi-step problems.

ReAct (Reasoning + Action) framework

This architecture combines the thinking and reasoning abilities of the LLMs with actionable steps. It directs AI agents to think and plan after each task. Each task defines which tool to use next. It follows a think-act-observe loop for solving problems and improving responses. You can ask the agent to reason slowly and display their thoughts. Their verbal reasoning defines how the responses are generated. The AI agents consistently update their context with new logic.

ReWoo (Reason Without Observation)

ReWoo architecture removes the AI agent’s dependency on output tools for action planning. On the contrary, they plan promptly. It avoids using redundant tools to anticipate which tool to use for initial input from the user. The architecture comprises three modules: a planning module (which plans which tools to use based on users’ prompts), data collection (which collects results produced from the tools), and pairing the initial plan with tool output (to generate a response). This can help you reduce token usage and computation complexities.

Types of AI Agents

Various AI agents are available to solve problems and complete simple to complex tasks. Choosing or developing an AI agent depends on factors and not one agent type can serve all use cases.

Simple reflex agents

These agents are the most basic forms of artificial intelligence. They make decisions based on sensory input and immediately respond to environmental stimuli. The key components of simple reflex agents are sensors, condition-action rules, and actuators. Use cases for simple reflex agents include industrial safety sensors, electronic sensors for lights or ACs, automated smoke detectors and sprinkler systems, and email auto-responders.

Model-based reflex agents

This model-based reflex agent tracks the evolving or changing environment and depends on its model, reflexes, previous precepts, and current state. It can store memory and operate in partially altering environments. The agent consists of a state tracker, a world model, and reasoning as its major components. Model-based reflex agents are used in smart home security systems, quality control systems, and network monitoring tools.

Goal-based agents

These agents are designed to achieve a specific goal based on defined rules or world models. They plan a series of actions using search and planning algorithms to achieve the desired outcome. The key components of goal-based agents include goal, planning, evolution, action selection, and world model. Such agents or robots are typically used in industrial robots and warehouse automation, smart heating systems, inventory management systems, and task scheduling systems.

Multi-agent systems (MAS)

In multi-agent systems, multiple AI agents collaborate within a shared environment, working together or independently to solve a collective problem or complete tasks. They are more focused on simple agents interacting through basic protocols and rules. The types of multi-agent systems include cooperative, competitive, and mixed systems. For example, multiple robots connect to coordinate to move and sort items or manage shared data.

Learning agents

They are artificial intelligence agents that can interact with the environment and grow and improve their behavior over time. Learning agents feed on suggestions and experience to optimize their performance. The key components of learning agents include performance element, critic, learning element, and problem generator. These agents are used for industrial process control, energy management systems, and customer service chatbots.

Utility-based agents

These agents make decisions based on the potential outcomes of their actions and choose the one that maximizes the potential. The key components of utility-based agents are utility function, state evaluation, decision mechanism, and environmental model. Utility-based agents are best suited for resource allocation systems, smart building management, and scheduling systems.

Hierarchical agents

They are AI agents that are structured in a tiered system. In this, the higher-tiered systems manage direct tasks to lower-level agents. It is best suited for complex tasks that break bigger tasks into subtasks. The key components of hierarchical agents are task decomposition, common hierarchy, coordination mechanism, and goal delegation. These agent systems are used in manufacturing control systems, building automation, and robotic task planning.

What are the benefits of using AI agents?

Implementing AI agents into your business will transform your operation, offering various benefits and bringing a new dawn of digital transformation. The benefits of AI agents are:

Improved productivity

AI agents can perform specific tasks without human interference to achieve business goals. They can automate repetitive tasks, like data entry, improving accuracy, enabling businesses to work on more important tasks, and adding value.

Reduced costs

They can mitigate operational inefficiencies, errors, and manual efforts. Agents can perform complex tasks and easily adapt to a consistently evolving environment, minimizing costs and maximizing profits.

Enhanced customer experience

Customers want faster and more personalized query resolution. Using AI agents can help you offer personal recommendations and prompt resolutions and improve customer engagement, conversion rates, and customer loyalty.

Informed decision-making

AI agents can fetch data from various datasets, clouds, or the Internet to analyze it and make informed decisions. Agents can analyze sales for a particular product, assess its performance, and make informed decisions on future product sales.

Difference between AI agents and chatbots

AI agents and chatbots use artificial intelligence (AI) technology to automate a process. Chatbots follow scripted conversations, while AI agents use natural processing language and generative AI and take decision-based actions to solve complex problems.

Real-world AI agent examples

AI agents are transforming how businesses operate and interact with customers. They are independent systems or software programs that perform complex tasks, make data-based decisions, and adapt to evolving environments. Real-world examples of AI agent usage in various industries:

Manufacturing

AI agents can help you optimize the manufacturing process, maintain quality standards of production, and predict equipment maintenance needs. They reduce operational downtime and improve efficiency and productivity. The higher-level agents plan and allocate tasks to lower-level agents to control manufacturing robots. These robots collaborate to maintain seamless coordination.

Retail

AI agents can improve customers’ shopping experience in physical and online retail. They can analyze inventory, customers’ buying patterns, and preferences and provide personalized product recommendations. These agents assist retailers in making data-driven decisions.

Warehouse

AI agents in warehouses are robots that manage and handle inventory using advanced machine learning. Advanced agents can optimize warehouses, sort inventory and distribution, and execute physical tasks. Multiple robots in the warehouse are connected for a seamless warehousing process.

Supply chain and logistics

In a supply chain, AI agents are the stakeholders (manufacturers, distributors, and retailers). The AI agents can collaborate and coordinate to initiate a seamless and optimized supply chain process, from procurement to deliveries.

How can Master Software Solutions help with your AI agent requirements?

Master Software Solutions is a digital transformation company, specializing in ERP systems and AI technology. We provide consultation and custom AI agent development for task automation, operational optimization, real-time data processing, and smart customer interaction. We can help you develop ERP AI agents to improve accuracy and precision. Schedule a call and discuss your business use case to see AI possibilities and how we can help.