What Is an AI Agent and How It Works: A Complete Guide

AI agents help us more and more in our daily lives. From a toaster or innovative lighting system to virtual assistants, robotics, and autonomous vehicles, they help us with simple to complex tasks. This technology is relatively new, though it is developing in leaps and bounds.

There are staggering numbers in the AI agent market. As per forecasts, this industry is expected to grow from $7.84 billion in 2025 to $52.62 billion by 2030, at a CAGR of 46.3% during this period. Many developers build and experiment with AI agents using the sophisticated tools that are available today.

In this post, we provide a high-level overview of this complex and exciting topic. Our experts touch on what AI agents are, how they differ, and the tools you can use to build your own.

A brief AI agent definition

AI agents are not a recent invention; they have been explored since at least 2005. A breakthrough became possible thanks to key advancements. Chief among them was the rise of Large Language Models (LLMs). They turned AI agents from simple chatbots to advanced software capable of human-like actions.

There is no standardized definition of AI agents. Yet, there are specifications that key AI organizations, such as OpenAI, Hugging Face, and Anthropic, use. These definitions have several key aspects in common that define an AI agent.

So, what is an autonomous agent? The key component is that it is a system or software that accomplishes simple or complex tasks. Second, an agent uses specific tools to perform its tasks. Third, an agent can be based on an LLM.

As Anthropic mentions, agents’ important characteristic is that they work dynamically. It’s not just prescribed commands that they perform. Intelligent agents in AI handle their processes, including how they use tools and accomplish tasks. Moreover, agents evolve based on experience, continuously improving performance.

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Benefits of using AI agents in SaaS and marketplaces

As AI agents are capable of performing multiple tasks promptly, they are invaluable for digital platforms. Here is how they enhance marketplaces and SaaS solutions:

• Enhanced operational efficiency. Data entry, customer support, workflow management, and other routine tasks are performed by AI agents. So, your team can focus on more strategic problems while AI-powered automation handles the routine.
• Personalized customer experiences. AI for marketplace platforms analyzes user behavior and preferences. Thus, they can deliver tailored recommendations and interactions. Personalization positively influences your user experience and conversions.
• Scalability and flexibility. With AI agents as smart assistants for startups and established enterprises, you can scale your operations without involving additional human work. Even if your business workload grows, AI agents can handle it with consistent performance at peak times.
• Data-driven decision making. By processing vast amounts of data, AI agents provide you with actionable insights. They are capable of forecasting market trends, identifying opportunities, and more to help your business evolve.
• Cost reduction. As AI agents automate tasks, businesses require less human labor, resulting in significant cost reductions. These resources can be allocated to other areas that help you innovate and evolve.

The core components of AI agents

AI agents range from simple systems that can turn utensils on and off to advanced agents that help perform a wide range of tasks, from simple to creative. Based on the AI agent architecture, we can outline several groups of its components.

Physical interface components

These elements are commonly used in appliances, robots, and other instrumental agents.

• Sensors. These devices or modules help agents collect data from the environment. They include cameras, microphones, and data feeds. For example, a toaster turns on and off depending on the sensed temperature.
• Actuators. These are mechanisms found in robots and utensils with displays that perform specific actions.

Cognitive processing components

Intelligence is what makes decision-making in AI agents possible. There are several necessary components to help them make decisions:

• Percepts. These are inputs perceived by an AI agent at a given moment. Percepts give information to the agent, which is then processed to act accordingly.
• Agent program. This component helps agents process percepts and determine appropriate actions.
• Performance element. Selects and executes actions based on the processed percepts.

Learning and adaptation components

These are components of agentic behavior that actually determine the nature of AI agents. They can learn and adapt to the environment and new experiences.

• Learning element. Thanks to this component, AI agents evolve over time.
• Critic. It evaluates the agent’s actions and suggests feedback, typically in the form of rewards or penalties.
• Problem generator. Suggests exploratory actions to help an agent improve learning and discover new strategies.

Environmental context

Agents analyze their environment or the world around them and take respective actions. Upon affecting the environment, advanced agents learn if the strategy worked out and adjust as needed.

Read also: The Role of Generative AI in Marketplace Development

How AI agents work in practice

AI agents base their work on using LLMs, machine learning techniques, and decision-making algorithms. AI agents differ in functionality, yet they have common steps that move them in performing tasks. Let’s take a high-level look at these intricate processes:

• Perception of the environment. An agent receives information about its environment via sensors or from user input. This information, known as percepts, helps an agent understand the current state of its surroundings.
• Data interpretation or processing. This data processing step involves normalizing, cleaning, and transforming the data. With processed data, the agent understands the state of the environment to take the next step.
• Decision making. Based on the understood data, the agent decides which action to take to achieve the goal. This step involves reasoning, planning, and selecting from possible actions.
• Action execution. Based on the decision made, the agent performs the action via actuators or software commands and affects the environment accordingly.
• Learning and improvement. After performing an action, the agent analyzes its benefits based on the critic element. The rewarding or penalizing outcomes help the agent to correct its strategies and improve over time.

For example, a smart home assistant receives a command: turn on the lights. It processes the input and understands that it must send a command to the intelligent lighting system. The agent performs the necessary commands. Considering the wording from a user, the agent understands their preferences and adjusts its commands accordingly in the future.

Types of AI agents and their use cases

AI agents range from simple systems that act based on predefined input to advanced solutions capable of reasoning and learning. There are five main types of agents based on their capabilities. Let’s consider them in more detail.

Simple reflex agents

They act based on predefined rules and do not remember past experiences or future outcomes. AI agent examples in this category include devices like toasters and traffic lights. Reactive agents are practical in environments with predictable conditions or where following strict conditions is necessary.

Model-based reflex agents

In addition to condition-action rules, these agents include an internal model of the environment. This model helps them analyze their past interactions with the environment and correct future actions. For instance, a robot vacuum cleaner knows the obstacles and also the areas it has already passed.

Goal-based agents

These agents extend capabilities compared to previous types. They also make decisions based on inputs from the environment. But also, goal-based AI agents are capable of finding the best strategies to reach their goals.

For example, a robot designed to navigate a building will search for the best routes to reach a specific point within it. These agents are used in robotics, autonomous vehicles, and advanced simulation systems.

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Utility-based agents

These solutions go beyond goal-based agents and tie their actions to the benefits they gain through those actions. They understand the value of their actions and choose the best option available at the time being.

For instance, an agent in a self-driving car weighs options between safety, the best route, and fuel economy. Instead of just reaching the destination, like in goal-based agents, the utility-based agent chooses the option that is most beneficial.

These agents are helpful in dynamic environments, where two-condition decisions may not be sufficient. Regarding the complexity of the decisions, it is challenging to develop such agents, as many factors must be considered for effective decision-making.

Learning agents

These agents can learn from their actions and improve them based on the feedback from the environment. They do not rely on predefined rules; rather, their advanced components allow them to learn and adapt to new circumstances, improving over time.

Learning agents are used for complex tasks. These AI agent use cases include robotics, autonomous driving, and customer support. For example, an agent in reinforcement learning, using machine learning technologies, explores different approaches and receives rewards and penalties for each. This system of confirmation and rejection helps the model choose better options in the future and learn over time.

Multi-agent systems

These systems, as the name suggests, use more than one AI agent to perform tasks. Why is this handy? First, agents break down complex processes into smaller, more manageable subtasks for improved efficiency. Second, the agents are trained in certain processes. That's why dedicating specific tasks to a specialized agent will yield better outcomes.

While high-level agents focus on general tasks, low-level agents perform more specific tasks. They work thanks to orchestration mechanisms that enable them to operate autonomously while achieving shared goals.

For example, multi-agent systems automate tasks in SaaS apps by taking on routine processes and reaching their goals via autonomous task delegation. So, integrations with AI agents allow CRM systems to input and update customer data, like in Salesforce. Also, Slack automates responses to customers, gets insights into customer behavior, creates channels, updates canvases, and much more with AI agents.

Read also: AI Pricing: How Much Does AI Cost in 2025?

Limitations and ethical considerations of AI agents

Though AI agents show excellent potential, there are constraints we should take into account. Besides, ethical considerations regarding agents’ autonomy also arise. When creating or implementing AI agents, consider the following aspects.

Limitations of AI Agents

• Error accumulation in complex tasks. Currently, we see accuracy when AI agents have clean data, clear input, and simple tasks. As complexity grows, AI agents can lose accuracy, which compounds with each step the agent takes.
• Bias and fairness issues. Training AI agents with biased datasets can lead to unfair outcomes, as AI may perpetuate and amplify those biases. It is especially critical for the hiring and lending realms.
• Lack of transparency and explainability. It is not always explainable how AI agents make decisions, which is critical in cases where processes or decisions have significant consequences.
• Security concerns. Like any technology, agents can be targeted by cyberattacks. Moreover, their autonomous nature also poses risks as attackers can make deceitful inputs and cause an agent to take harmful actions. Autonomy must be constrained properly to avoid security risks.

Ethical considerations

Regarding the autonomous nature of AI agents, ethical considerations are also the top priority when implementing them in operations. Their ability to access sensitive data and make decisions must be strictly regulated. Moreover, AI agents blur accountability, so in each case, organizations should understand who is responsible for the actions and outcomes of AI agents.

LLMs work like black boxes, and researchers put their effort into making them interpretable. This will give an understanding of how and why an AI agent makes a specific decision.

Further reading: A Guide to Responsible AI: Best Practices and Examples

Building or integrating AI agents into your startup product

Several components are necessary to develop or integrate AI agents into your product. You can use open-source instruments, APIs, or choose custom development. Let's take a quick look at each of these blocks.

Open source tools and frameworks

Open source frameworks offer flexibility and allow startups to customize their AI agents as needed. Here are several examples of open-source frameworks to speed up your AI agent development:

• LangChain. It is a go-to tool to build apps with LLMs, known for its modular structure and suitability for production pipelines.
• LangGraph. Extends LangChain and coordinates many agents.
• LlamaIndex. Ideal for building intricate AI agents of different levels of complexity.
• CrewAI. Specializes in creating multi-agent teams, with distinct roles for complex workflows.

Pre-trained APIs from OpenAI, Google, or Hugging Face

Organizations like OpenAI, Google, Hugging Face, and Anthropic offer pre-trained APIs. These APIs grant developers access to models trained on vast datasets. Hence, you do not need to build models from scratch, and you still gain access to sophisticated AI functionality. Here are several examples:

• OpenAI's GPT-4 API. Provides enhanced language interpretation and generation capabilities.
• Google Cloud's Vertex AI. Offers a suite of tools for building and deploying AI agents.
• Hugging Face Transformers. Hosts an extensive collection of pre-trained models.
• Anthropic Claude’s APIs. Provide access to LLMs designed to produce and understand human-like text.

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Custom in-house development

Despite the fact that there are many ready-made AI agents today, custom solutions meet your business needs in a more subtle way. Custom agents are designed to meet your organization's specific goals, workflows, and data structures. With a tailored agent, you get deep personalization, seamless integration with your systems, scalability, security, and a competitive edge.

At Codica, we provide custom AI agent development services to help you manage your business tasks with better efficiency. While off-the-shelf solutions are quick to develop, if you plan to scale and want precision in operations, custom AI agents are a go-to solution.

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The future of AI agents

According to IBM’s and Morning Consult’s survey, 99% of 1,000 developers who build AI apps for enterprise explore AI agents. This direction has great promise in terms of solving complex tasks, but requires effort. AI agents already help businesses, but we need breakthroughs in technology to see tangible results.

Major corporations are integrating AI agents into their operations. For instance, Microsoft 365 Copilot features agents for sales, service, and finance. Oracle’s AI agents help organize processes in finance, HR, supply chain management, quality control, and more, enhancing productivity and decision-making.

Platforms, such as LangGraph and Zapier Agents, help you build custom AI agents to automate tasks and improve efficiency in organizations.

However, we should have clear expectations for what AI agents are capable of. Today, they already have decision-making processes and capabilities to help businesses with certain tasks. Yet, for more autonomy, the technology has to mature. We should be aware that it also poses not only opportunities but also challenges. In this regard, compliance and an understanding of the rapid, non-linear advancements in technology are paramount.

To conclude

AI agents are not just a buzzword. They already bring results, though advanced features are still in development. As of now, this software is capable of perceiving input from a user or the environment and making decisions. Then, an agent acts depending on the level of sophistication with which it is equipped.

As a custom software development company, we know the intricacies of developing AI agents tailored to your needs. With our AI development services, we help our customers to tackle tedious tasks and leave space for strategic steps and investments in innovation and business growth.

Check out our portfolio that shows our versatile experience, and feel free to contact us. We would be happy to help you with your project.