An AI agent is a software program that can interact with its environment, gather data, and use that data to achieve predetermined goals. AI agents can choose the best actions to perform to meet those goals.
An agent can perform autonomous actions without constant human intervention. Also, they can have a human in the loop to maintain control.
- Agents have a memory to store individual preferences and allow for personalization. It can also store knowledge. An LLM can undertake information processing and decision-making functions.
- Agents must be able to perceive and process the information available from their environment.
- Agents can also use tools such as accessing the internet, using code interpreters and making API calls.
- Agents can also collaborate with other agents or humans.
Multiple types of AI agents are available such as learning agents, simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.
A system with AI agents can be built with different architectural approaches.
1 - Single Agent: Agents can serve as personal assistants.
2 - Multi-Agent: Agents can interact with each other in collaborative or competitive ways.
3 - Human Machine: Agents can interact with humans to execute tasks more efficiently.
The agent function describes how the data collected is translated into actions that support the agent’s objective. When designing the agent function, developers consider the type of information, AI capabilities, knowledge base, feedback mechanism, and other technologies required.
An agent program is the implementation of the agent function. It involves developing, training, and deploying the AI agent on the designated architecture. The agent program aligns the agent’s business logic, technical requirements, and performance elements.
AI agents work by simplifying and automating complex tasks. Most autonomous agents follow a specific workflow when performing assigned tasks.
The AI agent receives a specific instruction or goal from the user. It uses the goal to plan tasks that make the final outcome relevant and useful to the user. Then, the agent breaks down the goal into several smaller actionable tasks. To achieve the goal, the agent performs those tasks based on specific orders or conditions.
AI agents need information to act on tasks they have planned successfully. For example, the agent must extract conversation logs to analyze customer sentiments. As such, AI agents might access the internet to search for and retrieve the information they need. In some applications, an intelligent agent can interact with other agents or machine learning models to access or exchange information.
With sufficient data, the AI agent methodically implements the task at hand. Once it accomplishes a task, the agent removes it from the list and proceeds to the next one. In between task completions, the agent evaluates if it has achieved the designated goal by seeking external feedback and inspecting its own logs. During this process, the agent might create and act on more tasks to reach the final outcome.
A simple reflex agent operates strictly based on predefined rules and its immediate data. It will not respond to situations beyond a given event condition action rule. Hence, these agents are suitable for simple tasks that don’t require extensive training. For example, you can use a simple reflex agent to reset passwords by detecting specific keywords in a user’s conversation.
A model-based agent is similar to simple reflex agents, except the former has a more advanced decision-making mechanism. Rather than merely following a specific rule, a model-based agent evaluates probable outcomes and consequences before deciding. Using supporting data, it builds an internal model of the world it perceives and uses that to support its decisions.
Goal-based agents, or rule-based agents, are AI agents with more robust reasoning capabilities. Besides evaluating the environment data, the agent compares different approaches to help it achieve the desired outcome. Goal-based agents always choose the most efficient path. They are suitable for performing complex tasks, such as natural language processing (NLP) and robotics applications.
A utility-based agent uses a complex reasoning algorithm to help users maximize the outcome they desire. The agent compares different scenarios and their respective utility values or benefits. Then, it chooses one that provides users with the most rewards. For example, customers can use a utility-based agent to search for flight tickets with minimum traveling time, irrespective of the price.
A learning agent continuously learns from previous experiences to improve its results. Using sensory input and feedback mechanisms, the agent adapts its learning element over time to meet specific standards. On top of that, it uses a problem generator to design new tasks to train itself from collected data and past results.
Hierarchical agents are an organized group of intelligent agents arranged in tiers. The higher-level agents deconstruct complex tasks into smaller ones and assign them to lower-level agents. Each agent runs independently and submits a progress report to its supervising agent. The higher-level agent collects the results and coordinates subordinate agents to ensure they collectively achieve goals.
Links:
https://www.ibm.com/think/topics/ai-agents
https://aws.amazon.com/what-is/ai-agents/
https://toloka.ai/blog/10-ai-agent-types-powering-intelligent-systems-in-2025/