According to Harvard Business Review, an outstanding 99% of survey participants said that "investments in data and AI are a top organizational priority." Moreover, the World Economic Forum states that "AI agents could be worth $236 billion by 2034."
It's undeniable the power of Artificial Intelligence agents for business and team operations, decision-making and customer service. Let's break down the types of AI agents, how they work and when to use each to build smarter business processes!
At its core, an AI agent is a software system that uses AI to make decisions and take actions to achieve a specific, environment-based goal, such as responding to user inputs, accessing databases or interacting with digital interfaces.
These agents combine areas such as Generative AI, Machine Learning and Natural Language Processing to assess context, reason through options and act independently, adapting their decisions to real-time data and constraints. This process relies on multimodal capabilities, which allow models to process information from multiple data types, such as text, audio, images, video or other forms of sensory input. Some edges of Multimodal AI include:
Examples of AI agents include customer support agents that monitor incoming tickets, analyze sentiment and automatically respond or escalate to a human. Product analytics AI agents can also analyze user behavior data and identify patterns to select recommendations. In design and development operations, AI agents can generate variations and run usability checks, continuously adapting based on user, system or data feedback.
Simple Reflex Agents (SRAs) respond to the current state of their environment using predefined condition–action rules to determine what to do next. Under a specific condition, a defined action is triggered immediately, regardless of past events or future outcomes. Inputs are evaluated against a fixed set of rules to trigger actions.
Examples include thermostats that activate heating when temperatures drop and robotic arms when moving an object to its designated position. AI chatbots follow this pattern when responding to specific keywords with predefined replies, such as "hi" for "hello."
SRAs are easy to implement and reliable when rules are clear and conditions are predictable. However, they can repeat the same mistakes and fail to adapt to changing conditions or incomplete inputs because they lack memory and learning capabilities.
MBRA (Model-Based Reflex Agents) incorporate an internal world model that stores memories and knowledge bases, thereby extending the capabilities of Simple Reflex Agents. Instead of reacting solely to current input, these agents maintain an internal state that represents what they know about the environment, enabling them to make decisions when the environment is partially observable or when data is unavailable.
When a model-based reflex agent receives new input, it updates its internal state using its model that represents how the world works and how it changes. It then applies condition–action rules, not only to the current percept but to the combination of real-time input and stored context.
A robot vacuum, such as a modern Roomba, can use model-based reflex agents to remember a room's layout and track which areas have already been cleaned, avoiding unnecessary repetition. Similarly, an LLM-powered conversational agent can maintain context across turns, remembering prior user inputs to produce coherent responses.
Goal-based agents rely on search and planning mechanisms to evaluate which actions or action sequences are most likely to help them achieve a defined goal. In this journey, agents explore possible future states, assess the consequences of different actions and select the path that best aligns with their objective.
If conditions change or new information becomes available, goal-based agents can re-plan and adjust their strategy. This forward-looking approach makes goal-based agents more flexible than reflex-based systems, especially in dynamic or unfamiliar environments.
Goal-based agents introduce explicit goals and planning to anticipate future states. Examples include navigation systems that calculate optimal routes to a destination and robotics agents that plan paths through an environment.
Utility-based agents choose actions based on which outcome is expected to deliver the highest overall utility, focusing on achieving a goal while maximizing value. This value can be a quantitative measure of how desirable or beneficial a result is, based on predefined preferences or priorities.
Instead of asking "Did I reach the goal?", Utility-based agents ask "Which possible outcome is best?" The answer relies on a function, a mathematical model that scores potential actions based on factors such as cost, risk, time, reward or impact. Utility-based agents can then operate effectively in situations with multiple valid paths or limited resources.
A sales chatbot may prioritize leads based on their likelihood to convert and potential deal size. At the same time, a stock trading bot would continuously balance risk and return to optimize long-term gains. In both cases, success depends on choosing the best option, not just a viable one.
MAS (Multi-Agent Systems) are composed of multiple AI agents that interact with one another to achieve individual or shared objectives. Each agent operates independently, with its own goals, capabilities and view of the environment, yet contributes to the system's overall behavior.
Depending on their design, agents may collaborate toward a common goal or compete for resources, shifting between cooperation and competition. This structure allows multi-agent systems to tackle problems that are complex, distributed or dynamic for a single AI agent. As a result, MAS is a more advanced, scalable approach to building AI-powered solutions.
MAS's applications appear in domains where coordination matters, such as financial transactions that may involve coordinated invoicing, fraud detection and reporting. In supply chain management, different AI agents can manage inventory levels, shipping logistics and demand forecasting while exchanging information.
Hierarchical agents organize data across multiple decision-making layers to manage complex tasks. Rather than treating every decision at the same level, these agents separate what needs to be achieved from how it should be executed.
Higher layers focus on abstract goals and long-term planning, while lower layers handle concrete actions and real-time responses. A high-level layer might define strategy, priorities or desired outcomes, which are then translated into actionable steps by lower-level layers. This structure enables agents to break complex objectives into smaller, manageable sub-tasks.
Hierarchical agents are effective when tasks span different levels of detail. In manufacturing, a high-level agent could plan an entire assembly process with an intermediate-layer agent managing coordination and a lower-layer agent controlling robotic arms and timing. This Separation of Concerns (SoC) makes the system easier to scale, maintain and extend as new capabilities are added.
Learning agents refine their decision-making based on action or outcome feedback to improve performance over time, making them effective in dynamic environments. To do so, these learning agents work with four interconnected parts.
By leveraging Supervised, Unsupervised or Reinforcement Learning, AI agents can balance what they know with what they still need to discover. As a result, they become better at handling uncertainty, generalizing from past experiences and responding to environmental changes. Examples include trading agents that adjust strategies based on market performance and recommendation systems that refine suggestions based on user queries.
AI agents are autonomous systems that perceive, reason and act within digital environments. Different agent types, ranging from reflex-based to learning and multi-agent systems, serve distinct use cases depending on complexity, adaptability and optimization needs. Selecting the appropriate AI agent architecture is essential for building scalable, intelligent products.
Agentic AI is the foundation of modern products, and the right agent architecture can make or break your product goals, business needs and growth stage. If you want to take full advantage of the advances of different types of AI agents, get in touch! We're the Growth Partner that can guide you through complex decisions with a clarity-first approach to scale without losing value.
According to Harvard Business Review, an outstanding 99% of survey participants said that "investments in data and AI are a top organizational priority." Moreover, the World Economic Forum states that "AI agents could be worth $236 billion by 2034."
It's undeniable the power of Artificial Intelligence agents for business and team operations, decision-making and customer service. Let's break down the types of AI agents, how they work and when to use each to build smarter business processes!
At its core, an AI agent is a software system that uses AI to make decisions and take actions to achieve a specific, environment-based goal, such as responding to user inputs, accessing databases or interacting with digital interfaces.
These agents combine areas such as Generative AI, Machine Learning and Natural Language Processing to assess context, reason through options and act independently, adapting their decisions to real-time data and constraints. This process relies on multimodal capabilities, which allow models to process information from multiple data types, such as text, audio, images, video or other forms of sensory input. Some edges of Multimodal AI include:
Examples of AI agents include customer support agents that monitor incoming tickets, analyze sentiment and automatically respond or escalate to a human. Product analytics AI agents can also analyze user behavior data and identify patterns to select recommendations. In design and development operations, AI agents can generate variations and run usability checks, continuously adapting based on user, system or data feedback.
Simple Reflex Agents (SRAs) respond to the current state of their environment using predefined condition–action rules to determine what to do next. Under a specific condition, a defined action is triggered immediately, regardless of past events or future outcomes. Inputs are evaluated against a fixed set of rules to trigger actions.
Examples include thermostats that activate heating when temperatures drop and robotic arms when moving an object to its designated position. AI chatbots follow this pattern when responding to specific keywords with predefined replies, such as "hi" for "hello."
SRAs are easy to implement and reliable when rules are clear and conditions are predictable. However, they can repeat the same mistakes and fail to adapt to changing conditions or incomplete inputs because they lack memory and learning capabilities.
MBRA (Model-Based Reflex Agents) incorporate an internal world model that stores memories and knowledge bases, thereby extending the capabilities of Simple Reflex Agents. Instead of reacting solely to current input, these agents maintain an internal state that represents what they know about the environment, enabling them to make decisions when the environment is partially observable or when data is unavailable.
When a model-based reflex agent receives new input, it updates its internal state using its model that represents how the world works and how it changes. It then applies condition–action rules, not only to the current percept but to the combination of real-time input and stored context.
A robot vacuum, such as a modern Roomba, can use model-based reflex agents to remember a room's layout and track which areas have already been cleaned, avoiding unnecessary repetition. Similarly, an LLM-powered conversational agent can maintain context across turns, remembering prior user inputs to produce coherent responses.
Goal-based agents rely on search and planning mechanisms to evaluate which actions or action sequences are most likely to help them achieve a defined goal. In this journey, agents explore possible future states, assess the consequences of different actions and select the path that best aligns with their objective.
If conditions change or new information becomes available, goal-based agents can re-plan and adjust their strategy. This forward-looking approach makes goal-based agents more flexible than reflex-based systems, especially in dynamic or unfamiliar environments.
Goal-based agents introduce explicit goals and planning to anticipate future states. Examples include navigation systems that calculate optimal routes to a destination and robotics agents that plan paths through an environment.
Utility-based agents choose actions based on which outcome is expected to deliver the highest overall utility, focusing on achieving a goal while maximizing value. This value can be a quantitative measure of how desirable or beneficial a result is, based on predefined preferences or priorities.
Instead of asking "Did I reach the goal?", Utility-based agents ask "Which possible outcome is best?" The answer relies on a function, a mathematical model that scores potential actions based on factors such as cost, risk, time, reward or impact. Utility-based agents can then operate effectively in situations with multiple valid paths or limited resources.
A sales chatbot may prioritize leads based on their likelihood to convert and potential deal size. At the same time, a stock trading bot would continuously balance risk and return to optimize long-term gains. In both cases, success depends on choosing the best option, not just a viable one.
MAS (Multi-Agent Systems) are composed of multiple AI agents that interact with one another to achieve individual or shared objectives. Each agent operates independently, with its own goals, capabilities and view of the environment, yet contributes to the system's overall behavior.
Depending on their design, agents may collaborate toward a common goal or compete for resources, shifting between cooperation and competition. This structure allows multi-agent systems to tackle problems that are complex, distributed or dynamic for a single AI agent. As a result, MAS is a more advanced, scalable approach to building AI-powered solutions.
MAS's applications appear in domains where coordination matters, such as financial transactions that may involve coordinated invoicing, fraud detection and reporting. In supply chain management, different AI agents can manage inventory levels, shipping logistics and demand forecasting while exchanging information.
Hierarchical agents organize data across multiple decision-making layers to manage complex tasks. Rather than treating every decision at the same level, these agents separate what needs to be achieved from how it should be executed.
Higher layers focus on abstract goals and long-term planning, while lower layers handle concrete actions and real-time responses. A high-level layer might define strategy, priorities or desired outcomes, which are then translated into actionable steps by lower-level layers. This structure enables agents to break complex objectives into smaller, manageable sub-tasks.
Hierarchical agents are effective when tasks span different levels of detail. In manufacturing, a high-level agent could plan an entire assembly process with an intermediate-layer agent managing coordination and a lower-layer agent controlling robotic arms and timing. This Separation of Concerns (SoC) makes the system easier to scale, maintain and extend as new capabilities are added.
Learning agents refine their decision-making based on action or outcome feedback to improve performance over time, making them effective in dynamic environments. To do so, these learning agents work with four interconnected parts.
By leveraging Supervised, Unsupervised or Reinforcement Learning, AI agents can balance what they know with what they still need to discover. As a result, they become better at handling uncertainty, generalizing from past experiences and responding to environmental changes. Examples include trading agents that adjust strategies based on market performance and recommendation systems that refine suggestions based on user queries.
AI agents are autonomous systems that perceive, reason and act within digital environments. Different agent types, ranging from reflex-based to learning and multi-agent systems, serve distinct use cases depending on complexity, adaptability and optimization needs. Selecting the appropriate AI agent architecture is essential for building scalable, intelligent products.
Agentic AI is the foundation of modern products, and the right agent architecture can make or break your product goals, business needs and growth stage. If you want to take full advantage of the advances of different types of AI agents, get in touch! We're the Growth Partner that can guide you through complex decisions with a clarity-first approach to scale without losing value.
