Types of AI agents and which one to choose for your organisation

From tech giants to startups, everyone seems to be entering the AI race. We’re no exception, with our sovereign AI platform and 14 new AI agents .

Indeed, at the heart of this trend are AI agents. According to IBM, 99% of developers are already working on AI agents or have plans to bring them into their workflows soon.

AI agents vary in autonomy, intelligence, and how they interact with their environment. However, choosing the wrong type can limit results and create inefficiencies. That’s why, after considering your infrastructure and data, a clear understanding of agent types is an important step towards incorporating them into your workflows.

Several factors must be considered, including the desired outcome, efficiency, cost, scalability, and potential risks — we’ll explore these in the following sections.

Here are six types of AI agents worth considering:

1. Simple reflex AI agents

A simple reflex AI agent is the most basic form of AI. It operates strictly on the condition-action principle, responding to current perceptions using a fixed set of predefined rules. It follows an “if-then” logic: "If you encounter (condition), do (action)."

The actions of simple reflex agents are purely reactive and based only on immediate sensory inputs. With no regard for past experiences or future outcomes, this makes them better suited for structured, predictable environments where conditions and responses are clearly defined. For example, to track time and attendance.

Let’s explore further: A clocking system, like the one we launched as part of the OneAdvanced People Management Portfolio, can log shifts in employee attendance immediately after an ID or fingerprint is presented. While it responds only to the input fed to it, this helps deliver precise, tamper-proof attendance records, ensuring unbiased workforce accountability.

Components and operation of a simple reflex agent

Sensors
↓ (Perceives the current state of the environment)
Condition–action rules
↓ (Matches current input to predefined action)
Actuators → (Execute the selected action in the environment)

Benefits of simple reflex agent

• Easy to design and implement
• Doesn’t require extensive training or tuning
• Low computational needs, enabling fast response and cost-efficiency
• Highly predictable and reliable performance

Limitations of simple reflex agent

• Cannot adapt; not suitable for dynamic, unexpected, or complex tasks
• Lacks reasoning, planning, and decision-making abilities
• Lacks learning ability and context awareness, leading to repeated errors
• May enter infinite loops in partially observable settings

2. Model-based reflex AI agents

A model-based AI agent is a more advanced iteration of the simple reflex agent. It maintains an internal model of the environment but can represent and track elements beyond that, such as past events or inferred states. This allows the agent to interpret current percepts (stored knowledge) in context - inferring hidden information, and reasoning about environmental changes over time.

For example, a robot in a warehouse adjusting its path in response to obstacles.

Components and operation of a model-based reflex agent

Sensors
↓ (Perceive the current state of the environment)
Internal model
↓ (Updates with perceived information)
Reasoning component
↓ (Determines the suitable action based on current context and stored knowledge)
Actuators → Execute the chosen action in the environment

Benefits of model-based reflex agent

• Suitable for partially observable environments
• Adapts to dynamic conditions
• Predicts future states linked to actions, improving planning and outcomes
• Uses contextual understanding for more accurate responses

Limitations of model-based reflex agent

• Complex to design, implement, and maintain
• High resource, computational, and cost requirements
• Dependent on model accuracy; errors may lead to flawed decisions
• Cannot learn or improve performance autonomously

3. Goal-based AI agents

Goal-based agents make autonomous decisions to achieve specific objectives. Using planning and reasoning abilities, they evaluate possible actions before execution. Progress is continuously monitored to stay aligned with the goal and adapt as needed to achieve it. For example, AI agents in autonomous vehicles ensure safe and efficient operations based on dynamically changing conditions on the road.

Components and operation of a goal-based agent

Perception module
↓ (Collects current state of the environment via sensors)
Knowledge base
↓ (Stores environment’s information, current state, facts, rules, and past experiences)
Goal
↓ (Defines desired objective to be achieved)
Reasoning/decision-making module
↓ (Assesses current state, compares with goal, predicts outcomes and selects suitable action)
Planning module
↓ (Develops sequence of actions to reach the goal; includes contingencies)
Execution module
↓ (Execute actions using actuators, monitors outcome, provides feedback and adjusts behaviour as needed)

Benefits of goal-based agent

• Autonomous with minimal human input
• Handles complex tasks through strategic reasoning
• Goal-focused behaviour reduces costs, improves efficiency, and reliability
• Can adapt to unexpected or dynamic environments

Limitations of goal-based agent

• Integration with legacy systems can become difficult and costly
• Defining clear goals is challenging, especially when they are multi-objective
• High computational and development costs that rise with complexity
• Prone to training data biases

4. Utility-based AI agents

Utility-based AI agents build upon goal-based agents by not just achieving goals but maximising overall impact using a utility function. Through complex reasoning, they assess various possible scenarios and outcomes, assign utility values to each, and select the action that achieves the desired goal with the highest overall value.

This type of AI agent is useful when there are multiple ways to achieve a goal, or when trade-offs between competing goals or uncertain outcomes should be considered. For example, an AI-assisted job allocation agent optimising task assignments while balancing factors such as skill set, cost, time, etc.

Components and operation of a utility-based agent

Perception module
↓ (Gathers data from the environment using sensors)
State representation / estimator
↓ (Maintains a model of current and past states, anticipating future states)
Utility function
↓ (Mathematical model assigns numerical value to outcomes based on desirability)
Transition model
↓ (Simulates effects of actions on the environment)
Decision-making mechanism
↓ (Considers utility values and transition model to choose the most efficient action)
Execution module
↓ (Executes the selected action via actuators or predefined operations)
Learning component (in advanced agents)
↺ (Improves utility function, transition model, and state representation by learning from past experiences)

Benefits of utility-based agent

• Handles conflicting goals through nuanced trade-off evaluation
• Delivers context-aware, optimised outcomes that maximise overall benefits
• Uses probabilistic reasoning to make decisions under uncertainty
• Adapts to dynamic environments and new information

Limitations of utility-based agent

• Preferences and goals can be subjective and hard to model
• Utility evaluation can be costly in large or dynamic environments
• High computation can bottleneck real-time decision-making
• May fail when encountering data gaps or unexpected situations

5. Learning AI agents

Learning agents are autonomous systems that improve their performance over time by learning from interactions with their environment, often using techniques like reinforcement learning. By processing sensory input and feedback, they adapt to changing needs, patterns, and standards.

Personal assistants like Siri and Alexa incorporate learning agent features like these. They adapt to user preferences and routines to deliver better responses and recommendations over time. Similarly, summarisation agents use natural language processing and learned data patterns to effectively summarise unstructured texts. Our GP Workflow Assistant tool applies this approach through its clinical summarisation agent.  

Components and operation of a learning agent

Performance element
↓ (Selects and executes actions based on current knowledge and strategies)
Critic
↓ (Evaluates actions against predefined standards and provides feedback on outcomes)
Learning element
↓ (Uses critic’s feedback to improve knowledge, rules, or predictions)
Problem generator
↺ (Proposes new actions or scenarios to explore and improve)

Benefits of learning agent

• Adapts in real time to changing environments
• Improves performance and outcomes over time
• Makes accurate, data-driven decisions
• Exceeds human capabilities in detecting patterns, problems, and insights

Limitations of learning agent

• Poor early performance; unreliable for critical tasks without sufficient training
• Require large datasets; collection and processing can be costly
• High maintenance and operational costs 
• Decision-making can be opaque ("black box")

6. Multi-agent AI system

A multi-agent system consists of multiple autonomous or semi-autonomous agents interacting within a shared environment to achieve individual or collective goals. Depending on the context, agents may cooperate, compete, or both. In the context of business workflows, these represent a shift from “AI workflow automation” based on predefined conditions powered by code, to “agentic automation” based on real-time predictions, powered by models.

For example, our OneAdvanced AI-powered risk assessment agent leverages specialised agents which focus on specific tasks (generating risk statements, suggesting mitigations, and more) to collaboratively achieve risk management goals.