AI agents are capable of performing tasks, responding to events, making decisions, and working towards their defined goals. Not all AI agents operate in the same way, though. Some AI agents have predetermined rules and policies to accomplish their tasks, while some rely on learned behavior or even sophisticated AI algorithms for handling complex tasks.
Knowing the types of AI agents will help you understand their capabilities and know when and where they could be utilized. They enable you to shift from rule-based to goal-oriented digital workforces that solve support tickets on their own or handle supply chain management.
In this article, we’ll discuss seven types of AI agents, their workflows, and use cases.
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1. Simple Reflex Agents
The Simple Reflex Agent is the most basic kind of AI architecture. They run purely on pre-programmed condition-action rules like “IF condition, THEN action.” Such agents have no memory, no internal state, nor foresight. They have to decide only on the basis of the current perception without considering anything that happened in the past.
How Does It Operate?
The agent relies on a direct rulebook known as Condition-Action Rules (often written as “IF this happens, THEN do that”):
- Sensors detect what the environment looks like at this exact moment.
- The agent checks its pre-programmed rules for a match.
- Actuators carry out the assigned action immediately.
Example: A smart thermostat that reads the ambient air temperature, have to turn ON and OFF the
- Percept: Room drops below 65°F.
- Rule: IF temperature < 65°F, THEN switch on heater.
- Action: Engage the furnace.
In case the condition changes beyond its rule set, it fails to cope with it because they do not use any past experience and do not have any internal memory to remember the past data of the environment.
These kinds of agents are most effective in an environment where the condition is clear.
Use Cases
- Temperature control: Switching heating or cooling systems on and off based on temperature.
- Basic customer support: Providing predefined responses to specific keywords or conditions.
- Automated alerts: Sending an alert when a monitored value crosses a predefined threshold.
The Big Limitation
Simple reflex agents have no memory and no foresight. If you tape a photo in front of a motion sensor or if the environment becomes partially hidden, the agent cannot figure out what to do. It only succeeds in fully observable environments where every possible situation has a straightforward, hardcoded response.
2. Model-Based Reflex Agents
These types of agents can process information that isn’t directly observable at the current moment. The internal model helps them track changes in the environment and maintain a more complete understanding of the situation.
Workflow
- Observe: The agent collects information from its environment.
- Update its internal state: It combines new observations with data obtained from earlier analysis.
- Evaluate conditions: It uses its internal state and predefined rules to determine what to do.
- Act: It performs an action based on its updated understanding.
Example: A robot vacuum tracks its position and remembers obstacles it has encountered to navigate a room more effectively.
Use Cases
- Robotics: Helping robots navigate environments where not everything is immediately visible.
- Inventory monitoring: Maintaining an estimate of stock levels using available updates and records.
- Equipment monitoring: Tracking equipment conditions over time to respond to changing circumstances.
3. Goal-Based Agents
Goal-based agents select actions based on a specified objective. Instead of simply reacting to a condition, they consider which actions could help them achieve their goal.
They may evaluate different possible actions or sequences of actions before choosing a path forward.
Workflow
- Receive a goal: The agent identifies the desired outcome.
- Assess the current situation: It gathers information about the environment.
- Explore possible actions: Then, the agent evaluates potential steps that could move it closer to the goal.
- Select a plan: It chooses an appropriate sequence of actions.
- Execute and reassess: At last, performs the actions and checks whether the goal has been achieved.
Example: A route-planning system searches for a path that takes a driver from their current location to a destination.
Use Cases
- Route planning: Finding a suitable path to a destination.
- Task scheduling: Arranging tasks to meet a deadline.
- Order fulfillment: Planning the steps required to process and deliver an order.
4. Utility-Based Agents
These agents evaluate possible outcomes using a utility function. This function assigns values to outcomes based on factors such as cost, speed, risk, or convenience.
When several actions could achieve a goal, the agent can compare their expected outcomes and choose the one with the highest estimated utility.
Workflow
- Identify the objective: The agent determines the desired outcome and relevant preferences.
- Gather information: Collects the data needed to evaluate possible actions.
- Estimate outcomes: Assess the likely consequences of each available action.
- Compare utility: Evaluate the outcomes using the defined utility function.
- Choose and act: Select the action with the best estimated utility and execute it.
Example: A delivery planning system at a quick commerce can compare several routes to reduce delivery time, fuel costs, and traffic conditions.
Use Cases
- Delivery optimization: Brands can use it to balance delivery speed and transportation costs.
- Resource allocation: Distributing available resources according to priorities and constraints.
- Pricing decisions: Evaluating possible pricing strategies against business objectives and constraints.
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5. Learning Agents
The type of agents that improve their behavior using experience, feedback, or additional data are learning agents. Instead of relying entirely on pre-fixed rules, they can adapt their decisions as they learn.
A learning agent may contain a learning component, a performance component, a way to evaluate progress, and a mechanism for exploring new actions. Its exact design depends on the application.
Workflow
- Collect information: Agent gathers observations, outcomes, or feedback.
- Make a decision: Use the current model or learned behavior to select an action.
- Observe the result: Check what happened after the action.
- Evaluate performance: Compare the outcome with the desired result.
- Update the model: Use the available feedback to improve future decisions.
Example: A recommendation system learns from user interactions to improve the relevance of its suggestions.
Use Cases
- Recommendation systems: Improving product, content, or service recommendations.
- Fraud detection: Adapting detection models as new patterns emerge.
- Demand forecasting: Improving predictions using historical data and observed outcomes.
Note: Learning does not always happen continuously or automatically. Some systems are retrained periodically using reviewed data.
6. Large Language Model (LLM)-Powered Agents
LLM-powered agents use large language models to interpret instructions, reason through tasks, and decide what to do next. Depending on their design, they may use external tools, retrieve information, interact with software, and complete multiple steps toward a goal.
Unlike a basic chatbot this only generates a response, an LLM-powered agent can be configured to take actions and observe their results.
However, using an LLM does not automatically make a system an autonomous agent. Its capabilities depend on the tools, permissions, memory, and control mechanisms provided to it.
Workflow
- Receive an instruction: Understand the user’s request or assigned task.
- Gather context: Retrieve relevant information from connected sources when necessary.
- Determine the next step: Decide whether to answer, use a tool, or perform another action.
- Execute an action: Call an approved tool or interact with a connected system.
- Review the result: Use the returned information to determine what to do next.
- Complete or escalate: Provide the result, continue the workflow, or request human assistance.
Example: An invoice follow-up agent retrieves outstanding invoices from accounting software, identifies accounts requiring attention, and drafts payment reminders. Depending on its permissions, it may send approved reminders or request employee approval first.
Use Cases
- Sales automation: Researching leads, preparing personalized follow-ups, and updating CRM records.
- Invoice management: Identifying overdue invoices and preparing payment reminders.
- Customer support: Retrieving information from knowledge bases and drafting responses.
- Business reporting: Collecting information from connected systems and preparing summaries.
7. Multi-Agent Systems
These type of agents interact or coordinate within a shared environment. Each agent may have a different responsibility, capability, or objective to perform.
These systems can divide complex tasks into smaller parts. Depending on their design, agents may collaborate, exchange information, delegate work, or independently evaluate results.
A multi-agent system does not necessarily require multiple LLMs. Its agents may use different models, conventional algorithms, or a combination of approaches.
Workflow
- Define the overall goal: Establish the task that needs to be completed.
- Divide the work: Assign subtasks to suitable agents.
- Perform individual tasks: Each agent works on its assigned responsibility.
- Exchange results: Agents share relevant information or completed work.
- Coordinate and review: A coordinating component combines results and checks for inconsistencies.
- Deliver the outcome: The system produces the final result or requests human review.
Example: A sales research workflow can use one agent to find information about a prospective customer, another to analyze the lead, and a third to prepare a sales brief. A coordinating component combines the results for a salesperson to review.
Use Cases
- Sales operations: Coordinating lead research, qualification, and sales preparation.
- Market research: Dividing research, competitor analysis, and report preparation among specialized agents.
- Supply chain management: Coordinating demand analysis, inventory monitoring, and delivery planning.
- Software development: Assigning coding, testing, and code-review tasks to separate agents under appropriate controls.
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Conclusion
Different types of AI agents use different approaches to make decisions and complete tasks. Simple reflex agents follow predefined rules, while model-based agents maintain an internal representation of their environment. Goal-based and utility-based agents evaluate actions according to objectives and desired outcomes. Learning agents adapt using experience, while LLM-powered agents can work with language and external tools. Multi-agent systems coordinate multiple agents to accomplish more complex tasks.
The right approach depends on the problem, available data, system requirements, and level of autonomy needed. Understanding these differences helps businesses identify where AI agents can add value and which approach may be suitable for their workflows.