What are the main components of an AI agent?

An AI agent is usually built from several cooperating components that let it understand a goal, decide what to do, take actions, remember relevant information, and evaluate results.

The exact design varies, but most AI agents include the following core components.

1. Goal or instructions

The agent needs a clear objective that defines what it is trying to accomplish.

For example:

> Find three suitable hotels near the conference venue under ₹8,000 per night.

The goal guides the agent's decisions throughout the task.

2. Perception or input

The agent needs information about the current situation.

Inputs may include:

• user messages
• tool results
• API responses
• files
• database records
• application state
• sensor data

These observations help the agent understand what is happening before it chooses an action.

3. Reasoning or decision-making

The agent needs a mechanism for deciding what to do next.

In many modern agents, this role is handled partly by a large language model (LLM). The model may interpret the goal, evaluate available information, and choose the next step.

Other systems may combine an LLM with rules, planners, classifiers, or application logic.

4. Tools and actions

Tools allow the agent to affect or query its environment.

Examples include:

• web search
• database queries
• API calls
• code execution
• file access
• calendar operations
• sending messages

Without actions, the system may only be able to generate text rather than interact with external systems.

5. State

Agent state stores information needed during execution.

It may contain:

• the original goal
• completed steps
• pending tasks
• intermediate results
• tool outputs
• errors
• decisions already made

State allows later steps to use information from earlier steps.

6. Memory

Some agents also use memory beyond the immediate execution state.

Memory can help retain information such as previous interactions, user preferences, or knowledge learned across multiple tasks.

Not every agent needs long-term memory. For many tasks, temporary state is enough.

7. Control or agent loop

The control layer coordinates the overall execution.

A simplified loop is:

Observe → Decide → Act → Observe again

The controller determines when another step should run and when the agent should stop.

This mechanism is covered separately in “What is an agent loop?”

8. Feedback and stopping conditions

After an action, the agent evaluates the result.

The result may indicate that the agent should:

• continue
• try another approach
• retry a failed action
• ask the user for information
• finish the task

Stopping conditions define when the execution should end.

Putting the components together

A simplified agent architecture looks like this:

Goal → Input/Observation → Reasoning → Action/Tool → Result → State Update → Next Decision

This cycle continues until the task is complete or another stopping condition is reached.

In short

The main components of an AI agent are typically a goal, inputs or observations, a decision-making mechanism, tools or actions, state or memory, and a control loop that uses feedback to determine the next step.

Different agent systems may organize these components differently, but they serve the same basic purpose: helping the agent move from a goal to an outcome through a sequence of decisions and actions.