What Is an AI Agent?

1. Perception

Observe the environment.

The agent receives inputs from its environment: user messages, API responses, tool results, sensor data, file contents, database records. It interprets and contextualizes what it observes before deciding what to do next.

2. Reasoning

Plan the approach.

The agent breaks complex goals into sub-tasks, decides which actions to take and in what order, and adapts its plan when things go wrong. This is where the language model does its core work: understanding the problem and mapping out a path to the solution.

3. Action

Execute with tools.

The agent takes real actions: calling APIs, writing code, editing files, browsing the web, running shell commands, sending messages. Tools extend the agent beyond text generation into real-world operations.

4. Memory

Retain context across steps.

Short-term memory maintains context within a conversation or task (the working memory of what has been tried so far). Long-term memory persists knowledge across sessions using files, databases, or vector stores. Memory is what makes agents improve over time.

5. Feedback loop

Learn from results.

The agent observes the result of its action, evaluates whether it succeeded or failed, and adjusts its approach accordingly. This observe-evaluate-adjust cycle is the agentic loop. It runs until the goal is met or the agent determines it needs human input.

The loop

Perceive, reason, act, observe.

These five functions form a continuous cycle. The agent perceives its environment, reasons about the best action, executes that action, observes the result, stores relevant information in memory, and loops back to perceiving the updated environment. This is the defining pattern of an AI agent.

Takes actions

Not just text output.

A chatbot returns text. An agent calls tools: APIs, file systems, databases, browsers, command lines. The output of an agent is not a message. It is a change in the world.

Multi-step reasoning

Loops until done.

A chatbot handles one prompt and one reply. An agent works in a loop: try something, observe the result, adjust, try again. It handles multi-step workflows that require adapting to intermediate results.

Adapts to failure

Recovers autonomously.

When a tool call fails or returns unexpected results, the agent diagnoses the problem and tries a different approach. It does not simply error out. This resilience is core to what makes agents useful for real-world operations.

ReAct

Reason + Act in a loop.

The agent interleaves thinking and tool calls. It reasons about what to do, takes an action, observes the result, reasons again, and repeats. This is the most common pattern for general-purpose agents. It is simple, flexible, and works well for most tasks.

Plan-then-Execute

Full plan first, then carry it out.

The agent creates a complete step-by-step plan before taking any action, then executes each step in order. This works well when the task structure is predictable and you want to validate the approach before committing resources.

Reflexion

Self-critique after each step.

After each action, the agent explicitly evaluates its own performance: did this step move me closer to the goal? What could go wrong? Should I change approach? This self-critique loop produces higher quality results but takes more time.

Multi-Agent

Specialized agents collaborate.

Multiple agents with different specializations work together on a problem. A quoting agent handles rate shopping, a routing agent optimizes delivery paths, an exception agent monitors for problems. Each agent is simpler and more reliable than one agent that does everything.

Choosing

Match architecture to task.

Use ReAct for general-purpose tasks with unpredictable steps. Use Plan-then-Execute when the workflow is well-defined. Use Reflexion when accuracy matters more than speed. Use Multi-Agent when the problem has distinct sub-domains that benefit from specialization.

In practice

Most production agents are hybrid.

Real-world agents often combine architectures. A freight agent might use Plan-then-Execute for the overall workflow (quote, book, track) but ReAct within each step to handle unexpected API responses or missing data.

Perceive

Read the source files.

The agent reads the relevant source files, error logs, and test results. It gathers the context it needs to understand the problem. No human tells it which files to open. The agent navigates the codebase itself.

Reason

Identify the likely cause.

Based on the error messages and code structure, the agent forms a hypothesis about what is causing the bug. It considers multiple possible causes and prioritizes the most likely one based on the evidence.

Act

Edit the code to fix it.

The agent edits the source file to apply the fix. It makes the smallest change that addresses the root cause. It does not refactor surrounding code or add unrelated improvements.

Observe

Run the tests.

The agent runs the test suite to verify the fix. It observes whether the tests pass or fail. If they pass, the loop can end. If they fail, the agent goes back to reasoning.

Adapt

Try a different fix if needed.

If the first fix did not work, the agent does not give up. It analyzes the new test failures, revises its hypothesis, and applies a different fix. The loop continues until the tests pass.

Complete

Report the result.

Once the fix is verified, the agent reports what it found, what it changed, and why. The human reviews the result. The agent did the work. The human validates the judgment.