What Is an AI Agent? How Codex and Claude Work

An AI agent is a system that uses an AI model to pursue a goal through a series of actions. Instead of answering one question and stopping, an agent can inspect information, decide what to do next, use connected tools, check the result, and continue until the task reaches a defined stopping point.

That distinction explains why names such as Codex and Claude appear so often. Claude is primarily the name of an AI model and assistant, while Codex has been used for code-focused AI models and, more recently, coding agent products. Either can participate in an agentic workflow when connected to tools, files, software environments, or other systems.

What is an AI agent?

An AI agent combines four basic elements:

• A goal: the result it is expected to produce.
• A model: the reasoning and language system that interprets the goal and chooses actions.
• Tools: capabilities such as browsing, searching files, running code, calling an API, or editing a document.
• A control loop: a process that lets the system observe results, make another decision, and stop when appropriate.

A chatbot might answer, “Here is an example of a Python function.” An agent might receive a request to fix a failing test, inspect the project files, identify a likely cause, edit the code, run the tests, review the error output, and revise the change. The agent is not simply producing text. It is taking steps in an environment.

The word “agent” does not necessarily mean that a system operates independently or without limits. Permissions, approval requests, time limits, tool restrictions, and human review can all be part of the design.

How does an AI agent work?

Most agentic systems follow a repeated loop. The exact implementation varies, but the overall pattern is straightforward:

1. Interpret the task. The system turns a natural-language request into an intended outcome and may identify constraints such as file type, deadline, or allowed tools.
2. Inspect the available context. It may read a document, examine a codebase, retrieve information, or review the result of an earlier action.
3. Choose an action. The model decides whether to answer, ask for clarification, search, call a tool, write a file, or take another permitted step.
4. Execute the action. A separate tool or software environment performs the operation. The model itself does not automatically gain access to every system.
5. Evaluate the result. The system observes the output, such as a test failure, a returned API value, or a document that needs revision.
6. Continue or stop. It repeats the process if more work is needed, or returns a result when the task is complete or requires human input.

In simplified pseudocode, the process looks like this:

This example leaves out many engineering details. A production system may add authentication, permission checks, logging, retries, error handling, tool-specific rules, and a maximum number of steps. Those controls matter because an agent can make a mistake repeatedly if nothing limits its actions.

What is the difference between an AI model, an assistant, and an agent?

These terms are related, but they describe different layers:

• AI model: the underlying system that processes input and generates an output. Claude is commonly discussed at this level, although it can also be used inside assistants and agents.
• Assistant: an application built around a model to provide a conversational or task-focused experience. It may answer questions, remember selected context, or connect to tools.
• Agent: a model-driven system that can select and carry out multiple actions toward a goal, usually through a loop.

The boundaries are not perfectly fixed. A product may call itself an assistant even when it uses tools, or an agent even when human approval is required for every significant action. The useful question is not the label. Ask what the system can actually do after producing its first answer.

How does Claude work?

Claude is an AI model and assistant family developed by Anthropic. At a basic level, Claude receives an input context, predicts a useful continuation based on patterns learned during training, and returns text or another supported response. The context can include a conversation, instructions, documents, and, depending on the application, results returned by tools.

On its own, a language model generates responses. In a tool-using workflow, an application can give the model a list of available operations. The model can then request an operation, such as searching a connected knowledge base or running a calculation. The application executes that request and sends the result back into the conversation. Claude can use the new information to produce an answer or decide whether another action is needed.

A simplified example might look like this:

1. A user asks for a summary of the latest entries in a project tracker.
2. The application provides Claude with a tracker-search tool.
3. Claude requests a search with appropriate parameters.
4. The application performs the search and returns the matching entries.
5. Claude organizes the results into a summary and identifies any unresolved items.

In that example, Claude supplies language understanding and decision-making within the workflow. The application supplies access to the tracker and controls what the model is allowed to do. This separation is essential: a model cannot inspect a private system merely because a user mentions it in a message.

How does Codex work?

Codex has commonly referred to code-focused AI systems from OpenAI. The name has also been used for coding-oriented products and agents, so the exact capabilities depend on the version and product being discussed.

A coding agent built around a Codex-style model follows the same general loop as other agents, but the working environment is a software project. A typical task could involve:

• reading relevant source files and configuration,
• finding where a function or feature is implemented,
• proposing or applying a code change,
• running tests or other checks,
• examining failures, and
• revising the change when the evidence indicates that it is incomplete.

The valuable part is not that the model can produce code snippets. Many language models can do that. The agentic part is the connection between the model and the project environment, including the ability to inspect files and receive feedback from tools such as a test runner.

That connection also creates limits. A coding agent can misunderstand the intended behavior, overlook an edge case, make a change in the wrong location, or interpret a passing test as proof that the entire feature is correct. Code review and tests remain necessary, especially when a change affects data, authentication, payments, or other sensitive behavior.

Why are AI agents suddenly everywhere?

Several developments arrived at the same time.

Language models became better at multi-step tasks

Earlier systems were often judged mainly by the quality of a single response. As models became better at following instructions, working with longer context, producing structured outputs, and revising their work, applications could place them inside longer workflows.

Tool connections made answers actionable

A model that can only write text has limited reach. A model connected to a search system, code environment, calendar, database, or business application can help complete a task. Tool use turns a response into an operation, although the application still needs to enforce permissions and validate results.

Agent workflows are easier to build

Developers now have more established patterns for sending tool descriptions to models, receiving structured tool calls, returning results, and managing the next step. That reduces the work required to turn a general-purpose model into a task-specific system.

The interface is easier to understand

People may not need to learn a specialized programming language to request a multi-step task. A natural-language instruction can describe the desired outcome, while the application handles the intermediate actions. This makes agentic behavior visible in ordinary software rather than only in research demonstrations.

These changes do not mean that agents understand tasks like people do. They mean that the surrounding software can give a capable model access to relevant context and controlled actions.

What can AI agents do well?

Agents are most useful when a task has a clear goal, accessible information, repeatable steps, and a way to check progress. Examples include:

• summarizing a set of documents after retrieving the relevant files,
• classifying incoming requests and routing them to an appropriate workflow,
• drafting code changes and running checks against a project,
• turning structured data into a report, and
• collecting information from approved systems before preparing a response.

They are less reliable when the goal is vague, the necessary information is missing, success is difficult to measure, or a wrong action has serious consequences. “Handle this problem” is not a useful agent specification unless the system also knows what counts as a correct result and which actions are permitted.

What are the main risks and limitations?

Agentic systems add capability, but they also add more ways to fail.

• Incorrect reasoning: a model may choose a plausible but unsuitable next step.
• Fabricated information: it may state an unsupported fact or treat an unreliable result as trustworthy.
• Tool errors: an external service can return incomplete, stale, or misleading data.
• Excessive permissions: a broad tool connection can allow an error to cause unnecessary damage.
• Prompt injection: untrusted text inside a webpage, document, or file may contain instructions that conflict with the actual task.
• Weak verification: a system may stop after producing an output without checking whether the output meets the real requirement.

Useful safeguards include limiting permissions, separating read and write operations, requiring approval for consequential actions, recording tool activity, testing failure cases, and giving the system a clear stopping condition. Human review is particularly important when an agent can send messages, change records, publish content, spend money, or modify production systems.

How should an AI agent be evaluated?

Do not judge an agent only by how polished its final response sounds. Evaluate the complete workflow:

1. Task accuracy: did it achieve the requested outcome?
2. Evidence: can the important claims or changes be checked?
3. Tool discipline: did it use only the tools and permissions needed?
4. Recovery: what happens when a tool fails or the first attempt is wrong?
5. Human control: can a person review, stop, or undo consequential actions?
6. Consistency: does it perform reliably across representative examples, including difficult cases?

This model-versus-agent distinction is useful when comparing AI products on ReadyTools or anywhere else. A strong chat response does not automatically indicate a strong autonomous workflow, and an agent with many tools is not necessarily better than one with a narrower, well-controlled job.

The simplest definition to remember is this: an AI model generates responses, while an AI agent uses a model in a loop of decisions and actions to accomplish a goal. Claude, Codex, and similar systems can serve as the reasoning component, but the surrounding tools, permissions, checks, and human oversight determine what the resulting agent can safely do.