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How Much Do You Need to Understand How an Agent Works?

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In the previous article, we asked what else developers need to learn when they can already solve problems with Codex. Let’s narrow that question to one situation: an agent changes some API code, but the result is not what you expected. How much do you need to know about how the agent works to choose your next step?

A useful first question is less “How should I rewrite the prompt?” and more “What did it actually read and run during this task?” The mismatch could come from the material it used, the conditions under which it ran, or the code’s behavior. We only need to understand the agent well enough to begin telling those possibilities apart.

The Model Chooses an Action; the Surrounding System Runs It

It helps to distinguish the model from the harness, the system around it that runs the work. Given a request and its current context, the model chooses its next response or tool use. The harness manages the context, tools, and task state, and applies the configured execution and approval boundaries.[S2]

A simplified flow looks like this:

request and current context → model chooses a next action or tool → execution in the permitted tools and environment → output informs the next step → another action or completion[S1][S2]

For example, the model may choose to read a file or run a test, then use the tool’s output to continue editing. OpenAI describes this kind of cycle: changing code, running tools, observing results, and addressing failures.[S1] The arrows are a guide, though. A task may skip or repeat steps, or pause for human input.[S1][S2]

This distinction matters because a requested action is not necessarily an executed action. If you ask the agent to “run the tests,” check the task record to see whether a test command ran and how far it got.[S2][S5]

A File in the Repository Is Not Necessarily a File the Agent Read

The information available to an agent comes in different forms. There is the user’s request; task instructions such as AGENTS.md, which are loaded according to defined discovery rules; ordinary files the agent finds and reads with tools; and output returned after a command runs.[S3][S5] Instructions tell the agent how to work. Reference material, such as an API contract, tells it what the change should conform to. Tool output reports what a read or execution actually returned.

The practical distinction is between a file existing and its contents having been read during this task. Codex documentation explains how AGENTS.md instructions are discovered, but it does not say that every ordinary file in a repository is read automatically.[S3] OpenAI also recommends pointing the agent to documentation relevant to the task.[S6] So even if the repository contains the latest API contract, you need to inspect the files read and the resulting changes to see whether that contract informed the edit.

The examples from here on are hypothetical. Suppose you ask for an API response’s status field to match a new contract, but the edited code appears to follow an older one. That impression alone does not establish that the agent “ignored” the latest contract. First check which contract file it read, the version and scope of that file, and which definition matches the diff.[S3][S5]

The Record Helps You Choose What to Check Next

A task record may show commands and their output, file changes, and diffs; what you can inspect depends on the environment and interface.[S5] It helps answer “What was attempted, and what happened?” But the visible record is not the model’s entire internal reasoning process. The Codex App Server documentation treats task items and reasoning displays separately, and showing raw reasoning depends on model support.

The point of reading the record, then, is to narrow down what to check next, not to reconstruct every unseen thought. Even within the same API change, signs that the wrong contract was used, a test that could not run, and a test that ran and failed call for different next steps.

Four Paths Through the Same API Change

The table continues the hypothetical status field change. These are neither reports of real incidents nor results of experiments. No symptom establishes its cause before you check the evidence.

Hypothetical situationEvidence to checkNext step
The edit appears to follow an old contract.The contract file and version actually read, the contract that currently applies, and the diff.[S3][S5]Confirm the location and scope of the current contract, then review the change against it. The file’s presence in the repository does not establish that it was read.
A test command was attempted but could not connect to the DB.Whether the command ran, its exit status and error output, the connection target, and whether the DB was ready.[S5]Check the DB and connection settings, establish the required conditions, then run the test. A connection failure alone does not establish a code defect.
Required execution or access was blocked by a permission boundary.Approval or denial records, the environment and permission settings in effect, and which command could not run.[S2][S4][S5]Check the access and approval conditions needed for this task. Do not assume that stronger wording in the request changes the execution boundary.[S4]
The test ran and an assertion failed.The failed assertion, expected and actual values, the fixture used, the relevant code, and the diff.[S5]Investigate whether the contract, test, or implementation is out of alignment. After a fix, rerun the affected tests; the failure message alone does not establish the cause.

The phrase “the test failed” can lead to different questions depending on whether the command actually ran. The record helps narrow the investigation before you settle on an explanation.

For a permission issue, inspect the execution environment and approval settings rather than the prompt’s tone. Sandbox and approval policies govern what a command can access and when approval is required before it runs.[S4] Start by identifying the access the task needs.

Let the Remaining Problem Set the Depth of Study

In the API example, the evidence you find can guide how deeply you study the system. The following is a proposed way to make that decision, not a universally proven method.

If finding a missing reference and correcting the task context resolves the issue, that may be all you need to understand for this problem. If distinguishing an environment issue from a permission issue lets the test run, you have learned the execution conditions you needed.

If the problem persists after you have checked the materials read and the execution conditions—or if you need to make a specific implementation decision—there is a reason to go deeper. Focus that study on the implementation, harness, or model behavior relevant to the remaining question.

Pick one recent development task you gave Codex and look for four things: Which files did it actually read? Which commands ran, and what were their results? What could not run or remains unverified? What is one thing you should check next? If the task record cannot answer a question, that gap is itself a starting point for further investigation.

This article asked how much you need to understand about an agent’s operation to interpret its results. The next turns to a decision made before handing over the work: When you can delegate more, what does the developer need to decide?

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