An AI coding assistant turns a request and available project context into code or guidance. Some assistants only suggest code; agent-capable tools can also inspect and edit files, run commands, and execute tests. When tools return results, the assistant may use them to revise its work—but generating code, running tests, and proving code correct are three different things.
How an AI coding assistant produces code
1. It builds a prompt from the task and context
You describe what you want, and the product may add relevant code snippets, files, repository details, or instructions. That combined context is sent to a language model. The response depends partly on what information the assistant can access: a request without the relevant surrounding code may leave important assumptions unstated. GitHub describes this task-and-context prompt process in its explanation of agents.
2. The model generates code or asks for an action
The model produces output based on the prompt. In a suggestion-focused interaction, that output may be code or a natural-language explanation for you to use. In an agent workflow, it may instead request an action—such as reading a file or running a command—through a tool made available by the product. OpenAI describes model output as either text for the user or a tool request in its account of the Codex agent loop.
3. The surrounding tool runs the action
A product component called a harness handles tool requests. Depending on the product, mode, permissions, and environment, it may inspect files, edit code, or run commands. These capabilities are not universal. For example, GitHub says its cloud agent can run automated tests and linters in an ephemeral, firewalled development environment; the Codex CLI documentation describes working in a local repository and using tools installed on the user’s machine.
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4. Tool results can guide another attempt
The harness can return command output or test results to the model as additional context. The model may then explain the result, propose an edit, or request another action. OpenAI describes the output being appended to the prompt and supplied to a subsequent model call; the cycle continues until the model responds to the user rather than requesting another tool action. A failing test can inform a revision, but the feedback loop does not guarantee the assistant will diagnose or fix every failure.
Does the assistant write tests, run them, or both?
“Test” can refer to separate stages. Check the session output or product controls to see which actually occurred; seeing test code is not evidence that it was executed.
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- Test generation: The assistant proposes test code, such as unit tests. GitHub’s IDE guide says Copilot Chat can generate unit tests. That does not, by itself, mean the tests were run.
- Test execution: An agent invokes the project’s tests or linters using available tools. GitHub documents test and linter execution by its cloud agent in its agent guidance.
- Human validation: A person reviews the change, checks the test output and coverage, and judges whether the tests match the intended behavior. GitHub explicitly assigns users responsibility for reviewing and validating Copilot cloud agent responses in that same guidance.
What a passing test does—and does not—tell you
A passing run is evidence that the tests that were executed passed in the environment where they ran. It does not establish that every relevant behavior was tested, that the tests themselves express the right requirements, or that the code is correct in every setting. A test result should be read alongside the changes and the test coverage, not as a blanket guarantee.
Generated code also needs scrutiny beyond whether it passes a particular suite. A 2024 study abstract comparing four assistants on method-generation tasks reported that they had complementary capabilities but “rarely generate ready-to-use correct code.” That is a finding about the study’s assistants and task scope, not a universal error rate or a measurement of every current coding assistant. Read the study abstract.
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How to assess an assistant’s coding workflow
When evaluating a coding assistant or reviewing one of its sessions, check the parts of the workflow that determine what it could do and what its results mean:
- Capabilities: Does it only suggest code, or can it edit files and execute commands?
- Context: Which files, repository details, and instructions were available to it?
- Testing: Did it generate tests, run existing tests, run linters, or do some combination?
- Execution environment: Did commands run locally or in an isolated cloud environment?
- Boundaries: What permissions and network access applied to the tools?
- Visibility: Can you inspect the code diff, commands, output, and test results?
These are product- and mode-specific choices. GitHub’s agent guidance and OpenAI’s Codex CLI documentation illustrate different execution arrangements; do not assume one assistant’s workflow describes them all.
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