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When you ask an AI coding agent, “How does X work in this codebase?”, it may do much more than generate an answer: it can search files, read code, run permitted tools, and use each result to decide what to do next. That iterative process helps explain why a final message may not make every step—or every code change—easy to follow. To tell whether a task is actually complete, review the changes and relevant checks rather than relying on a confident-sounding summary.
What an AI coding agent is
An AI coding agent is not just a model. It is a model working inside software that supplies task context, makes tools available, and manages execution. The model can produce a user-facing response or request a tool action; the surrounding runtime interprets that request, runs an allowed tool, and returns the result to the model.
Anthropic defines an agent as “an AI model that directs its own processes and tool use when accomplishing a task—that is, deciding for itself how to achieve what users want, rather than following a fixed script.” In practice, the degree of choice is bounded by the agent’s configuration and the permissions its environment grants.
How the agent loop works
The interaction is usually iterative, not a single prompt followed by a single answer. OpenAI describes this repeated process as an agent loop. Each tool result becomes new context that can guide another model turn.
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- Receive a task and context. The system gives the model the user’s request and whatever relevant context the product supplies.
- Choose a next action. The model may respond directly or request an operation, such as searching files, reading a file, running a command, or editing code.
- Execute permitted tools. The runtime carries out allowed requests in its execution environment. What it can access or change depends on the product and configuration.
- Return observations. Tool output is added to the context. The model can use it to refine its approach, ask for another action, or form a response.
- Reach a stopping point. A run may end with a final response, pause for approval, or require human input. The exact stopping behavior varies.
GitHub’s Copilot SDK guide illustrates how a codebase question can lead to repository searches and multiple file reads before the agent answers. The sequence matters: what looks like one request in a chat can involve many operations behind the scenes.
What an agent can inspect or change
There is no universal set of coding-agent capabilities. Tool access, permissions, and execution environments vary by product and setup. An agent might be able to read repository files but not edit them, or it may be permitted to make local changes and use development tools. Some workflows pause for approval or human input; others can proceed further within configured limits.
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When evaluating a particular agent, check these practical details rather than assuming all agents work alike:
- Which tools it can use, and what permissions each tool has.
- Where those tools run and which files or systems they can reach.
- When it asks for approval or other human input.
- Whether you can inspect its activity, tool results, traces, and code diffs.
- Which tests or other validation steps the workflow actually runs.
Why agent-written code can be hard to understand
A final answer can compress a longer trail of model turns and tool operations. It may not show which files the agent inspected, which commands it ran, what those commands returned, or why it changed direction. If it edits several files, understanding the result also means connecting each change to the original task.
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That is a consequence of the workflow, not a measured claim that readers regularly struggle with agent-written code. The agent may have gathered relevant evidence during its run, but a short final message alone may not reveal that evidence or the reasoning path behind the edits. A fluent explanation is not a substitute for seeing the changes and checking their behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to check whether a coding task is complete
GitHub says users are responsible for reviewing and validating generated responses. For code changes, use a practical review sequence:
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- Compare the diff with the request. Check which files changed and whether each change is relevant to the task. Look for unrelated edits or omissions.
- Inspect the activity record, if available. Review relevant tool calls and results, or the agent’s trace. OpenAI describes traces that can record model calls, tool calls, guardrails, and handoffs; the available detail depends on the product.
- Run appropriate checks. Use the project’s relevant tests or other validation steps, and inspect their results. A command appearing in an activity record does not establish that it passed or that it covered the behavior you care about.
- Decide whether the outcome meets the task. Confirm that the requested behavior is present and that the observed checks support it. A trace can help explain what happened, but it does not by itself prove the software is correct.
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