How AI Coding Agents Work
An agent is not one giant prompt. It is a loop that repeatedly turns repository state and tool results into the next useful action.
The loop in one line
A useful simplified model is:
Goal → inspect → plan → act → observe → verify → repeat or stop
Real products implement this differently, but the loop explains why an agent can do more than return a block of generated code. Every tool result can become new evidence for the next model decision.
1. Gather the context needed for the task
A repository is usually too large to place in a prompt indiscriminately. The agent therefore needs ways to find relevant context: directory listings, code search, file reads, symbols, version-control history, project instructions, issue text and previous tool output.
Good context is task-specific. Fixing an API validation bug may require the route, schema, tests and error output — not every file in the repository. More context can help until it becomes noise or consumes the available context window.
2. Choose an action and use a tool
Once the model has enough evidence, it can propose an action. Typical coding tools include reading and editing files, searching text, applying patches, running shell commands, invoking tests and inspecting Git state.
The model itself does not magically modify a filesystem. The agent host exposes tools and decides how a requested action is executed. This separation is important because the host can constrain paths, commands, network access and approval requirements.
Tool protocols can also standardize this boundary. The Model Context Protocol (MCP), for example, defines a way for AI applications to connect to servers that expose tools, resources and prompts. MCP is one integration mechanism; it is not required for a system to be an agent.
3. Observe what actually happened
After a tool runs, the result returns to the loop. That may be file content, a successful patch, compiler diagnostics, test failures, command output or an error from the tool itself.
This observation step is the difference between “here is code that should work” and “I changed the code, ran the relevant check, saw a failure and adjusted the implementation.”
It also creates a new risk: tool output is input to the model. Repositories, webpages, issues and generated logs can contain misleading or malicious instructions. Agent systems therefore need to distinguish task instructions from untrusted content rather than blindly treating every piece of text as authority.
4. Verify against the task
A good agent loop should seek evidence proportional to the change. Verification might include a focused unit test, a broader suite, linting, type checking, a build, a diff inspection or several of these.
Passing checks are not proof of overall correctness. Tests have coverage boundaries, and an agent can optimize toward the visible checks while missing an unstated requirement. Human review remains important, especially for security, data migrations, authentication, billing and production infrastructure.
5. Stop, report or ask for help
The loop needs stopping conditions. It should stop when the requested work is complete and sufficiently verified, when an approval boundary is reached, when critical information is missing or when further attempts are no longer productive.
“I need a decision” can be a better result than silently guessing. Autonomy is useful when it is bounded by explicit task scope, permissions and escalation points.