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A coding agent works in a repeated loop: it gathers evidence about a task and the project, decides on a next step, uses tools to act, then checks the results. If the checks reveal a problem, those results inform another decision. A tool call shows that an action ran; it does not, by itself, prove the requested outcome was achieved.
What the four stages mean
“Observe, Model, Act, Verify” is a practical way to describe an iterative coding-agent workflow. Microsoft’s Visual Studio Code documentation describes the repeated reasoning, action, and validation steps as an agent loop. The four labels here are an explanatory model, not a universally mandated standard.
Observe: gather evidence
The agent takes in the user’s goal and relevant evidence from the development environment. That can include code and files, command output, test results, or error messages. These observations provide context; they are not proof that a proposed diagnosis is correct.
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Model: choose a next step
The agent reasons about the goal in light of the available context, determines what may need to change, and selects a concrete action. The action should be proportionate to the task and the evidence so far. This is the decision stage, not the result.
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Act: use a tool
The agent may read a file, edit code, or run a command. Depending on the workflow, actions can also trigger builds, tests, or lint checks. The surrounding software, often called the harness, coordinates the model, tools, and development environment.
Verify: check what happened
The agent examines tool results and compares them with the requested outcome. A test failure or new error is useful feedback: it can change what the agent does next. When validation is incomplete or fails, the loop can return to observing the updated state and choosing another action.
How feedback turns actions into an iterative loop
The stages are connected by returned evidence. After an action, the agent receives results that become context for its next decision. For example, an agent asked to add form validation might inspect the form and existing project patterns, edit code and tests, run relevant tests, diagnose a failure, revise the changes, and run validation again. Visual Studio Code’s documented example illustrates this return path: editing is one step, not the end of the task.
AWS describes the broader agent cycle as “perceive, reason, act.” Hugging Face’s educational explanation uses “thought, action, observation,” with tool information returned to the context before the next reasoning step. These are related descriptions of iterative behavior, not interchangeable names for the four stages above.
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What counts as evidence that the task is done?
Completion language should match what has actually been checked. An edit establishes that code was changed; a completed command establishes that the command ran. Neither alone establishes that the requested behavior works. Stronger evidence comes from relevant checks whose results support the specific outcome—for example, tests that exercise the changed behavior—along with review of the resulting changes.
A sensible stopping rule is to stop when the available evidence supports the requested outcome and a human has reviewed the changes and validation. If the agent lacks necessary context, needs permission, or encounters a problem it cannot resolve safely, it should ask for clarification or escalate rather than claim success. This is a design recommendation, not a guarantee shared by every coding-agent system.
What varies between coding-agent workflows
The loop’s broad shape does not mean all agents have the same capabilities or controls. When evaluating a particular workflow, look at the dimensions that determine what it can observe, do, and verify:
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- Actions: which tools are enabled, such as file access, editing, command execution, or build and test tasks.
- Execution and permissions: where tools run, what access they have, and whether actions require approval.
- Feedback: which tests, lint checks, build results, or other signals can inform the next decision.
- State: how prompts, responses, tool results, and revisions are retained across steps.
- Human review: who inspects or approves the changes and validation before they are accepted.
These are useful comparison criteria, not a product ranking. AWS’s architecture guidance gives one implementation pattern: combine a user query with environment context, send that information to a language model for reasoning, and execute selected actions in an IDE or runtime. Its examples include IDE integrations, sandboxing, and storage for intermediate prompts, responses, or revision history; those are implementation options, not requirements for every agent.
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Where human control fits
The agent loop does not remove the need for oversight. In Visual Studio Code’s documented workflow, users choose which tools are available and what approval requirements apply, then review changes and validation results. Those controls shape what an agent can do; the final judgment about whether its work is acceptable remains part of the human review.
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