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To create an AI coding agent, combine a model with a controlled loop, a small set of file and command tools, an isolated workspace, and checks that verify what the agent actually did. Start with one bounded repository task—such as fixing a small bug and running its relevant test—rather than granting a model unrestricted access to a computer.
Decide what your first agent is allowed to do
Write a task contract before choosing tools or a runtime. It should tell the agent what job to perform, where it may work, what commands it may run, what counts as a completed result, and when a person must review its work.
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For example: “Inspect this repository, fix the bug described in the issue, change only files needed for the fix, run the relevant test, and report the files changed and the test output. Stop before committing or deploying.” This is a starting specification, not a guarantee that the agent will obey it; enforce important restrictions in the tools and application as well as in the prompt.
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- Scope: Name the repository or workspace and any files or directories that are off limits.
- Allowed actions: List the tools and commands the agent may use, and identify actions that need approval.
- Completion criteria: Specify the expected code change and checks, such as a relevant test or lint command.
- Report: Require a summary of the diff, commands run, actual outputs, and unresolved issues.
Choose who owns the agent runtime
There are two useful starting architectures. A code-first SDK gives your application direct responsibility for the tools, deployment, state, and approval decisions while the SDK runs the agent loop. A managed agent API can provide a hosted harness and execution environment; OpenAI’s Agents API quickstart demonstrates a coding assistant creating and running a script in an OpenAI-hosted sandbox. These approaches address different ownership needs, not a universal ranking.
#1 Best Overall
| Question | Code-first SDK | Managed agent API |
|---|---|---|
| Who owns the application’s tools and approvals? | Your application server defines and controls them. | The hosted harness supplies part of the workflow; confirm which controls your application still owns. |
| Where does code run? | In the execution environment you select and integrate. | The quickstart demonstrates an OpenAI-hosted sandbox. |
| Who manages run state and deployment? | Your application owns these responsibilities. | Some harness capabilities are managed; verify state, tracing, and deployment behavior for the API you choose. |
| What is the trade-off? | More direct control, with more implementation and operational responsibility. | A hosted path can reduce harness work, while making the provider’s runtime and capabilities central to the design. |
Compare the options against the questions that matter to your product: who owns the loop, where commands execute, how state is stored and resumed, how tools or MCP connections are integrated, how approvals and guardrails work, how runs are traced, and what current limits and costs apply. The cited official materials establish these as architectural differences, but do not establish a neutral performance or cost winner.
Build the model-and-tools loop
A coding agent is not just a prompt. It needs instructions, a model interaction loop, callable tools, an execution context, and a way to retain or return run state. At a high level, the loop works like this:
- Send the task, agent instructions, and relevant context to the model.
- Check whether the response is a final answer or a request to call a tool.
- Validate a requested tool call against your application’s policy and input schema.
- Run an approved call in the designated workspace and capture its result.
- Return the tool result to the model, then continue until it responds with a final answer or the application stops the run.
Keep each tool narrow, with explicit inputs and outputs. A first repository agent may need only these capabilities:
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- Read a file by path, with checks that prevent access outside the workspace.
- Apply an edit to an allowed file and return the changed path or a clear error.
- Run an allowed command in the workspace and return its exit status and captured output.
Do not expose broad host access merely because it is easier to implement. Validate paths, command arguments, and requested actions before execution. The application—not the model’s description of its own permissions—should determine which operations are allowed and which require human approval.
Give repository work an isolated workspace
If the task involves multiple files, shell commands, dependencies, generated artifacts, or resumable work, provide a workspace in an isolated execution environment. The orchestration layer should route tool calls, enforce policy, record run state, and handle approvals; the workspace should execute the file and command operations requested by the agent. A sandbox is the execution plane, not a replacement for application-level control.
Keep credentials and other trusted application responsibilities outside the sandbox. The OpenAI Agents API quickstart explicitly says to keep the API key outside the sandbox. Apply least privilege to mounted files, environment variables, network access, and permitted commands; the right restrictions depend on the sandbox provider and the deployment you are building.
Rank #4
Decide what should happen when an operation fails. A missing file, rejected command, nonzero test result, or expired execution should be returned as a tool result the agent can reason about—or should stop the run when recovery would be unsafe. Capture enough state to understand what happened and, if your workflow permits it, resume after a review pause.
Make verification and human review part of the workflow
Do not treat the model’s claim that it fixed a problem as evidence that it did. Capture the actual tool calls, outputs, exit statuses, and resulting changes. Before accepting consequential work, let a person inspect the diff and the recorded checks.
Best Value
- Use policy checks before tools execute, rather than relying on a reminder in the prompt.
- Pause for approval before actions that your task contract marks as consequential.
- Preserve enough run state and tracing to diagnose a bad tool choice or resume after review.
- Keep a clear record of proposed changes and commands so a reviewer can compare the report with the run.
These are responsibilities to design into the application even when an SDK or managed harness supplies parts of the workflow. OpenAI’s SDK guidance treats guardrails, human review, results and state, and observability as capabilities to add as workflows grow; sandbox guidance also discusses recovery and resumable state.
Test the agent on representative repository tasks
Build a small evaluation set from the kinds of jobs you actually expect it to handle. For each task, check whether it changed the right files, stayed within the allowed commands, passed the relevant checks, and described the outcome accurately. Include cases where the correct action is to stop, ask for approval, or report that a check failed.
Record your own results and the conditions under which you measured them. The official materials cited here do not provide a named, publisher-attributed performance figure or an independent comparative coding-agent benchmark suitable for predicting how your implementation will perform.
Add capabilities only when the workflow needs them
Start by inspecting real runs of the narrow workflow. Add handoffs, persistent state, more tools, stronger guardrails, or additional review steps only when those runs reveal a need. Anthropic’s article Building agents with the Claude Agent SDK describes giving an agent computer-like tools; it summarizes the design principle this way: “The key design principle behind the Claude Agent SDK is to give your agents a computer, allowing them to work like humans do.” The practical boundary matters: provide the tools needed for the task, while keeping execution and approval under application control.
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