How to Set Up an AI Coding Agent
Most disappointment with coding agents traces back to setup, not the agent itself. A team installs the tool, runs a vague task, gets a mediocre result, and concludes agents are overhyped. The reality is that an agent given access to your tooling, your conventions, and a clear task performs very differently from one running blind. The steps below put that foundation in place.
Choose the Right Agent
Start by choosing an agent that fits how you work. The main dimensions are interface, model and privacy requirements, and the kind of tasks you do. If you want a polished visual experience integrated into your editor, a tool like Cursor fits. If you want an agent that works with any editor and excels at large refactors, a terminal agent like Claude Code fits. If you need to choose your own model or keep code on your own infrastructure, an open-source agent like Aider fits.
Do not over-research this step. The agents have substantial overlap in capability, and you will learn more from using one than from comparing specifications. Pick the one whose interface and model approach match your needs, with the understanding that you can switch later if it does not work out. The best coding agent comparison can help narrow the choice, but a reasonable pick you actually use beats a perfect pick you keep deliberating over.
Install and Authenticate
Install the agent in your project following its documentation, and connect it to a language model. For commercial agents this usually means signing in or providing an API key. For open-source agents it means pointing the agent at either a cloud model's API or a locally running model.
Once installed, confirm the basics work before doing anything ambitious. Check that the agent can read your files, that it can run a simple command, and that it responds to a basic request. This verification takes a minute and saves confusion later, because it separates setup problems from agent behavior problems. If the agent cannot read your project or run a command, fix that before moving on, because nothing else will work until it can.
Connect Your Test and Lint Tooling
This is the single most important step for output quality. Give the agent the commands it needs to run your test suite, your linter, your type checker, and your build. When the agent can run these, it verifies its own work and self-corrects before you ever see the result. An agent with access to your tests produces dramatically better code than the same agent without it, because the feedback loop that catches its mistakes is only as good as the checks it can run.
Be specific about the commands. Tell the agent exactly how to run the full test suite, how to run a focused subset, how to invoke the linter, and how to build the project. Many agents let you record these commands in their configuration so they are available for every task. The few minutes this takes pays back on every task the agent performs afterward.
Document Your Conventions
Write project-level instructions that describe how your codebase works. Cover the patterns you follow, the libraries you prefer, the coding standards you enforce, and anything unusual about your project's structure. These instructions persist across sessions, so the agent starts each task already understanding your norms instead of rediscovering them every time.
Good convention documentation is specific and prescriptive. Instead of "write clean code," say "use the repository pattern for data access, validate all inputs at the boundary, and follow the existing error-handling structure in the services directory." The more precisely you describe your expectations, the more consistently the agent meets them. This documentation is also where you encode security requirements, which feeds directly into a stronger security posture for everything the agent writes.
Set Permissions and Boundaries
Decide how much autonomy to grant the agent and configure it accordingly. Most agents let you require approval before they run commands, edit files, or take other actions, or let them operate more freely once you trust them. The right level depends on the situation: a throwaway prototype warrants more freedom, while a production service warrants approval gates on sensitive actions.
Set boundaries that keep the agent safe. Restrict it to the project directory so it cannot modify files elsewhere. Block destructive operations unless you explicitly allow them. Establish that high-stakes changes, like anything touching authentication, payments, or production configuration, get human review regardless of the agent's confidence. These boundaries let you grant autonomy where it is safe while protecting the areas where mistakes are costly.
Run a First Task and Review
With the foundation in place, assign a small, well-scoped first task. Choose something routine that follows existing patterns, like adding a simple endpoint or writing tests for an existing function. A small task lets you observe how the agent works without much risk, and it reveals whether your setup is sound.
Watch how the agent approaches the task, then review the result carefully. Look at whether it followed your conventions, whether it ran the tests, and whether the code meets your standards. Use what you learn to refine your instructions. The first few tasks are as much about tuning your setup as about getting work done. After a handful of tasks and refinements, the agent settles into producing consistent results, and you can scale up to larger and more valuable work with confidence.
Setting up a coding agent well means choosing a tool that fits your workflow, connecting it to your test and lint tooling so it can self-correct, documenting your conventions so it matches your codebase, setting permissions that grant autonomy safely, and refining through a first task. The tooling connection and convention documentation matter most: they are what turn an unreliable agent into a consistent one.