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Build a Coding Agent with Claude Code, Step by Step

Start with a task your agent can verify

A useful coding agent is not just a chat window connected to a repository. It needs a clear task, access to relevant files and commands, and a way to check whether its changes work. Start with a small, testable job, such as adding validation to one API endpoint or fixing a failing unit test. Keep the first run narrow so you can inspect how Claude Code reads the project, chooses tools, and handles feedback.

Open a clean branch and note the project’s normal checks: build, lint, and test commands. Confirm the working tree is clean before you begin. This makes the agent’s changes easier to review and gives you a reliable way to distinguish its work from existing edits.

Install Claude Code and connect 59API

Install Claude Code using Anthropic’s current installation instructions, then open a terminal in the repository. To route requests through 59API, configure the Claude-compatible API base URL and an API key from your 59API account. For a shell session, the essential environment settings are:

• ANTHROPIC_BASE_URL: https://api.59api.com
• ANTHROPIC_API_KEY: your 59API key

Set credentials in your shell or a local secrets manager, not in source code, committed configuration, or terminal screenshots. Check 59API’s current setup guidance for any required model selection or compatibility settings. Start Claude Code and verify that it can connect before asking it to edit files. If authentication fails, check the base URL, key, shell environment, and account balance first.

Using a relay can make model experimentation more affordable: 59API offers pay-as-you-go access to multiple providers through one API, including Claude, GPT, Gemini, DeepSeek, and others. Its listed models include Claude Sonnet 5.5 for general coding work and DeepSeek V4 Flash for a lower-cost task to compare. 59API describes its models as native, official-quality offerings rather than downgraded versions, and also provides a referral rebate. Review the available models and pricing before choosing a default, since both cost and performance depend on the task.

Give the agent project context

Before delegating, inspect the repository’s instructions and conventions. If the project uses a CLAUDE.md file, keep it concise and actionable: describe the architecture, important directories, commands to run, formatting rules, and boundaries such as generated files or migrations that need extra care. Avoid duplicating every document in the repository. Point Claude Code to the relevant source of truth instead.

Then describe one outcome and its acceptance criteria. For example: “Add server-side validation for the profile update route. Reuse the existing validation library, return the project’s standard error shape, add tests for invalid and valid input, and run the focused test suite.” This gives the agent room to inspect and implement while making completion measurable.

Review the plan and control tool access

Ask Claude Code to inspect the route, nearby tests, and validation patterns before changing anything. Read its proposed approach and correct misunderstandings early. Keep permissions scoped to the repository and approve consequential commands deliberately. An agent that can edit files and run tests is useful; unrestricted access to credentials, production systems, or destructive commands is unnecessary for an ordinary code change.

During implementation, ask for focused progress when the task spans multiple files. If the agent’s approach conflicts with existing conventions, redirect it with a concrete example from the codebase. Avoid asking it to “clean up” unrelated areas: smaller diffs are faster to understand and safer to merge.

Verify the result like a teammate’s pull request

When the agent finishes, inspect the diff yourself. Check that the implementation matches the acceptance criteria, error handling is consistent, tests cover the new behavior, and no secrets or unrelated formatting changes slipped in. Run the project’s focused tests, then broader lint or build checks where practical. A passing test suite is evidence, not a substitute for reviewing the code.

If a check fails, give Claude Code the exact command and relevant error output, then ask it to diagnose the failure before editing. Re-run the check after the fix and inspect the new diff. This feedback loop is the core of a dependable coding agent workflow: provide context, delegate a bounded change, verify the result, and keep the final decision with the developer.

Make the workflow repeatable

Once the task works, update the project instructions only if you learned a durable convention that will help future runs. Track which model and settings work well for different task sizes, and watch API usage as your workflow grows. If you want to try Claude Code with a single pay-as-you-go relay, you can sign up for 59API and configure its API base URL for your development environment.

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