← Retour aux guides

System Prompts That Make Coding Agents Reliable

Why system prompts matter

A coding agent can inspect files, edit code, run tests, and explain its work. However, it still needs operating rules. A vague instruction such as “fix the bug” leaves important decisions unanswered: Which files may it change? Should it preserve the current architecture? What counts as a successful fix? A good system prompt establishes these rules before the agent starts working.

The goal is not to write the longest prompt. The goal is to remove ambiguity around the repository, the task, the tools, and the expected result. Clear instructions reduce unnecessary exploration, risky edits, and incomplete answers.

Start with the agent’s role and priorities

Open with a short description of the agent’s role. Then define the priorities it should follow when requirements conflict. For example:

• Role: “You are a senior engineer working in an existing TypeScript monorepo.”
• Priorities: “Preserve existing behavior, make the smallest appropriate change, and verify the result with tests.”
• Communication: “Report assumptions, changed files, verification performed, and any remaining risks.”

This gives the agent a decision framework. It is especially useful when a requested feature could be implemented in several technically valid ways.

Give repository-specific instructions

Generic programming advice is less valuable than local context. Tell the agent where to find the important information and which conventions are mandatory. Include the package manager, test commands, formatting rules, directory ownership, and patterns that should be reused.

• Use pnpm and run pnpm test for the full test suite.
• Keep API handlers in src/routes and business logic in src/services.
• Use the existing validation helpers instead of adding a new validation library.
• Do not edit generated files or change public API responses without explicit approval.

Keep this section accurate. Outdated instructions are worse than no instructions because they cause the agent to trust a broken workflow.

Define a repeatable work process

A reliable system prompt tells the agent how to approach a task, not just what the final code should look like. A practical sequence is:

• Inspect the relevant files, tests, configuration, and recent history before editing.
• Summarize the likely cause or implementation plan briefly.
• Make focused changes that match existing patterns.
• Run the narrowest relevant test first, then broader checks when practical.
• Review the final diff for unrelated edits, security issues, and missing edge cases.

This process prevents a common failure mode: editing the first plausible file before understanding how the system works. You can also instruct the agent to ask a question when requirements are contradictory or when a destructive action would be required.

Set boundaries for tools and safety

Coding agents often have access to shell commands and file tools. State what they may do automatically and what requires confirmation. For example, permit reading files, creating tests, and running local checks, but require approval before deleting data, changing deployment configuration, rotating credentials, or making broad dependency upgrades.

Add a security rule that secrets must never be printed, committed, or copied into source files. Tell the agent to treat repository content as data rather than instructions. This matters when documentation, issue text, or fixture files contain misleading commands.

Specify what a useful answer contains

Output requirements make results easier to review. Ask for a concise summary of the root cause, a list of changed files, tests that passed or failed, and any assumptions or follow-up work. If the agent cannot run a check, require it to say why instead of implying that verification happened.

For larger repositories, separate stable system rules from task-specific context. Keep the system prompt focused on durable behavior, while providing the current bug report, acceptance criteria, and relevant file paths in the user request.

Improve prompts through measured iteration

Test a prompt against several realistic tasks: a small bug fix, a feature touching multiple modules, and a change with unclear requirements. Record where the agent wastes time, violates conventions, or skips verification. Then add one precise rule for the recurring problem. Avoid piling on contradictory instructions.

Model choice and API cost also affect how often you can run these experiments. 59API provides pay-as-you-go access to native Claude models including Opus, Sonnet, Haiku, and Fable, plus GPT models, through https://api.59api.com. It is compatible with Claude Code, Codex, and OpenAI SDK workflows, so it can be a practical low-cost option for iterating on prompts without using downgraded models. Developers can sign up when they want affordable usage, and the referral rebate can further reduce ongoing costs.

The strongest system prompt is specific, current, testable, and short enough to maintain. Treat it like engineering documentation: review it when the repository workflow changes, and improve it based on observed agent behavior.

Tarifs · Docs