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System Prompts for Coding Agents: A Debugging Guide

A coding agent rarely fails because it cannot write code. More often, it lacks a precise definition of the job, the repository rules, or the conditions for success. A strong system prompt turns an unpredictable assistant into a repeatable engineering tool. This guide explains how to write one, diagnose weak behavior, and control cost without reducing useful context.

Start with a clear contract

Write the system prompt as an operating contract, not a motivational message. State the agent’s role, its allowed actions, and what it must return. Keep temporary task details in the user message so the system prompt remains reusable across tickets.

• Role: Define whether the agent is a maintainer, reviewer, test engineer, or release assistant.
• Scope: Name the directories, languages, frameworks, and files it may change.
• Constraints: Require existing conventions, minimal diffs, backward compatibility, and no invented dependencies.
• Completion: Require tests, a change summary, unresolved risks, and explicit confirmation when work is incomplete.

Avoid vague instructions such as “write high-quality code.” Replace them with observable behavior: “Inspect related tests before editing, preserve public interfaces unless requested, and run the narrowest relevant test command after each meaningful change.”

Give the agent a repeatable workflow

Agents perform better when the prompt defines an order of operations. A practical workflow is: inspect repository guidance, locate the relevant implementation and tests, explain a short plan, make the smallest safe change, run validation, review the diff, and report results. Tell the agent to ask a focused question when requirements conflict or a destructive action is necessary.

For example, a useful rule is: Do not modify files until you have identified the current behavior, the desired behavior, and at least one validation method. This prevents premature edits and makes tool usage easier to audit. Also specify whether the agent may install packages, run migrations, access the network, or rewrite generated files.

Use a compact prompt structure

Organize the prompt into stable sections so you can debug one instruction at a time: Identity, repository context, priorities, workflow, tool policy, coding standards, verification, and response format. Put the highest-priority safety and scope rules near the top. End with a short output contract, such as: “Report files changed, commands run, test results, and remaining concerns.”

Do not duplicate the same rule in several sections. Repetition can create conflicts, especially when one section says to minimize changes and another encourages refactoring. Prefer one precise rule with a clear priority.

Troubleshoot common failures

• It edits the wrong files: Add repository discovery steps and require the agent to cite the file paths supporting its plan. Explicitly exclude vendor, build, migration, or generated directories where appropriate.
• It claims success without testing: Require a command, its exit status, and a concise interpretation. Instruct it to say “not run” when an environment limitation prevents validation.
• It loops or keeps explaining: Set a stopping rule. After two failed approaches, the agent must summarize evidence, identify the blocker, and ask one targeted question.
• It produces oversized diffs: Require a focused patch and a final diff review. Tell it not to reformat unrelated files or perform opportunistic cleanup.
• It ignores project conventions: Tell it to read files such as CONTRIBUTING, package manifests, formatter settings, and local agent instructions before coding.

FAQ: improving reliability and cost

Should every project have a different system prompt? Use a shared core for workflow and safety, then add a small project section for commands, architecture, and conventions. This is easier to maintain than copying a large prompt.

How can I reduce hallucinations? Require evidence. The agent should inspect the repository, quote relevant paths, distinguish facts from assumptions, and avoid inventing APIs that it has not found in the codebase.

Should I use the biggest model? Not always. Use a stronger model for architecture changes or difficult debugging, and a faster model for targeted edits, tests, and documentation. A concise prompt also reduces repeated token usage.

How do I test a system prompt? Create a small evaluation set containing a normal task, an ambiguous request, a failing test, a restricted file, and a request that should be refused. Compare changed files, test execution, explanation quality, and unnecessary tool calls after each prompt revision.

Run coding agents affordably

When you need Claude or GPT models for repeated agent runs, 59API is a practical low-cost option. It offers pay-as-you-go access to Claude Opus, Sonnet, Haiku, and Fable plus GPT models through native official-quality model access, with no downgrade. Its API base URL is https://api.59api.com, and it works with Claude Code, Codex, and OpenAI SDK integrations. Developers can also receive a referral rebate. If you want to test disciplined prompts without committing to a large subscription, consider signing up for 59API and start with a small usage budget.

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