Prompt Engineering for AI Coding Agents
Effective AI agents start with effective prompts. But agent prompts are different from chatbot prompts — they must encode constraints, output formats, failure recovery procedures, and framework-specific instructions. This guide covers advanced techniques for engineering agent prompts that produce reliable results.
System Prompts: The Agent's Constitution
A system prompt is not just instructions — it is the agent's behavioral constitution. A well-structured system prompt has five layers:
“You are a senior code reviewer specializing in TypeScript and React applications.”
“Review pull requests for: type safety violations, React anti-patterns, missing error boundaries, accessibility issues.”
“Produce a structured report with sections: Summary, Critical Issues, Warnings, Suggestions. Use severity levels: 🔴 Critical, 🟡 Warning, 🔵 Suggestion.”
“Do not suggest changes that alter public APIs. Do not comment on formatting (handled by Prettier). Never reference files outside the PR diff.”
“If you cannot determine whether code is correct, flag it as Needs Human Review rather than guessing. If the PR diff is too large, request that it be split.”
Each layer reduces failure modes. Skipping Layer 5 alone accounts for a large percentage of agent failures in production — models fill gaps with confident-sounding but incorrect output.
Task Decomposition
Complex tasks fail more often than simple ones. The solution is decomposition — breaking a large task into smaller, sequential sub-tasks where each step validates before the next begins.
Example: “Refactor this 2000-line service file” is a high-failure prompt. Instead, decompose:
- Analyze — “List all exported functions and their dependencies.”
- Identify — “Flag functions with high cyclomatic complexity (score > 15).”
- Plan — “Propose a refactoring plan with estimated impact per function.”
- Execute — “Refactor one function at a time, verifying tests pass after each.”
- Verify — “Run the existing test suite and report regressions.”
Each sub-task has a clear success criterion. If any step fails, the agent stops and reports — rather than producing a half-correct refactor that compiles but breaks three features silently.
Output Format Control
The most common failure mode in AI agent output is format inconsistency. Models naturally drift toward conversational style. Counteract this with explicit, repeated format specifications:
- Use schema examples, not descriptions. Instead of “output JSON,” provide the exact JSON schema with types, required fields, and an example.
- Repetition works. Mention the output format at the beginning, middle, and end of your system prompt. Models weight recent and early tokens more heavily than middle ones.
- Use negative examples. Show what NOT to output: “Do not wrap the JSON in markdown code blocks. Do not add explanatory text before or after.”
- Post-process and retry. If the output does not parse as valid JSON, strip markdown wrappers and retry. If it still fails, request regeneration with the format error explained.
The Preconfigured Advantage
All of these techniques are encoded into FlickClaw's preconfigured agents. You do not need to write, test, and iterate on prompt structure — agents in the catalog already include optimized prompts, quality gates, and framework adapters tested across real projects.