Intermediate LevelAI, LLMs, Embeddings & MCP
What are Self-Correction and Reflection loops in AI Agent design?
❓ Interview Prompt: Explain reflection agents, error feedback loops, test-driven self-correction, and output quality enhancement.
Standard LLMs produce code or text in a single pass without reviewing their output. Self-Correction and Reflection Loops instruct an agent to inspect and critique its own work before returning a final answer.
Reflection Pattern Steps:
1
Generation: Primary agent generates code or text.
2
Execution / Critique: Code is executed in a sandbox (or inspected by a secondary Critic agent), generating compiler error messages or unit test failures.
3
Refinement: Error trace is fed back into the prompt: "Your previous code failed with Error X. Fix the code."
4
Looping: Repeats until unit tests pass or maximum iterations are reached.
Code Implementation Example
typescript
async function generateSelfCorrectedCode(problem: string): Promise<string> {
let code = await agent.generate("Write TS function for: " + problem);
for (let attempt = 0; attempt < 3; attempt++) {
const testResult = runSandboxTests(code);
if (testResult.passed) return code; // Success!
// Feed error traceback back into model prompt for self-correction
code = await agent.generate("Your code failed with error:\n" + testResult.error + "\n\nFix the function code:");
}
return code;
}💡Agent self-correction loop executing sandbox tests and feeding back error traces.
🎯 Interview Key Takeaway
Reflection loops test agent output against compilers or critique prompts, iteratively fixing errors before returning answers.
#Reflection#Self-Correction#Agents#Testing#Intermediate
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