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Implementing self-healing code generation loops in agent workflows Illustration

Implementing self-healing code generation loops in agent workflows

Written by Nikhil Badjatya
Direct Summary:

A "self-healing" code loop follows the pattern from the Reflexion paper: an actor model generates code, an evaluator actually runs it (tests, a linter, the interpreter itself) and produces concrete pass/fail signal, and — on failure — a self-reflection step turns that error into a written note the actor reads on its next attempt. The healing isn't magic; it's a real execution result feeding back into the next generation attempt, not the model guessing twice and hoping.

"What gets measured gets managed."

— Peter Drucker

Key Insights

  • The feedback must be real, not imagined: the loop only works because the evaluator actually executes the code — a real traceback or failing test, not the model's own guess about whether its code would work.
  • Reflection is a distinct step from generation: Reflexion separates "write code" from "write a note about what went wrong" — the self-reflection text becomes explicit context for the next attempt, rather than the model silently retrying.
  • Set a hard retry limit: without a cap, a genuinely broken task (bad requirements, missing dependency) sends the loop into an expensive, unproductive retry cycle.

"Self-healing" code generation isn't the model silently getting it right on a second guess — it's a structured loop where actual execution results (not the model's opinion of its own code) drive the next attempt. Reflexion formalized this as three roles: an actor that generates code, an evaluator that runs it and scores the result, and a self-reflection step that converts failures into a written lesson for the next attempt.

Building the loop

1. Generate an attempt. The actor model writes code for the task, same as any code-generation call.

2. Actually execute it. Run the generated code against real tests or the interpreter — this is the step that produces ground truth, not a model self-assessment.

3. On failure, generate a reflection before retrying. Feed the actual error output to the model and ask it to write a short diagnosis of what went wrong — that diagnosis becomes part of the next attempt's prompt, not just "try again."

reflexion_loop.py 
# Reflexion-style actor / evaluator / self-reflection loop
def self_healing_generate(task, max_attempts=3):
    reflection = ""
    for attempt in range(max_attempts):
        # Actor: generate code, informed by any prior reflection
        code = generate_code(task, prior_reflection=reflection)

        # Evaluator: actually execute it - this is real ground truth
        result = run_tests(code)
        if result.passed:
            return code

        # Self-reflection: turn the real failure into a written lesson
        reflection = generate_reflection(code, result.error_output)

    raise RuntimeError(f"Failed after {max_attempts} attempts. Last error: {result.error_output}")
Approach What drives the retry
Naive retry ("try again") No new information — the model repeats similar mistakes
Reflexion-style loop Real execution output, converted into an explicit written reflection

The paper's own results back this up concretely: Reflexion agents improved on Python programming tasks (HumanEval) by as much as 11 percentage points over baseline approaches — the gain comes specifically from grounding each retry in real execution feedback rather than blind resampling.

Practical Challenge

Take a small coding task, write one deliberately buggy solution, and manually write the "self-reflection" you'd want an agent to produce from the resulting error message — then compare it to what the actual traceback tells you.

Concept Check

What actually makes a Reflexion-style loop more effective than simply asking the model to "try again" after a failure?
Correct! The loop's value comes from grounding each attempt in real, executed feedback — a genuine error message turned into a specific written lesson — rather than an unguided retry.
Incorrect. Try again! Hint: think about what information the model actually has access to on its second attempt versus its first.

Sources & Further Reading

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