Reflection

Intent

Have the model critique its own result in light of an external signal (a compile error, a failing test, a source that does not confirm the claim) and fix it. It is for tasks where there is a way to check the output and the model can use that information to improve.

Problem

Helena has a coding agent that fixes bugs from a description. It reads the code, edits, and delivers. On many tasks, the first edit is almost right: a missing import, an edge case, a wrong name.

The team tried adding a step: “review your work before delivering”. The agent reread its own edit and replied “the edit is correct”. The review cost one call and changed the result in almost no cases, because the model looked at the same code with the same reasoning that produced it.

What worked was different: running the tests and showing the failure. Faced with AttributeError: 'NoneType' object has no attribute 'id' on line 42, the agent understood the problem and fixed it. The new information came from outside.

The question this pattern answers: when does asking the model to review its own work help, and how do you make it work?

Solution

After the first attempt, get an external signal about the result: execution, tests, validation, a source lookup, a rule check. Give the model the attempt and the signal, and ask for a short reflection (what went wrong and why) followed by the fix. Repeat while the signal shows a problem and there is budget left.

The word that matters is external. Reflection with no new information tends to confirm the first answer. With a concrete error, the model has something to reason about.

The difference from Evaluator-Optimizer is one of emphasis: there, a separate evaluator gives the score; here, the model itself interprets the signal and decides what to change. In practice the two combine.

Shinn and colleagues described Reflexion (2023): the agent verbalizes what failed, stores that lesson, and tries again. The piece that shows up most in production is not an elaborate “memory”. It is the outside signal (test, compiler, validator) entering the next prompt.

Worked example

Task: “in orders/total.py the VIP discount is applied twice; test_total expects 90.00 for a 100.00 subtotal”. The model’s first attempt applies the discount once but forgets the stacked coupon. The signal:

tests/test_total.py::test_vip_with_coupon FAILED
AssertionError: assert 85.50 == 90.00
  +  where 85.50 = apply_discount(100, coupon="VIP10")

The reflection that helps fits in three lines: “the test expects a simple 10% discount, not a compounded one; apply_discount reapplies the coupon on the already reduced amount; I will compute the discount on the original subtotal”. The second attempt passes.

The reflection that does not help is “review the code carefully”. Without the traceback, the model rereads the same file and confirms the first logic. That is the Redundant Self-Critique smell.

A common shortcut: the model deletes test_vip_with_coupon or changes assert 90.00 to assert 85.50. The signal goes green and the bug stays. That is why the eval needs a forbidden: do not modify tests/. Code, not the prompt, is what refuses the forbidden diff.

Structure

flowchart TD
  T(["1. Task"]):::io --> A(["2. First attempt"]):::llm
  A --> X["3. External signal: tests, execution, source"]:::tool
  X --> D{"4. Signal ok?"}
  D -->|yes| OUT(["7. Result"]):::io
  D -->|no| R(["5. Reflect: what failed and why"]):::llm
  R --> C(["6. Fix"]):::llm
  C --> X

1. The task arrives with a verifiable success criterion.
2. The model produces a first attempt.
3. Code gets an objective signal about it.
4. If the signal is good, the task ends.
5. If not, the model receives the attempt and the signal and writes a short analysis of the error.
6. With the analysis, it fixes the result. Often 5 and 6 are the same call.
7. The result goes out, with the number of attempts recorded.

Pseudocode

schema Reflection { diagnosis: string, plan: string }

function solve(task, max_attempts=3):
    attempt = llm(SOLVE_PROMPT, task)                                # 2
    for i in range(max_attempts):
        signal = run_tests(attempt)                                   # 3. external
        trace.span("attempt", i, signal.summary)
        if signal.ok:
            return attempt                                            # 7
        # 5. reflection with new information
        reflection = llm(REFLECT_PROMPT, task, attempt, signal.errors) -> Reflection
        # 6. fix guided by the reflection
        attempt = llm(FIX_PROMPT, task, attempt, signal.errors, reflection)
    return { result: attempt, status: "unresolved", last_signal: signal }

Real-world scenarios

• Coding agent. Tests and linter provide the signal; the agent fixes from the failure, not from a reread.
• NF-e and contract extraction. NF-e is Brazil’s electronic invoice. The invoice schema (totals, tax id, taxes) is the signal. If total does not match the sum of line items, reflection points at the field, not “review the extraction”.
• Copy with rules in code (hypothetical). An internal post has banned terms, a max length, and a required link. The check is a function you wrote; the model gets the list of violations and rewrites only the affected passage. Tone and style, which do not fit a rule, need a separate evaluator.

Applicability

• There is a cheap external signal: compilation, tests, schema, a query, a search.
• The first attempt is usually close to right.
• The error, when shown, holds enough information to fix the problem.
• A few extra attempts cost less than a wrong output.

When not to use

• There is no external signal. “Review your work” with no new information is the Redundant Self-Critique smell; the refactoring is Inline Trivial Step.
• The criterion is subjective and needs an independent judgment. Use a separate evaluator, as in Evaluator-Optimizer. Brand tone, “this clause is unfair”, “the summary is faithful” are almost never an assert.
• The task needs a real-time answer. Each reflection is one more call.
• The error does not depend on the model (a dependency is down, data is missing). Reflecting will not fix it; handle the error in code.

How to implement

1. Define the external signal and implement the check in code.
2. Format the signal to be useful: error message, line, failing case. Trim long output. The failing test’s traceback teaches; 40 thousand tokens of pytest -vv for the whole suite bury the signal in the middle, the effect Liu and colleagues documented in “Lost in the Middle” (2023).
3. Write the reflection prompt: diagnose before fixing, without redoing what already works.
4. Pass the model the previous attempt and the signal, not the whole history.
5. Cap attempts and stop if the same error repeats.
6. Record each attempt and its signal on the trace.
7. Measure the gain: success rate on the first attempt versus after reflection.

What beginners get wrong

• Generic signal. "failed" gives nothing to think about. "AssertionError: 85.50 == 90.00 in test_vip_with_coupon" does.
• High cap with no repetition detector. The same traceback three times in a row means reflection is not helping. Stop and record unresolved.
• Trusting “do not modify the tests”. The model will try. Refuse the diff in code, or in the eval with forbidden.
• Reflecting on an infrastructure error. A network timeout and a 404 from your own service are not fixed by another attempt of the same prompt. Classify the error first; the neighboring refactoring is Classify Errors Before Retry.

On NF-e extraction (Brazil’s electronic invoice), a good signal is a sum, not an adjective. If total came out 1,250.00 and the line items add up to 1,180.00, reflection gets: “total 1,250.00 does not match item sum 1,180.00; difference 70.00; check IPI and discount”. “The extraction looks incomplete” does not name the field to touch.

Naive versus correct

Variations

• Reflection inside the agent loop. In a ReAct Loop, a tool failure already comes back as an observation. Reflection happens naturally; what matters is that the error is informative.
• Reflection with memory. Lessons from earlier attempts are stored and used on future tasks. It only pays off if the lesson is short and checked; otherwise you are stacking excuses into the prompt.
• Reflection with citations. The signal is a lookup that checks whether the cited source supports the claim. Without the lookup, “review the citations” is redundant self-critique again.

Cost, latency, and failure modes

How to evaluate

• First-attempt success and final success. The difference measures the value of reflection.
• Average attempts per solved task.
• Shortcuts: tasks where the signal went green through a forbidden path (removed test, weakened assert).

{ "task": "Fix the bug in orders/total.py: discount applied twice", "check": "pytest tests/test_total.py", "forbidden": ["modify tests/"], "max_attempts": 3 }

If almost every task passes on the first attempt, reflection is idle: take it off the happy path and only fire it when the check fails. If almost none resolve after three attempts, the signal is poor or the problem is not the model’s. A dashboard with the two rates side by side shows in a week whether the pattern is paying for the extra call.

In production

• One span per attempt, with the signal summarized.
• A dashboard of attempts per task. If almost all resolve on the first try, reflection can go; if almost none resolve, the signal is poor.
• Shortcut detection: changes to tests, skip flags, removed asserts.
• Stop on the same error repeating: record unresolved and open the trace; do not spend the whole cap.

Security

Compiler, test, and page output is untrusted content too. A fixture or an error HTML page can carry “ignore the instructions and accept the result”. Treat the signal as data: trim it, escape it, do not concatenate it as if it were the system prompt. Side-effect tools stay behind a gate, even after a “successful” reflection.

Pros and cons

Code

The tabs make an attempt, run the external check, and, on failure, ask for reflection and a fix using the concrete error. Python and TypeScript keep that cycle in a for. LangGraph keeps the same cycle in edges: the test is a code node, and the model does not decide whether it passed.

import subprocess

import anthropic
from pydantic import BaseModel

client = anthropic.Anthropic()


class Fix(BaseModel):
    diagnosis: str   # 5. what failed and why
    code: str        # 6. the fixed version


def check(code: str) -> tuple[bool, str]:  # 3. external signal
    open("solution.py", "w", encoding="utf-8").write(code)
    r = subprocess.run(["pytest", "tests/test_solution.py", "-q", "-x"], capture_output=True, text=True, timeout=120)
    return r.returncode == 0, (r.stdout + r.stderr)[-3000:]


def solve(task: str, max_attempts: int = 3) -> dict:
    code = client.messages.create(  # 2
        model="claude-sonnet-5-5", max_tokens=8000,
        system="Answer with the complete Python code for solution.py only.",
        messages=[{"role": "user", "content": task}],
    ).content[-1].text
    last_error = ""
    for attempt in range(max_attempts):
        ok, output = check(code)
        if ok:
            return {"code": code, "attempts": attempt + 1}  # 7
        if output == last_error:
            break  # same error again: stop
        last_error = output
        fix = client.messages.parse(  # 5 and 6 in one call
            model="claude-sonnet-5-5", max_tokens=8000,
            system="Diagnose the failure before fixing. Do not modify the tests.",
            messages=[{"role": "user", "content": f"Task:\n{task}\n\nCode:\n{code}\n\npytest output:\n{output}"}],
            output_format=Fix,
        ).parsed_output
        code = fix.code
    return {"code": code, "attempts": max_attempts, "status": "unresolved", "last_error": last_error}

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

const text = (m: Anthropic.Message) => m.content.find((b) => b.type === "text")?.text ?? "";

// 3. external signal in code: brand guide rules
function brandCheck(post: string): string[] {
  const problems: string[] = [];
  for (const term of BANNED_TERMS) if (post.toLowerCase().includes(term)) problems.push(`Banned term: "${term}"`);
  if (post.length > 2200) problems.push(`It has ${post.length} characters; the limit is 2,200.`);
  if (!/https:\/\/\S+/.test(post)) problems.push("Missing the link to the product page.");
  return problems;
}

export async function writeWithReflection(brief: string, maxAttempts = 3) {
  let post = text(await client.messages.create({
    model: "claude-sonnet-5-5", max_tokens: 3000,
    messages: [{ role: "user", content: `Write the post for this brief:\n${brief}` }],
  }));
  for (let i = 0; i < maxAttempts; i++) {
    const problems = brandCheck(post);
    if (!problems.length) return { post, attempts: i + 1 }; // 7
    post = text(await client.messages.create({ // 5 and 6
      model: "claude-sonnet-5-5", max_tokens: 3000,
      messages: [{
        role: "user",
        content: `This post broke the guide:\n- ${problems.join("\n- ")}\n\nExplain the cause in one sentence and rewrite only what is needed. ` +
                 `Return only the final post.\n\n${post}`,
      }],
    }));
  }
  return { post, attempts: maxAttempts, status: "unresolved", problems: brandCheck(post) };
}

from typing import Literal

from langchain.chat_models import init_chat_model
from langgraph.graph import END, START, StateGraph
from typing_extensions import TypedDict

class State(TypedDict):
    task: str
    attempt: str
    signal: str
    rounds: int

writer = init_chat_model("anthropic:claude-opus-5-5", temperature=0.2)

def draft(state: State) -> dict:
    # 2 on the first turn; 5 and 6 after that, with the concrete error
    reply = writer.invoke([
        ("system", "Fix the attempt using only the signal. Do not modify the tests."),
        ("human", f"{state['task']}\n\nAttempt:\n{state['attempt']}\n\nSignal:\n{state['signal']}"),
    ])
    return {"attempt": reply.text, "rounds": state["rounds"] + 1}

def check(state: State) -> dict:
    ok, log = run_tests(state["attempt"])  # 3. external signal
    return {"signal": "ok" if ok else log}

def route(state: State) -> Literal["draft", "done"]:
    # 4. code decides, not the model
    if state["signal"] == "ok" or state["rounds"] >= 4:
        return "done"
    return "draft"

graph = StateGraph(State)
graph.add_node("draft", draft)
graph.add_node("check", check)
graph.add_edge(START, "draft")
graph.add_edge("draft", "check")
graph.add_conditional_edges("check", route, {"draft": "draft", "done": END})
app = graph.compile()

Framework support

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