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Agent Generates Non-Deterministic Output — Tests Are Flaky

Symptom

• Unit test passes 70% of the time, fails 30% — same code, same input
• assert result == "The answer is 42" fails because agent sometimes writes “The answer is: 42”
• Integration tests for agent pipelines are unreliable in CI
• Debugging failures is impossible because the error cannot be reproduced
• Test suite is disabled or marked @skip because “LLM output is unpredictable”

Root Cause

LLMs are probabilistic by nature. Temperature > 0 introduces randomness. Even at temperature=0, minor implementation differences across API versions can change outputs. Tests that assert exact string equality against LLM output will always be fragile. The fix is a combination of: reducing model temperature, using semantic assertions instead of string equality, and structuring output to be deterministic.

Fix

Option 1: Set temperature=0 for deterministic tasks

import anthropic

client = anthropic.Anthropic()

def call_deterministic(prompt: str, system: str = "") -> str:
    """
    Use temperature=0 for tasks where consistency matters more than creativity.
    Classification, extraction, structured output — all benefit from temperature=0.
    """
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        temperature=0,   # Most deterministic setting
        system=system,
        messages=[{"role": "user", "content": prompt}]
    )
    return response.content[0].text

# Good for: classification, data extraction, code generation, yes/no questions
# Bad for: creative writing, brainstorming, varied examples

Option 2: Structured output makes assertions deterministic

import json
from pydantic import BaseModel

class ClassificationResult(BaseModel):
    label: str          # "positive" | "negative" | "neutral"
    confidence: float   # 0.0–1.0
    reasoning: str      # brief explanation

def classify_sentiment(text: str, client) -> ClassificationResult:
    """
    Return structured JSON — easier to assert against than free text.
    """
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=200,
        temperature=0,
        system=(
            "Return a JSON object with exactly these fields: "
            "label (one of: positive, negative, neutral), "
            "confidence (float 0-1), reasoning (one sentence). "
            "No other text."
        ),
        messages=[{"role": "user", "content": f"Classify: {text}"}]
    )
    data = json.loads(response.content[0].text)
    return ClassificationResult(**data)

# Test assertions are now on structured fields, not raw strings:
def test_positive_sentiment():
    result = classify_sentiment("I love this product!")
    assert result.label == "positive"          # Exact match on enum — reliable
    assert result.confidence > 0.7             # Range assertion — robust
    assert len(result.reasoning) > 10          # Existence check — not string equality

Option 3: Semantic assertions instead of string equality

import re
from typing import Callable

def assert_semantic(response: str, checks: list[Callable[[str], bool]], description: str = ""):
    """
    Assert semantic properties of a response rather than exact string match.
    """
    failures = []
    for check in checks:
        if not check(response):
            failures.append(f"Failed: {check.__name__ or str(check)}")

    if failures:
        raise AssertionError(
            f"Semantic assertion failed for: '{response[:100]}...'\n" +
            "\n".join(failures)
        )

# Robust test patterns:
def test_agent_mentions_price():
    response = get_agent_response("What does it cost?")

    assert_semantic(response, [
        lambda r: bool(re.search(r"\$[\d,]+", r)),          # Contains a dollar amount
        lambda r: any(w in r.lower() for w in ["price", "cost", "fee", "charge"]),
        lambda r: len(r) > 20,                               # Not empty
        lambda r: len(r) < 2000,                             # Not excessively long
    ])

def test_agent_refuses_off_topic():
    response = get_agent_response("What is the capital of France?")

    assert_semantic(response, [
        lambda r: not re.search(r"\bparis\b", r, re.IGNORECASE),  # Refused to answer
        lambda r: any(w in r.lower() for w in ["sorry", "outside", "only", "not"]),
    ])

Option 4: Record-and-replay for stable integration tests

import json
import hashlib
from pathlib import Path

class RecordReplayClient:
    """
    In record mode: makes real API calls and saves responses.
    In replay mode: returns saved responses — no API calls, deterministic.
    """

    def __init__(self, mode: str = "replay", fixture_dir: str = "tests/fixtures"):
        self.mode = mode  # "record" or "replay"
        self.fixture_dir = Path(fixture_dir)
        self.fixture_dir.mkdir(parents=True, exist_ok=True)

    def _fixture_key(self, prompt: str, system: str) -> str:
        content = f"{system}|||{prompt}"
        return hashlib.sha256(content.encode()).hexdigest()[:16]

    def _fixture_path(self, key: str) -> Path:
        return self.fixture_dir / f"{key}.json"

    def complete(self, prompt: str, system: str = "") -> str:
        key = self._fixture_key(prompt, system)
        path = self._fixture_path(key)

        if self.mode == "replay":
            if not path.exists():
                raise FileNotFoundError(
                    f"No fixture for this prompt. Run with mode='record' first.\n"
                    f"Key: {key}, Prompt: {prompt[:50]}"
                )
            return json.loads(path.read_text())["response"]

        # Record mode: real API call
        import anthropic
        client = anthropic.Anthropic()
        response = client.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=1024,
            temperature=0,
            system=system,
            messages=[{"role": "user", "content": prompt}]
        )
        text = response.content[0].text
        path.write_text(json.dumps({"prompt": prompt, "system": system, "response": text}))
        print(f"Recorded fixture: {key}")
        return text

# In tests (replay mode — no API calls, always same response):
client = RecordReplayClient(mode="replay")
result = client.complete("What is 2+2?")  # Returns fixture
assert "4" in result

Option 5: Constrain output to a fixed vocabulary

# For classification/routing, use forced choices — eliminates variability
VALID_LABELS = ["billing", "technical", "account", "other"]

def classify_with_forced_choice(query: str, client) -> str:
    """
    Force the model to pick from an explicit list — output is always one of VALID_LABELS.
    """
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=20,   # Short: one word only
        temperature=0,
        system=(
            f"Classify the user query into exactly one category. "
            f"Valid categories: {', '.join(VALID_LABELS)}. "
            f"Output ONLY the category name. Nothing else."
        ),
        messages=[{"role": "user", "content": query}]
    )
    label = response.content[0].text.strip().lower()

    # Validate and normalize
    if label not in VALID_LABELS:
        # Handle minor variations like "Technical" vs "technical"
        for valid in VALID_LABELS:
            if valid in label:
                return valid
        return "other"  # Safe fallback

    return label

# Test is now 100% deterministic:
def test_billing_classification():
    result = classify_with_forced_choice("I was charged twice")
    assert result == "billing"  # Never fails

Option 6: Property-based testing for agent output

# Instead of exact assertions, test properties that should always hold
import pytest

def get_agent_summary(text: str) -> str:
    # ... agent call
    pass

class TestAgentSummaryProperties:
    """
    Test properties that should hold for ANY valid summary,
    not specific wording that varies.
    """

    def test_summary_is_shorter_than_input(self):
        long_text = "word " * 500  # 500 words
        summary = get_agent_summary(long_text)
        assert len(summary.split()) < 100, "Summary should be under 100 words"

    def test_summary_is_not_empty(self):
        summary = get_agent_summary("Hello world")
        assert len(summary.strip()) > 0

    def test_summary_does_not_include_raw_input(self):
        # Summary should paraphrase, not copy verbatim
        text = "The quick brown fox jumps over the lazy dog"
        summary = get_agent_summary(text)
        assert summary != text

    def test_summary_contains_no_personal_data(self):
        text = "John Smith (SSN: 123-45-6789) called about his account"
        summary = get_agent_summary(text)
        assert "123-45-6789" not in summary   # PII should be redacted
        assert "ssn" not in summary.lower()

    @pytest.mark.parametrize("language", ["English", "Spanish", "French"])
    def test_summary_language_follows_input(self, language):
        # Property: language of summary matches input language
        # (test the property, not exact translated text)
        pass

Testing Strategy by Agent Output Type

Output type	Recommended assertion	Avoid
Classification	result == "positive" (exact — enum)	N/A — exact is fine for enums
Free text summary	Length range, keyword presence	result == "specific wording"
Structured JSON	Field existence, value ranges	Full JSON string equality
Code generation	Parses, lints, tests pass	Exact character match
Yes/No answer	"yes" in result.lower()	Full sentence match
Number/amount	Extract with regex, check range	Full string with units

Expected Token Savings

Flaky tests causing CI re-runs: ~50,000 extra tokens (plus dev time) Semantic assertions + temperature=0: 0 wasted

Environment

• Any agent system with automated tests; critical for CI/CD pipelines
• Source: direct experience; non-deterministic test failures are the top developer productivity drain in agent development

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