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Agent Ignores Environment-Specific Feature Flags — Dev Features Hit Production

Symptom

• Experimental agent behavior rolled out to all production users because flag check was skipped
• USE_NEW_SEARCH_ALGORITHM=true in dev .env file also applies in production
• Feature flag service returns wrong flags because it’s using the dev project key in prod
• A/B test that should only affect 10% of users affects 100% because flag is hardcoded
• Agent uses a new unvalidated tool in production because the flag defaults to True

Root Cause

Feature flag checks are bypassed or misconfigured in several ways: hardcoded fallback defaults that are too permissive, flag service initialized with wrong environment key, or .env file values overriding the intended production flags. Without explicit environment enforcement in the flag system, flags bleed across environments.

Fix

Option 1: Environment-aware feature flag client

import os
from enum import Enum

class Environment(str, Enum):
    DEV = "dev"
    STAGING = "staging"
    PROD = "prod"

class FeatureFlagClient:
    """
    Feature flag client that is explicitly environment-aware.
    Flags are defined per-environment — no cross-contamination possible.
    """

    # Define flags per environment — prod is always conservative
    FLAGS: dict[str, dict[str, bool]] = {
        "use_new_search_algorithm": {
            Environment.DEV: True,       # Enabled for dev testing
            Environment.STAGING: True,   # Enabled for staging validation
            Environment.PROD: False,     # Not yet enabled in prod
        },
        "experimental_tool_use": {
            Environment.DEV: True,
            Environment.STAGING: False,
            Environment.PROD: False,
        },
        "verbose_logging": {
            Environment.DEV: True,
            Environment.STAGING: True,
            Environment.PROD: False,     # Never verbose in prod
        },
    }

    def __init__(self, env: str):
        try:
            self.env = Environment(env)
        except ValueError:
            raise ValueError(
                f"Invalid environment: '{env}'. Must be one of: "
                f"{[e.value for e in Environment]}"
            )

    def is_enabled(self, flag_name: str, default: bool = False) -> bool:
        """
        Check if a flag is enabled for the current environment.
        default only applies if flag is not defined — use False to be safe.
        """
        if flag_name not in self.FLAGS:
            print(f"Unknown feature flag '{flag_name}' — using default={default}")
            return default

        return self.FLAGS[flag_name].get(self.env, False)  # Default False for unknown envs

# Initialize once at startup:
ENV = os.environ.get("AGENT_ENV", "dev")
flags = FeatureFlagClient(env=ENV)

# Usage — cannot use prod-disabled features in prod:
if flags.is_enabled("use_new_search_algorithm"):
    results = new_search(query)
else:
    results = legacy_search(query)

Option 2: Remote feature flag service with environment isolation

import os
import httpx
from functools import lru_cache

class RemoteFlagClient:
    """
    Use a remote feature flag service (LaunchDarkly, Unleash, etc.)
    with environment-specific SDK keys — physical separation of environments.
    """

    # Each environment has a DIFFERENT SDK key — wrong key = wrong flags
    SDK_KEYS = {
        "dev": os.environ.get("LAUNCHDARKLY_SDK_KEY_DEV"),
        "staging": os.environ.get("LAUNCHDARKLY_SDK_KEY_STAGING"),
        "prod": os.environ.get("LAUNCHDARKLY_SDK_KEY_PROD"),
    }

    def __init__(self, env: str, base_url: str = "https://app.launchdarkly.com"):
        self.env = env
        self.sdk_key = self.SDK_KEYS.get(env)
        if not self.sdk_key:
            raise RuntimeError(
                f"No SDK key for environment '{env}'. "
                f"Set LAUNCHDARKLY_SDK_KEY_{env.upper()} environment variable."
            )
        self.base_url = base_url
        self._cache: dict[str, bool] = {}

    async def is_enabled(self, flag_key: str, agent_id: str = "default") -> bool:
        """
        Check flag from remote service — flag state is managed externally.
        Using wrong SDK key returns flags for the wrong environment.
        """
        cache_key = f"{flag_key}:{agent_id}"
        if cache_key in self._cache:
            return self._cache[cache_key]

        async with httpx.AsyncClient() as client:
            resp = await client.get(
                f"{self.base_url}/sdk/latest-all",
                headers={"Authorization": self.sdk_key},
                timeout=5
            )
            flags = resp.json()
            flag = flags.get("flags", {}).get(flag_key, {})
            enabled = flag.get("on", False)

        self._cache[cache_key] = enabled
        return enabled

Option 3: Flag audit — verify production flags at startup

def audit_production_flags(flags: FeatureFlagClient) -> list[str]:
    """
    Check for flags that should never be enabled in production.
    Run at agent startup in prod to catch misconfigurations.
    """
    if flags.env != Environment.PROD:
        return []  # Only audit prod

    SHOULD_BE_DISABLED_IN_PROD = [
        "debug_mode",
        "verbose_logging",
        "experimental_tool_use",
        "bypass_rate_limits",
        "skip_validation",
        "mock_external_apis",
    ]

    violations = []
    for flag in SHOULD_BE_DISABLED_IN_PROD:
        if flags.is_enabled(flag):
            violations.append(
                f"DANGER: '{flag}' is enabled in PRODUCTION. "
                f"This flag should only be enabled in dev/staging."
            )

    return violations

# At startup in prod:
violations = audit_production_flags(flags)
if violations:
    for v in violations:
        print(f"FLAG AUDIT VIOLATION: {v}")
    raise RuntimeError(
        "Production flag audit failed. Fix flags before starting agent in prod."
    )

Option 4: Gradual rollout with percentage-based flags

import hashlib

class RolloutFlagClient:
    """
    Percentage-based rollout flags — only enable for X% of users/sessions.
    Deterministic: same user_id always gets same result.
    """

    # Flag definitions: (prod_percentage, dev_percentage, staging_percentage)
    ROLLOUTS = {
        "new_summarizer": (0, 100, 50),       # 0% prod, 100% dev, 50% staging
        "faster_retrieval": (10, 100, 100),   # 10% prod rollout
        "gpt4_responses": (5, 100, 20),       # 5% prod, controlled rollout
    }

    def __init__(self, env: str):
        self.env = env
        self._env_index = {"dev": 1, "staging": 2, "prod": 0}.get(env, 0)

    def _bucket(self, flag_name: str, user_id: str) -> int:
        """Deterministic bucket 0-99 based on flag+user"""
        key = f"{flag_name}:{user_id}"
        hash_val = int(hashlib.sha256(key.encode()).hexdigest(), 16)
        return hash_val % 100

    def is_enabled(self, flag_name: str, user_id: str = "default") -> bool:
        if flag_name not in self.ROLLOUTS:
            return False

        rollout_pct = self.ROLLOUTS[flag_name][self._env_index]

        if rollout_pct == 0:
            return False
        if rollout_pct == 100:
            return True

        # Percentage rollout — same user always gets same result
        return self._bucket(flag_name, user_id) < rollout_pct

rollout = RolloutFlagClient(env="prod")

# In prod with 10% rollout:
# 10% of user IDs → True
# 90% of user IDs → False
# Same user_id ALWAYS gets the same result (deterministic)
if rollout.is_enabled("faster_retrieval", user_id=session.user_id):
    use_faster_retrieval()

Option 5: Flag change detection and alerts

import json
from pathlib import Path
from datetime import datetime

class FlagChangeMonitor:
    """
    Detect when flags change and alert before they affect production.
    """

    def __init__(self, snapshot_path: str = "flag_snapshots.json"):
        self.path = Path(snapshot_path)
        self._history: list[dict] = []
        if self.path.exists():
            self._history = json.loads(self.path.read_text())

    def snapshot(self, flags: FeatureFlagClient, flag_names: list[str]) -> dict:
        """Take a snapshot of current flag state"""
        state = {
            "env": flags.env.value,
            "timestamp": datetime.utcnow().isoformat(),
            "flags": {name: flags.is_enabled(name) for name in flag_names}
        }
        return state

    def compare_and_alert(self, current: dict, prev: dict) -> list[str]:
        """Find flags that changed between snapshots"""
        changes = []
        for flag, value in current["flags"].items():
            prev_value = prev["flags"].get(flag)
            if prev_value is not None and prev_value != value:
                changes.append(
                    f"Flag '{flag}' changed: {prev_value} → {value} "
                    f"(env: {current['env']})"
                )
        return changes

    def record(self, flags: FeatureFlagClient, flag_names: list[str]) -> list[str]:
        current = self.snapshot(flags, flag_names)
        changes = []

        if self._history:
            prev = next(
                (s for s in reversed(self._history) if s["env"] == current["env"]),
                None
            )
            if prev:
                changes = self.compare_and_alert(current, prev)

        self._history.append(current)
        self.path.write_text(json.dumps(self._history[-50:], indent=2))  # Keep last 50
        return changes

monitor = FlagChangeMonitor()
changes = monitor.record(flags, ["new_search_algorithm", "experimental_tool_use"])
if changes:
    for change in changes:
        print(f"FLAG CHANGE DETECTED: {change}")
    # Send to alerting system, Slack, PagerDuty, etc.

Option 6: Test that flags behave correctly per environment

import pytest

@pytest.fixture
def dev_flags():
    return FeatureFlagClient(env="dev")

@pytest.fixture
def prod_flags():
    return FeatureFlagClient(env="prod")

class TestFeatureFlags:
    """Ensure flags are correctly set per environment"""

    def test_experimental_flags_disabled_in_prod(self, prod_flags):
        """Experimental features must never be enabled in prod by default"""
        experimental = [
            "experimental_tool_use",
            "debug_mode",
            "bypass_rate_limits",
        ]
        for flag in experimental:
            assert not prod_flags.is_enabled(flag), (
                f"'{flag}' is enabled in PROD — this is dangerous!"
            )

    def test_experimental_flags_enabled_in_dev(self, dev_flags):
        """Experimental features should be available for dev testing"""
        experimental = ["experimental_tool_use", "debug_mode"]
        for flag in experimental:
            assert dev_flags.is_enabled(flag), (
                f"'{flag}' should be enabled in dev for testing"
            )

    def test_invalid_environment_raises(self):
        with pytest.raises(ValueError):
            FeatureFlagClient(env="invalid_env")

    def test_unknown_flag_returns_safe_default(self, prod_flags):
        assert not prod_flags.is_enabled("nonexistent_flag", default=False)

Flag Configuration Risks

Risk	Scenario	Prevention
Default=True	Unknown flag defaults to enabled in prod	Always default=False
Shared flag key	Same LaunchDarkly key for dev+prod	Separate SDK keys per env
.env override	Dev env var overrides prod flag service	Explicit env validation
Missing flag → defaults	Flag removed from service → default applied	Audit missing flags
Hardcoded True	Developer hardcodes True during testing	Code review, flag audit

Expected Token Savings

Experimental agent behavior in prod causes incident + rollback: ~200,000 tokens Explicit per-environment flags + prod audit: 0 incidents

Environment

• Any agent with multiple deployment environments and feature flags for controlled rollout
• Source: direct experience; flag misconfiguration causes the most invisible production regressions in agent systems

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