Building an AI-Powered Authentication System with Temporal.io

The Vision: Pure Temporal, AI-Enhanced Authentication

Over the past few days, I’ve been immersed in building an authentication system that combines Temporal.io workflows with AI-enhanced security. The goal was ambitious: every operation, from user registration to email verification, password resets, AI fraud detection, and login processes, would flow through reliable, observable, and retryable Temporal workflows. In practice, this dream evolved into a hybrid system—one that balances Temporal orchestration with pragmatic fallbacks—while remaining production-ready.

Why Temporal.io?

Temporal.io’s appeal is undeniable. It allows developers to define workflows where each step can be retried automatically, observed in real-time, and compensated if an operation fails. For example, a user registration workflow can integrate AI fraud detection, create the user in the database, and send a verification email, all while handling failures gracefully:

@workflow.defnclass UserRegistrationWorkflow: """Reliable, observable user registration workflow""" async def run(self, user_data): fraud_score = await workflow.execute_activity("ai_fraud_detection", user_data, retry_policy=RetryPolicy(maximum_attempts=3)) user = await workflow.execute_activity("create_user", user_data) await workflow.execute_activity("send_verification_email", user.email, retry_policy=RetryPolicy(initial_interval=timedelta(seconds=1), maximum_interval=timedelta(minutes=5))) return {"success": True, "user_id": user.id}

The promise extends to AI-enhanced authentication, where login attempts can be analyzed for behavioral anomalies and fraud risk, dynamically enforcing MFA for higher-risk scenarios:

@workflow.defn class AIAuthenticationWorkflow: """AI-enhanced authentication workflow""" async def run(self, login_data): fraud_result = await workflow.execute_activity("analyze_registration_fraud_risk", login_data) behavior_result = await workflow.execute_activity("adaptive_authentication_challenge", login_data) if fraud_result.risk_score > 0.7: await workflow.execute_activity("require_mfa", login_data) return {"authenticated": True, "security_level": "high"}

Setting Up the Infrastructure

Getting the stack running was surprisingly straightforward. Using Docker Compose, I spun up a Temporal server, Python workers, and a backend API that communicated seamlessly:

services: temporal: image: temporalio/auto-setup:1.22.6 environment: - DB=postgresql - POSTGRES_SEEDS=postgres worker: build: ./backend command: ["python", "worker.py"] backend: build: ./backend command: ["uvicorn", "app.main:app", "--reload"]

Once the stack was up, basic workflows executed flawlessly. Observing them in the Temporal UI—seeing retries, timing, and execution history in real time—was deeply satisfying. Even a simple ping workflow gave an immediate sense of progress:

@workflow.defnclass PingWorkflow: @workflow.run async def run(self, name: str) -> str: result = await workflow.execute_activity("ping_activity", name, start_to_close_timeout=timedelta(seconds=30)) return result

Reality Hits: Sandbox Restrictions

The honeymoon period was short-lived. Complex authentication workflows began failing with cryptic errors:

RuntimeError: Failed validating workflow UserRegistrationWorkflowRestrictedWorkflowAccessError: Cannot access pathlib.Path.expanduser.__call__ from inside a workflow

Temporal workflows run in a sandbox with restrictions on , restricting filesystem access, network calls, non-deterministic operations, and certain Python standard library functions. Even Pydantic configurations with .env files triggered failures:

from app.config import settingsclass Settings(BaseSettings): DATABASE_URL: str TEMPORAL_HOST: str = "localhost:7233" class Config: env_file = ".env" # triggers Path.expanduser(), restricted in sandbox

These restrictions are intentional safeguards, not a limitation of Temporal itself. They prevent non-determinism errors and ensure workflow code does not produce side effects that could affect other workflows.

The effects of these restrictions can cascade: activities that rely on configuration files or database connections may fail workflow validation, making debugging a careful exercise in isolating imports and refactoring how configuration is loaded.

Workarounds and Hybrid Approaches

Several approaches helped stabilize workflows. First, a sandbox-safe configuration class accessed environment variables directly, avoiding restricted APIs:

class WorkflowSafeSettings: def __init__(self): self.DATABASE_URL = os.getenv("DATABASE_URL", "") self.TEMPORAL_HOST = os.getenv("TEMPORAL_HOST", "localhost:7233")

Next, isolating database operations within activities reduced dependencies that triggered sandbox violations:

@activity.defn(name="isolated*_user_creation")async def create_user_*isolated(user_data: dict): from app.database.connection import get_session from app.models.user import User async with get_session() as session: pass # database operations

Finally, restructuring imports to occur only during activity execution—late binding—resolved many sandbox issues:

def get*_user_*activities(): from app.temporal.activities.user import UserActivities return UserActivities()

Despite these fixes, enforcing a purely Temporal workflow model proved difficult. A hybrid solution emerged as the most reliable approach: workflows are attempted first, but fallback mechanisms ensure functionality when sandbox or configuration issues arise:

async def register*_user(user_*data: UserRegister): try: temporal_client = await get_temporal_client() result = await temporal_client.execute_workflow(UserRegistrationWorkflow.run, user_data, task_queue="auth-task-queue") return {"method": "temporal_workflow", **result} except Exception as e: logger.warning(f"Temporal workflow failed: {e}") result = await direct_user_registration(user_data) return {"method": "direct_registration", **result}

AI Integration: Ollama to the Rescue

While workflows presented challenges, AI integration exceeded expectations. Using Ollama for local AI processing, I implemented fraud detection and password security analysis with actionable results:

class OllamaService: async def analyze_fraud(self, user_data): prompt = f""" Analyze this registration for fraud: Email: {user_data.email} Rate fraud risk 0.0-1.0 and respond with: FRAUD_SCORE: <number> EXPLANATION: <reasoning> """ response = await self.generate_response(prompt) return self.parse_ai_response(response)

Tests returned clear fraud risk scores and explanations. A fallback rule-based system ensured continuous service, maintaining reliability while taking advantage of AI insights wherever possible.

Lessons Learned

Sandbox restrictions are real and must be accounted for from the start. Import chains and configuration patterns can break workflows in subtle ways. Hybrid architectures, combining Temporal workflows with fallback mechanisms, often provide better reliability and user experience than forcing pure orchestration. Local AI integration is mature and adds immediate production value. Observability and graceful degradation are crucial for debugging and maintaining user trust.

Current State and Future Path

Today, the system supports JWT-based authentication, a React frontend, PostgreSQL persistence, AI-powered fraud detection, and a hybrid workflow execution model. While full Temporal adoption awaits workflow-safe configuration refactors, the system provides immediate value while remaining flexible for incremental workflow integration.

Developers considering Temporal should design configurations and imports with sandbox restrictions in mind, implement fallback mechanisms early, and test workflows in isolation. Temporal shines in complex, multi-step processes requiring reliability, observability, and automatic retries, while simpler linear processes may be better suited to direct implementations.

Conclusion

This journey demonstrated that robust systems balance architectural ambition with practical delivery. The authentication system built today is reliable, observable, and production-ready, while maintaining a clear path toward full Temporal integration and AI-enhanced security. Incremental adoption, hybrid patterns, and fallback strategies proved essential for building a real-world system that delivers value from day one.

Tech Stack: Temporal.io, FastAPI, React, PostgreSQL, Docker, Ollama AI, Python 3.11+

GitHub: project-flow-shield