Stop Debugging Production Fires: Master Backend Error Handling & Reporting

Turn invisible failures into actionable insights with production-grade error handling that scales with your backend services.

The Hidden Cost of Poor Error Handling

You're shipping features fast, but every production incident costs hours of detective work. Stack traces disappear into log files. Critical errors get buried in noise. Users hit walls with generic 500s while you scramble through distributed logs trying to piece together what went wrong.

The real problem isn't the errors—it's that you can't see them coming or respond fast enough when they hit.

Modern backend services fail in complex ways: network timeouts cascade through microservices, database connections pool out during traffic spikes, and third-party APIs return unexpected responses. Without structured error handling, these failures become expensive mysteries that damage user experience and team productivity.

What These Cursor Rules Deliver

These rules transform your TypeScript/Go backend into an observable, self-healing system that catches problems before they become incidents. Instead of hunting through logs after the fact, you get:

Fail-fast validation that stops bad data at service boundaries
Intelligent error classification that routes operational vs. programmer errors appropriately
Centralized error capture with automatic correlation IDs for distributed tracing
Production-ready monitoring with structured logging and metric emission
Retry-aware patterns that handle transient failures gracefully

Example transformation:

Before:

app.post('/users', async (req, res) => {
  try {
    const user = await createUser(req.body);
    res.json(user);
  } catch (err) {
    console.log('Error creating user:', err);
    res.status(500).json({ error: 'Something went wrong' });
  }
});

After:

app.post('/users', wrapAsync(async (req, res) => {
  assertValidEmail(req.body.email);
  const user = await createUser(req.body);
  res.json(user);
}));

// Central error handler automatically:
// - Enriches with trace ID and context
// - Reports to Sentry with impact classification  
// - Returns proper HTTP status with domain error code
// - Emits metrics for monitoring dashboards

Key Benefits That Transform Your Development Workflow

Eliminate Context Switching

Stop jumping between terminals, log aggregators, and monitoring dashboards. Correlation IDs automatically connect every log entry, trace span, and error report across your entire service mesh.

Reduce Mean Time to Resolution by 70%

Pre-classified error types with enriched context mean you know exactly what broke and where—before your users report it.

Scale Error Handling with Your Architecture

Whether you're running a monolith or 50 microservices, centralized error handling adapts without duplicating code across services.

Turn Failures into Features

Circuit breakers, exponential backoff, and retry logic become first-class citizens in your error handling pipeline, making your services self-healing.

Real Developer Workflows: Before vs. After

Scenario 1: Database Connection Pool Exhaustion

Before: Service starts returning 500s. You ssh into production, grep through logs, discover connection pool warnings buried 200 lines up, then spend 30 minutes correlating the timeline.

After:

// Connection wrapper with automatic reporting
const withConnection = wrapRetryable(async <T>(fn: (conn: Connection) => Promise<T>) => {
  const conn = await pool.acquire();
  if (!conn) {
    throw new OperationalError('CONNECTION_POOL_EXHAUSTED', { 
      retryable: false,
      severity: 'critical' 
    });
  }
  return fn(conn);
});

Your dashboard immediately shows the CONNECTION_POOL_EXHAUSTED spike, circuit breaker activates to protect the database, and you get a Slack alert with the exact service and correlation ID.

Scenario 2: Cascading Microservice Failures

Before: API gateway times out, but you don't know if it's the auth service, user service, or payment service failing downstream.

After:

// Every service call automatically enriched
const user = await withTracing('user-service-call', () =>
  userService.getById(userId, { traceId: req.traceId })
);

OpenTelemetry traces show the exact service chain, Temporal workflows handle compensation logic, and intelligent error grouping separates the root cause from cascading symptoms.

Scenario 3: Silent Data Corruption

Before: Invalid data gets processed, stored, and corrupts downstream calculations. You discover it days later during data analysis.

After:

export function assertValidUser(user: unknown): asserts user is User {
  if (!isValidEmail(user.email)) {
    throw new ValidationError('INVALID_EMAIL', { 
      field: 'email', 
      value: redact(user.email) 
    });
  }
  // Fails fast at service boundary
}

Guards validate every input immediately. Structured validation errors include field-level context. Bad data never enters your system.

Implementation Guide

Step 1: Set Up Error Infrastructure

Install the cursor rules and create your error handling foundation:

// errors/base.ts - Domain-specific error classes
export abstract class AppError extends Error {
  abstract readonly code: string;
  abstract readonly status: number;
  abstract readonly retryable: boolean;
  
  constructor(message: string, public readonly context: Record<string, unknown> = {}) {
    super(message);
    this.name = this.constructor.name;
  }
}

export class ValidationError extends AppError {
  readonly code = 'VALIDATION_FAILED';
  readonly status = 400;
  readonly retryable = false;
}

Step 2: Configure Central Error Handler

// middleware/error-handler.ts
app.use((err: Error, req: Request, res: Response, next: NextFunction) => {
  const enriched = enrichError(err, {
    traceId: req.traceId,
    service: 'user-api',
    endpoint: req.path
  });
  
  logger.error(enriched, err.message);
  errorReporter.capture(enriched);
  metrics.increment('errors_total', { code: enriched.code });
  
  res.status(enriched.status).json({ 
    error: enriched.code,
    message: enriched.userMessage 
  });
});

Step 3: Add Observability Layers

// observability/tracing.ts  
export const withTracing = <T>(operationName: string, fn: () => Promise<T>) => {
  const span = tracer.startSpan(operationName);
  return fn()
    .catch(err => {
      span.recordException(err);
      span.setStatus({ code: SpanStatusCode.ERROR });
      throw err;
    })
    .finally(() => span.end());
};

Step 4: Implement Smart Retry Logic

// utils/retry.ts
export const withRetry = async <T>(
  fn: () => Promise<T>,
  options: RetryOptions = {}
): Promise<T> => {
  const { maxAttempts = 3, backoff = 'exponential' } = options;
  
  for (let attempt = 1; attempt <= maxAttempts; attempt++) {
    try {
      return await fn();
    } catch (err) {
      if (!isRetryable(err) || attempt === maxAttempts) {
        throw err;
      }
      await delay(calculateBackoff(attempt, backoff));
    }
  }
};

Results & Impact: What You'll Achieve

Week 1: Immediate Visibility

All errors automatically captured with full context
Correlation IDs connecting distributed traces
Structured logs queryable across your entire stack

Week 2: Proactive Problem Solving

Circuit breakers preventing cascade failures
Intelligent retry logic handling transient issues
Error dashboards showing real system health

Month 1: Production Confidence

70% reduction in time spent debugging production issues
Automated incident response for known error patterns
Zero silent failures—every error classified and handled

Quarter 1: Development Velocity

New services inherit proven error handling patterns
Chaos testing validates error resilience automatically
Team focuses on features instead of firefighting

The bottom line: These rules don't just handle errors better—they eliminate entire categories of production problems while giving you the observability to prevent new ones. Your backend becomes antifragile: it gets stronger under stress instead of breaking down.

Stop hunting bugs in production. Start building systems that tell you exactly what's wrong and fix themselves when possible.

Robust Backend Error Handling & Reporting Rules

Stop Debugging Production Fires: Master Backend Error Handling & Reporting

The Hidden Cost of Poor Error Handling

What These Cursor Rules Deliver

Key Benefits That Transform Your Development Workflow

Eliminate Context Switching

Reduce Mean Time to Resolution by 70%

Scale Error Handling with Your Architecture

Turn Failures into Features

Real Developer Workflows: Before vs. After

Scenario 1: Database Connection Pool Exhaustion

Scenario 2: Cascading Microservice Failures

Scenario 3: Silent Data Corruption

Implementation Guide

Step 1: Set Up Error Infrastructure

Step 2: Configure Central Error Handler

Step 3: Add Observability Layers

Step 4: Implement Smart Retry Logic

Results & Impact: What You'll Achieve

Week 1: Immediate Visibility

Week 2: Proactive Problem Solving

Month 1: Production Confidence

Quarter 1: Development Velocity

Configuration