Introduction

In many .NET solutions, background processing starts simple but grows into a distributed setup.

Typical implementations include:

• Azure WebJobs inside App Services
• Background services (IHostedService)
• Azure Functions with queue triggers
• Queue-based processing (Storage Queues, Service Bus)
• Scheduling with Hangfire or Quartz.NET

Individually, these components are well understood.
Combined, they introduce operational complexity.

Context

Common scenarios:

• Scheduled imports or exports
• File and document processing
• Sending emails or notifications
• Data synchronization between systems
• Multi-step business processes

These processes often span multiple steps and systems, and do not fit cleanly into a single request/response flow.

The problem in practice

Background processing becomes infrastructure

What starts as a simple job often evolves into:

• One or more queues
• Multiple workers
• Retry and failure handling
• Scheduling logic
• Monitoring and alerting

Even with managed Azure services, these concerns remain.

Responsibility is fragmented

Different concerns are handled in different places:

• Execution logic in workers
• State in storage or memory
• Retries in code or queue configuration
• Monitoring in logs and metrics

There is no single, consistent model.

Reliability depends on implementation

Typical risks:

• Jobs interrupted during deployments or restarts
• Partial execution without clear recovery
• Duplicate processing due to retries
• Manual intervention required for failures

These are not edge cases — they are common in production systems.

Observability is limited

Even with Application Insights:

• No clear view of end-to-end execution
• Difficult to understand current state
• Hard to correlate steps across systems

Answering simple questions becomes difficult:

• “What is running?”
• “What failed?”
• “What is stuck?”

Scaling requires coordination

Scaling involves multiple moving parts:

• Queue throughput
• Worker instances
• Resource limits

This leads to:

• Backlogs when scaling lags behind
• Over-provisioning to stay safe
• Unpredictable behavior under load

The Taskurai approach

Taskurai replaces this fragmented model with a single execution model based on:

• Tasks
• Steps
• Built-in state and execution tracking

Instead of combining queues, workers, and schedulers, you define the process itself.

What changes

No infrastructure to design

You do not need to:

• Define or manage queues
• Implement retry pipelines
• Coordinate workers
• Build orchestration logic

Execution is durable by default

• Tasks continue after restarts or deployments
• Progress is stored automatically
• Failures do not result in lost work

State is explicit

• Each step has a clear input and output
• Progress is tracked centrally
• Partial execution can resume without custom logic

Observability is built-in

• Each task and step is visible
• Execution history is available
• Failures and retries are traceable

No need to reconstruct behavior from logs.

Scaling is handled by the platform

• Workers scale based on demand
• Idle workloads scale down
• No coordination between queues and workers required

What this replaces

Taskurai replaces combinations of:

• WebJobs or background services
• Queue-based worker systems
• Scheduling frameworks
• Custom retry and orchestration logic

Not by improving each part individually, but by removing the need to combine them.

Business impact

• Less operational overhead
• Fewer failure scenarios
• Faster troubleshooting
• More predictable execution
• Clear ownership of background processes

Summary

In .NET systems, background processing often evolves into a collection of loosely connected components.

Taskurai replaces this with a single, consistent model for:

• Execution
• State
• Retries
• Observability

Without requiring custom infrastructure or orchestration code.