workflow-orchestration-patterns – skill Agent & Tự động hóa cho agent AI

Workflow Orchestration Patterns

Master workflow orchestration architecture with Temporal, covering fundamental design decisions, resilience patterns, and best practices for building reliable distributed systems.

Use this skill when

• Working on workflow orchestration patterns tasks or workflows
• Needing guidance, best practices, or checklists for workflow orchestration patterns

Do not use this skill when

• The task is unrelated to workflow orchestration patterns
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.

When to Use Workflow Orchestration

Ideal Use Cases (Source: docs.temporal.io)

• Multi-step processes spanning machines/services/databases
• Distributed transactions requiring all-or-nothing semantics
• Long-running workflows (hours to years) with automatic state persistence
• Failure recovery that must resume from last successful step
• Business processes: bookings, orders, campaigns, approvals
• Entity lifecycle management: inventory tracking, account management, cart workflows
• Infrastructure automation: CI/CD pipelines, provisioning, deployments
• Human-in-the-loop systems requiring timeouts and escalations

When NOT to Use

• Simple CRUD operations (use direct API calls)
• Pure data processing pipelines (use Airflow, batch processing)
• Stateless request/response (use standard APIs)
• Real-time streaming (use Kafka, event processors)

Critical Design Decision: Workflows vs Activities

The Fundamental Rule (Source: temporal.io/blog/workflow-engine-principles):

• Workflows = Orchestration logic and decision-making
• Activities = External interactions (APIs, databases, network calls)

Workflows (Orchestration)

Characteristics:

• Contain business logic and coordination
• MUST be deterministic (same inputs → same outputs)
• Cannot perform direct external calls
• State automatically preserved across failures
• Can run for years despite infrastructure failures

Example workflow tasks:

• Decide which steps to execute
• Handle compensation logic
• Manage timeouts and retries
• Coordinate child workflows

Activities (External Interactions)

Characteristics:

• Handle all external system interactions
• Can be non-deterministic (API calls, DB writes)
• Include built-in timeouts and retry logic
• Must be idempotent (calling N times = calling once)
• Short-lived (seconds to minutes typically)

Example activity tasks:

• Call payment gateway API
• Write to database
• Send emails or notifications
• Query external services

Design Decision Framework

Does it touch external systems? → Activity
Is it orchestration/decision logic? → Workflow

Core Workflow Patterns

1. Saga Pattern with Compensation

Purpose: Implement distributed transactions with rollback capability

Pattern (Source: temporal.io/blog/compensating-actions-part-of-a-complete-breakfast-with-sagas):

For each step:
  1. Register compensation BEFORE executing
  2. Execute the step (via activity)
  3. On failure, run all compensations in reverse order (LIFO)

Example: Payment Workflow

1. Reserve inventory (compensation: release inventory)

2. Charge payment (compensation: refund payment)

3. Fulfill order (compensation: cancel fulfillment)

Critical Requirements:

• Compensations must be idempotent
• Register compensation BEFORE executing step
• Run compensations in reverse order
• Handle partial failures gracefully

2. Entity Workflows (Actor Model)

Purpose: Long-lived workflow representing single entity instance

Pattern (Source: docs.temporal.io/evaluate/use-cases-design-patterns):

• One workflow execution = one entity (cart, account, inventory item)
• Workflow persists for entity lifetime
• Receives signals for state changes
• Supports queries for current state

Example Use Cases:

• Shopping cart (add items, checkout, expiration)
• Bank account (deposits, withdrawals, balance checks)
• Product inventory (stock updates, reservations)

Benefits:

• Encapsulates entity behavior
• Guarantees consistency per entity
• Natural event sourcing

3. Fan-Out/Fan-In (Parallel Execution)

Purpose: Execute multiple tasks in parallel, aggregate results

Pattern:

• Spawn child workflows or parallel activities
• Wait for all to complete
• Aggregate results
• Handle partial failures

Scaling Rule (Source: temporal.io/blog/workflow-engine-principles):

• Don't scale individual workflows
• For 1M tasks: spawn 1K child workflows × 1K tasks each
• Keep each workflow bounded

4. Async Callback Pattern

Purpose: Wait for external event or human approval

Pattern:

• Workflow sends request and waits for signal
• External system processes asynchronously
• Sends signal to resume workflow
• Workflow continues with response

Use Cases:

• Human approval workflows
• Webhook callbacks
• Long-running external processes

State Management and Determinism

Automatic State Preservation

How Temporal Works (Source: docs.temporal.io/workflows):

• Complete program state preserved automatically
• Event History records every command and event
• Seamless recovery from crashes
• Applications restore pre-failure state

Determinism Constraints

Workflows Execute as State Machines:

• Replay behavior must be consistent
• Same inputs → identical outputs every time

Prohibited in Workflows (Source: docs.temporal.io/workflows):

• ❌ Threading, locks, synchronization primitives
• ❌ Random number generation (random())
• ❌ Global state or static variables
• ❌ System time (datetime.now())
• ❌ Direct file I/O or network calls
• ❌ Non-deterministic libraries

Allowed in Workflows:

• ✅ workflow.now() (deterministic time)
• ✅ workflow.random() (deterministic random)
• ✅ Pure functions and calculations
• ✅ Calling activities (non-deterministic operations)

Versioning Strategies

Challenge: Changing workflow code while old executions still running

Solutions:

1. Versioning API: Bind the procedure to the actual SDK language and installed version. Python uses workflow.patched / workflow.deprecate_patch; other SDKs have different APIs. Replay existing histories before deployment; follow [official Python versioning guidance](https://docs.temporal.io/develop/python/versioning).

2. New Workflow Type: Create new workflow, route new executions to it

3. Backward Compatibility: Ensure old events replay correctly

Resilience and Error Handling

Retry Policies

Default Behavior: Temporal retries activities forever

Configure Retry:

• Initial retry interval
• Backoff coefficient (exponential backoff)
• Maximum interval (cap retry delay)
• Maximum attempts (eventually fail)

Non-Retryable Errors:

• Invalid input (validation failures)
• Business rule violations
• Permanent failures (resource not found)

Idempotency Requirements

Why Critical (Source: docs.temporal.io/activities):

• Activities may execute multiple times
• Network failures trigger retries
• Duplicate execution must be safe

Implementation Strategies:

• Idempotency keys (deduplication)
• Check-then-act with unique constraints
• Upsert operations instead of insert
• Track processed request IDs

Activity Heartbeats

Purpose: Detect stalled long-running activities

Pattern:

• Activity sends periodic heartbeat
• Includes progress information
• Timeout if no heartbeat received
• Enables progress-based retry

Best Practices

Workflow Design

1. Keep workflows focused - Single responsibility per workflow

2. Small workflows - Use child workflows for scalability

3. Clear boundaries - Workflow orchestrates, activities execute

4. Test locally - Use time-skipping test environment

Activity Design

1. Idempotent operations - Safe to retry

2. Short-lived - Seconds to minutes, not hours

3. Timeout configuration - Always set timeouts

4. Heartbeat for long tasks - Report progress

5. Error handling - Distinguish retryable vs non-retryable

Common Pitfalls

Workflow Violations:

• Using datetime.now() instead of workflow.now()
• Threading or async operations in workflow code
• Calling external APIs directly from workflow
• Non-deterministic logic in workflows

Activity Mistakes:

• Non-idempotent operations (can't handle retries)
• Missing timeouts (activities run forever)
• No error classification (retry validation errors)
• Ignoring payload limits (2MB per argument)

Operational Considerations

Monitoring:

• Workflow execution duration
• Activity failure rates
• Retry attempts and backoff
• Pending workflow counts

Scalability:

• Horizontal scaling with workers
• Task queue partitioning
• Child workflow decomposition
• Activity

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