Workflow Orchestration Patterns

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.
• If detailed examples are required, open resources/implementation-playbook.md.

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: Use workflow.get_version() for safe changes
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 batching when appropriate

Additional Resources

Official Documentation:

• Temporal Core Concepts: docs.temporal.io/workflows
• Workflow Patterns: docs.temporal.io/evaluate/use-cases-design-patterns
• Best Practices: docs.temporal.io/develop/best-practices
• Saga Pattern: temporal.io/blog/saga-pattern-made-easy

Key Principles:

1. Workflows = orchestration, Activities = external calls
2. Determinism is non-negotiable for workflows
3. Idempotency is critical for activities
4. State preservation is automatic
5. Design for failure and recovery

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.

# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for Workflow Orchestration Patterns"

Storing Results

After completing work, store workflow/automation decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
  --type technical --project <project> \
  --tags workflow-orchestration-patterns workflow

Multi-Agent Collaboration

Share workflow state with other agents so they can trigger, monitor, or extend the automation.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
  --project <project>

Playbook Engine

Combine this skill with others using the Playbook Engine (execution/workflow_engine.py) for guided multi-step automation with progress tracking.