attune/docs/workflows/workflow-orchestration.md

Workflow Orchestration Design

Overview

This document describes the architecture and implementation plan for workflow orchestration in Attune. Workflows enable the composition of multiple actions into complex, conditional execution graphs with variable passing, iteration, and error handling.

Design Philosophy

Workflows in Attune follow these core principles:

1. Workflows are Actions: A workflow is itself an action that can be invoked by rules, other workflows, or directly via API
2. YAML-First: Workflow definitions are declarative YAML files stored in packs
3. Event-Driven: Workflows execute asynchronously via the same message queue infrastructure as regular actions
4. Composable: Workflows can invoke other workflows recursively
5. Observable: Each task in a workflow creates an execution record with full traceability
6. Recoverable: Failed workflows can be resumed or retried from specific tasks

Architecture Components

1. Workflow Definition Format

Workflows are defined as YAML files in pack directories alongside regular actions:

# packs/my_pack/workflows/deploy_application.yaml
ref: my_pack.deploy_application
label: "Deploy Application Workflow"
description: "Deploys application with health checks and rollback"
version: "1.0.0"

# Input parameters (like action parameters)
parameters:
  app_name:
    type: string
    required: true
    description: "Name of the application to deploy"
  version:
    type: string
    required: true
  environment:
    type: string
    enum: [dev, staging, production]
    default: dev

# Output schema (what this workflow produces)
output:
  type: object
  properties:
    deployment_id:
      type: string
    status:
      type: string
    deployed_version:
      type: string

# Workflow variables (scoped to this workflow execution)
vars:
  deployment_id: null
  health_check_url: null
  rollback_required: false

# Task graph definition
tasks:
  # Task 1: Create deployment record
  - name: create_deployment
    action: my_pack.create_deployment_record
    input:
      app_name: "{{ parameters.app_name }}"
      version: "{{ parameters.version }}"
      environment: "{{ parameters.environment }}"
    publish:
      - deployment_id: "{{ task.create_deployment.result.id }}"
      - health_check_url: "{{ task.create_deployment.result.health_url }}"
    on_success: build_image
    on_failure: notify_failure

  # Task 2: Build container image
  - name: build_image
    action: docker.build_and_push
    input:
      app_name: "{{ parameters.app_name }}"
      version: "{{ parameters.version }}"
      registry: "{{ pack.config.docker_registry }}"
    on_success: deploy_containers
    on_failure: cleanup_deployment

  # Task 3: Deploy containers
  - name: deploy_containers
    action: kubernetes.apply_deployment
    input:
      app_name: "{{ parameters.app_name }}"
      image: "{{ task.build_image.result.image_uri }}"
      replicas: 3
    on_success: wait_for_ready
    on_failure: rollback_deployment

  # Task 4: Wait for pods to be ready
  - name: wait_for_ready
    action: kubernetes.wait_for_ready
    input:
      deployment: "{{ parameters.app_name }}"
      timeout: 300
    retry:
      count: 3
      delay: 10
    on_success: health_check
    on_failure: rollback_deployment

  # Task 5: Perform health check
  - name: health_check
    action: http.get
    input:
      url: "{{ vars.health_check_url }}"
      expected_status: 200
    on_success: update_deployment_status
    on_failure: rollback_deployment

  # Task 6: Update deployment as successful
  - name: update_deployment_status
    action: my_pack.update_deployment_status
    input:
      deployment_id: "{{ vars.deployment_id }}"
      status: "success"
    on_success: notify_success

  # Task 7: Rollback on failure
  - name: rollback_deployment
    action: kubernetes.rollback_deployment
    input:
      app_name: "{{ parameters.app_name }}"
    publish:
      - rollback_required: true
    on_complete: cleanup_deployment

  # Task 8: Cleanup resources
  - name: cleanup_deployment
    action: my_pack.cleanup_resources
    input:
      deployment_id: "{{ vars.deployment_id }}"
      rollback: "{{ vars.rollback_required }}"
    on_complete: notify_failure

  # Task 9: Success notification
  - name: notify_success
    action: slack.post_message
    input:
      channel: "#deployments"
      message: "✅ Deployed {{ parameters.app_name }} v{{ parameters.version }} to {{ parameters.environment }}"

  # Task 10: Failure notification
  - name: notify_failure
    action: slack.post_message
    input:
      channel: "#deployments"
      message: "❌ Failed to deploy {{ parameters.app_name }} v{{ parameters.version }}"

# Workflow output mapping
output_map:
  deployment_id: "{{ vars.deployment_id }}"
  status: "{{ task.update_deployment_status.result.status }}"
  deployed_version: "{{ parameters.version }}"

2. Advanced Workflow Features

2.1 Parallel Execution

Execute multiple tasks concurrently:

tasks:
  - name: parallel_checks
    type: parallel
    tasks:
      - name: check_database
        action: postgres.health_check
      - name: check_redis
        action: redis.ping
      - name: check_rabbitmq
        action: rabbitmq.cluster_status
    # All parallel tasks must complete before proceeding
    on_success: deploy_app
    on_failure: abort_deployment

2.2 Iteration (with-items)

Iterate over lists with optional batching:

tasks:
  # Without batch_size: process items individually (one execution per item)
  - name: deploy_to_regions
    action: cloud.deploy_instance
    with_items: "{{ parameters.regions }}"
    input:
      # item is the individual region value
      region: "{{ item }}"
      instance_type: "{{ parameters.instance_type }}"
    # Creates one execution per item
    on_success: verify_deployments

  # With batch_size: items split into batches (batch processing)
  - name: process_large_dataset
    action: data.transform
    with_items: "{{ vars.records }}"
    batch_size: 100  # Process 100 items at a time
    concurrency: 5   # Process up to 5 batches concurrently
    input:
      # item is an array containing up to 100 records
      # Last batch may contain fewer items
      records: "{{ item }}"
    publish:
      - total_processed: "{{ task.process_large_dataset.total_count }}"

Batch Processing Behavior:

• Without batch_size: Each item is processed individually (one execution per item)
  • item variable contains a single value
  • index variable contains the item index (0-based)
• With batch_size: Items are split into batches and processed as arrays
  • item variable contains an array of items (batch)
  • index variable contains the batch index (0-based)
  • Last batch may contain fewer items than batch_size
  • Use batch_size for efficient bulk processing when actions support arrays

2.3 Conditional Execution

Execute tasks based on conditions:

tasks:
  - name: check_environment
    action: core.noop
    when: "{{ parameters.environment == 'production' }}"
    on_complete: require_approval

  - name: require_approval
    action: core.inquiry
    input:
      prompt: "Approve production deployment of {{ parameters.app_name }}?"
      schema:
        type: object
        properties:
          approved:
            type: boolean
    on_success: deploy_app
    # Only proceed if approved
    decision:
      - when: "{{ task.require_approval.result.approved == true }}"
        next: deploy_app
      - default: cancel_deployment

2.4 Error Handling and Retry

tasks:
  - name: flaky_api_call
    action: http.post
    input:
      url: "{{ vars.api_endpoint }}"
    retry:
      count: 5
      delay: 10  # seconds
      backoff: exponential  # linear, exponential, constant
      max_delay: 60
      on_error: "{{ task.flaky_api_call.error.type == 'timeout' }}"
    on_success: process_response
    on_failure: log_error

2.5 Timeout and Cancellation

tasks:
  - name: long_running_task
    action: ml.train_model
    input:
      dataset: "{{ vars.dataset_path }}"
    timeout: 3600  # 1 hour
    on_timeout: cleanup_and_notify

2.6 Subworkflows

Invoke other workflows:

tasks:
  - name: provision_infrastructure
    action: infrastructure.provision_stack  # This is also a workflow
    input:
      stack_name: "{{ parameters.app_name }}-{{ parameters.environment }}"
      region: "{{ parameters.region }}"
    on_success: deploy_application

Data Model Changes

New Tables

1. workflow_definition Table

CREATE TABLE attune.workflow_definition (
    id BIGSERIAL PRIMARY KEY,
    ref VARCHAR(255) NOT NULL UNIQUE,
    pack BIGINT NOT NULL REFERENCES attune.pack(id) ON DELETE CASCADE,
    pack_ref VARCHAR(255) NOT NULL,
    label VARCHAR(255) NOT NULL,
    description TEXT,
    version VARCHAR(50) NOT NULL,
    
    -- Workflow specification (parsed YAML)
    param_schema JSONB,
    out_schema JSONB,
    definition JSONB NOT NULL,  -- Full workflow definition
    
    -- Metadata
    tags TEXT[] DEFAULT '{}',
    enabled BOOLEAN DEFAULT true,
    
    created TIMESTAMPTZ DEFAULT NOW(),
    updated TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX idx_workflow_pack ON attune.workflow_definition(pack);
CREATE INDEX idx_workflow_enabled ON attune.workflow_definition(enabled);

2. workflow_execution Table

Tracks the state of a workflow execution:

CREATE TABLE attune.workflow_execution (
    id BIGSERIAL PRIMARY KEY,
    execution BIGINT NOT NULL REFERENCES attune.execution(id) ON DELETE CASCADE,
    workflow_def BIGINT NOT NULL REFERENCES attune.workflow_definition(id),
    
    -- Workflow state
    current_tasks TEXT[] DEFAULT '{}',  -- Currently executing task names
    completed_tasks TEXT[] DEFAULT '{}',
    failed_tasks TEXT[] DEFAULT '{}',
    
    -- Variable context (scoped to this workflow)
    variables JSONB DEFAULT '{}',
    
    -- Graph traversal state
    task_graph JSONB NOT NULL,  -- Adjacency list representation
    
    -- Status tracking
    status attune.execution_status_enum NOT NULL DEFAULT 'requested',
    error_message TEXT,
    
    created TIMESTAMPTZ DEFAULT NOW(),
    updated TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX idx_workflow_exec_execution ON attune.workflow_execution(execution);
CREATE INDEX idx_workflow_exec_status ON attune.workflow_execution(status);

3. workflow_task_execution Table

Tracks individual task executions within a workflow:

CREATE TABLE attune.workflow_task_execution (
    id BIGSERIAL PRIMARY KEY,
    workflow_execution BIGINT NOT NULL REFERENCES attune.workflow_execution(id) ON DELETE CASCADE,
    execution BIGINT NOT NULL REFERENCES attune.execution(id) ON DELETE CASCADE,
    
    task_name VARCHAR(255) NOT NULL,
    task_index INTEGER,  -- For with-items iterations
    
    -- Status
    status attune.execution_status_enum NOT NULL DEFAULT 'requested',
    started_at TIMESTAMPTZ,
    completed_at TIMESTAMPTZ,
    
    -- Results
    result JSONB,
    error JSONB,
    
    -- Retry tracking
    retry_count INTEGER DEFAULT 0,
    max_retries INTEGER DEFAULT 0,
    
    created TIMESTAMPTZ DEFAULT NOW(),
    updated TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX idx_wf_task_exec_workflow ON attune.workflow_task_execution(workflow_execution);
CREATE INDEX idx_wf_task_exec_execution ON attune.workflow_task_execution(execution);
CREATE INDEX idx_wf_task_exec_status ON attune.workflow_task_execution(status);

Modified Tables

Update action Table

Add a flag to distinguish workflow actions:

ALTER TABLE attune.action ADD COLUMN is_workflow BOOLEAN DEFAULT false;
ALTER TABLE attune.action ADD COLUMN workflow_def BIGINT REFERENCES attune.workflow_definition(id);

Variable Scoping and Templating

Template Engine

Use a Jinja2-like template engine (recommend tera crate in Rust) for variable interpolation.

Variable Scopes

Variables are accessible from multiple scopes in order of precedence:

1. task.* - Results from completed tasks

   • {{ task.task_name.result.field }}
   • {{ task.task_name.status }}
   • {{ task.task_name.error }}

2. vars.* - Workflow-scoped variables

   • {{ vars.deployment_id }}
   • {{ vars.custom_variable }}

3. parameters.* - Input parameters to the workflow

   • {{ parameters.app_name }}
   • {{ parameters.environment }}

4. pack.config.* - Pack configuration

   • {{ pack.config.api_key }}
   • {{ pack.config.base_url }}

5. system.* - System-level variables

   • {{ system.execution_id }}
   • {{ system.timestamp }}
   • {{ system.identity.login }}

6. kv.* - Key-value datastore

   • {{ kv.get('global.feature_flags.new_ui') }}

Special Variables for Iteration

When using with_items:

• {{ item }} - Current item or batch
  • Without batch_size: Individual item value
  • With batch_size: Array of items (batch)
• {{ index }} - Zero-based index
  • Without batch_size: Item index
  • With batch_size: Batch index

Example usage:

# Without batch_size - individual items
- name: process_one_by_one
  with_items: "{{ parameters.files }}"
  input:
    file: "{{ item }}"        # Single filename
    position: "{{ index }}"   # Item position

# With batch_size - arrays
- name: process_in_batches
  with_items: "{{ parameters.files }}"
  batch_size: 10
  input:
    files: "{{ item }}"       # Array of up to 10 filenames
    batch_num: "{{ index }}"  # Batch number

Template Helper Functions

# String manipulation
message: "{{ parameters.app_name | upper }}"
path: "{{ vars.base_path | trim | append('/logs') }}"

# JSON manipulation
config: "{{ vars.raw_config | from_json }}"
payload: "{{ vars.data | to_json }}"

# List operations
regions: "{{ parameters.all_regions | filter(enabled=true) }}"
first_region: "{{ parameters.regions | first }}"
count: "{{ vars.results | length }}"

# Batching helper
batches: "{{ vars.large_list | batch(size=100) }}"

# Conditional helpers
status: "{{ task.deploy.status | default('unknown') }}"
url: "{{ vars.host | default('localhost') | prepend('https://') }}"

# Key-value store access
flag: "{{ kv.get('feature.flags.enabled', default=false) }}"
secret: "{{ kv.get_secret('api.credentials.token') }}"

Workflow Execution Engine

Architecture

The workflow execution engine is a new component within the Executor Service:

┌────────────────────────────────────────────────┐
│           Executor Service                      │
├────────────────────────────────────────────────┤
│                                                 │
│  ┌──────────────────┐  ┌───────────────────┐  │
│  │ Enforcement      │  │ Execution         │  │
│  │ Processor        │  │ Scheduler         │  │
│  └──────────────────┘  └───────────────────┘  │
│                                                 │
│  ┌──────────────────────────────────────────┐ │
│  │     Workflow Engine (NEW)                │ │
│  ├──────────────────────────────────────────┤ │
│  │ - Workflow Parser                        │ │
│  │ - Graph Executor                         │ │
│  │ - Variable Context Manager               │ │
│  │ - Task Scheduler                         │ │
│  │ - State Machine                          │ │
│  └──────────────────────────────────────────┘ │
│                                                 │
└────────────────────────────────────────────────┘

Workflow Lifecycle

1. Rule triggers workflow action
2. Executor recognizes workflow action (is_workflow = true)
3. Workflow Engine loads workflow definition
4. Create workflow_execution record
5. Initialize variable context with parameters
6. Build task dependency graph
7. Schedule initial tasks (entry points with no dependencies)
8. For each task:
   a. Template task inputs using current variable context
   b. Create child execution for the action
   c. Publish execution.scheduled message
   d. Create workflow_task_execution record
9. Worker executes task and publishes result
10. Workflow Engine receives execution.completed
    a. Update workflow_task_execution
    b. Publish variables to workflow context
    c. Evaluate next tasks based on transitions
    d. Schedule next tasks or complete workflow
11. Repeat until all tasks complete or workflow fails
12. Update parent workflow execution status
13. Publish workflow.completed event

State Machine

                   ┌──────────────┐
                   │  Requested   │
                   └──────┬───────┘
                          │
                   ┌──────▼───────┐
                   │  Scheduling  │
                   └──────┬───────┘
                          │
                   ┌──────▼───────┐
                   │   Running    │◄────┐
                   └──┬───┬───┬───┘     │
                      │   │   │         │
         ┌────────────┘   │   └─────┐   │
         │                │         │   │
    ┌────▼────┐    ┌─────▼─────┐   │   │
    │ Paused  │    │  Waiting  │───┘   │
    └────┬────┘    │ (for task)│       │
         │         └───────────┘       │
         └─────────────────────────────┘
                      
         ┌────────────┬────────────┐
         │            │            │
    ┌────▼────┐  ┌───▼─────┐  ┌──▼────────┐
    │Completed│  │ Failed  │  │ Cancelled │
    └─────────┘  └─────────┘  └───────────┘

Core Components (Rust Implementation)

1. Workflow Definition Parser

// crates/executor/src/workflow/parser.rs

pub struct WorkflowDefinition {
    pub ref_: String,
    pub label: String,
    pub parameters: JsonSchema,
    pub output: JsonSchema,
    pub vars: HashMap<String, JsonValue>,
    pub tasks: Vec<TaskDefinition>,
    pub output_map: HashMap<String, String>,
}

pub struct TaskDefinition {
    pub name: String,
    pub task_type: TaskType,
    pub action: Option<String>,
    pub input: HashMap<String, String>,  // Template strings
    pub publish: Vec<PublishMapping>,
    pub transitions: TaskTransitions,
    pub retry: Option<RetryPolicy>,
    pub timeout: Option<u64>,
    pub when: Option<String>,  // Condition template
    pub with_items: Option<String>,  // List template
    pub batch_size: Option<usize>,
}

pub enum TaskType {
    Action,
    Parallel,
    Workflow,  // Subworkflow
}

pub struct TaskTransitions {
    pub on_success: Option<String>,
    pub on_failure: Option<String>,
    pub on_complete: Option<String>,
    pub on_timeout: Option<String>,
    pub decision: Vec<DecisionBranch>,
}

pub struct DecisionBranch {
    pub when: Option<String>,  // Condition template
    pub next: String,
    pub is_default: bool,
}

2. Variable Context Manager

// crates/executor/src/workflow/context.rs

pub struct WorkflowContext {
    pub execution_id: i64,
    pub parameters: JsonValue,
    pub vars: HashMap<String, JsonValue>,
    pub task_results: HashMap<String, TaskResult>,
    pub pack_config: JsonValue,
    pub system: SystemContext,
}

impl WorkflowContext {
    pub fn render_template(&self, template: &str) -> Result<String> {
        // Use tera template engine
        let mut tera = Tera::default();
        let context = self.to_tera_context();
        tera.render_str(template, &context)
    }
    
    pub fn publish_variables(&mut self, mappings: &[PublishMapping]) -> Result<()> {
        for mapping in mappings {
            let value = self.render_template(&mapping.template)?;
            self.vars.insert(mapping.var_name.clone(), value);
        }
        Ok(())
    }
}

3. Graph Executor

// crates/executor/src/workflow/graph.rs

pub struct TaskGraph {
    nodes: HashMap<String, TaskNode>,
    edges: HashMap<String, Vec<Edge>>,
}

pub struct TaskNode {
    pub name: String,
    pub definition: TaskDefinition,
    pub status: TaskStatus,
}

pub struct Edge {
    pub from: String,
    pub to: String,
    pub condition: EdgeCondition,
}

pub enum EdgeCondition {
    OnSuccess,
    OnFailure,
    OnComplete,
    OnTimeout,
    Decision(String),  // Template condition
}

impl TaskGraph {
    pub fn find_next_tasks(&self, completed_task: &str, result: &TaskResult) -> Vec<String> {
        // Traverse graph based on task result and transitions
    }
    
    pub fn get_ready_tasks(&self) -> Vec<&TaskNode> {
        // Find tasks with all dependencies satisfied
    }
}

4. Workflow Executor

// crates/executor/src/workflow/executor.rs

pub struct WorkflowExecutor {
    pool: PgPool,
    publisher: MessagePublisher,
    template_engine: Tera,
}

impl WorkflowExecutor {
    pub async fn execute_workflow(
        &self,
        execution_id: i64,
        workflow_def: WorkflowDefinition,
        parameters: JsonValue,
    ) -> Result<()> {
        // 1. Create workflow_execution record
        let wf_exec = self.create_workflow_execution(execution_id, &workflow_def).await?;
        
        // 2. Initialize variable context
        let mut context = WorkflowContext::new(execution_id, parameters, &workflow_def);
        
        // 3. Build task graph
        let graph = TaskGraph::from_definition(&workflow_def)?;
        
        // 4. Schedule initial tasks
        let initial_tasks = graph.get_ready_tasks();
        for task in initial_tasks {
            self.schedule_task(&wf_exec, task, &context).await?;
        }
        
        Ok(())
    }
    
    pub async fn handle_task_completion(
        &self,
        wf_execution_id: i64,
        task_name: String,
        result: TaskResult,
    ) -> Result<()> {
        // 1. Load workflow execution and context
        let wf_exec = self.load_workflow_execution(wf_execution_id).await?;
        let mut context = self.load_context(&wf_exec).await?;
        
        // 2. Update task result in context
        context.task_results.insert(task_name.clone(), result.clone());
        
        // 3. Publish variables from task
        let task_def = wf_exec.definition.get_task(&task_name)?;
        context.publish_variables(&task_def.publish)?;
        
        // 4. Save updated context
        self.save_context(wf_execution_id, &context).await?;
        
        // 5. Determine next tasks
        let next_tasks = wf_exec.graph.find_next_tasks(&task_name, &result);
        
        // 6. Schedule next tasks
        for next_task in next_tasks {
            let task_def = wf_exec.definition.get_task(&next_task)?;
            
            // Evaluate condition if present
            if let Some(when) = &task_def.when {
                let should_run = context.evaluate_condition(when)?;
                if !should_run {
                    continue;
                }
            }
            
            self.schedule_task(&wf_exec, task_def, &context).await?;
        }
        
        // 7. Check if workflow is complete
        if self.is_workflow_complete(&wf_exec).await? {
            self.complete_workflow(wf_execution_id, &context).await?;
        }
        
        Ok(())
    }
    
    async fn schedule_task(
        &self,
        wf_exec: &WorkflowExecution,
        task: &TaskDefinition,
        context: &WorkflowContext,
    ) -> Result<()> {
        // Handle with_items (iteration)
        let items = if let Some(with_items_template) = &task.with_items {
            let items_json = context.render_template(with_items_template)?;
            serde_json::from_str::<Vec<JsonValue>>(&items_json)?
        } else {
            vec![JsonValue::Null]  // Single execution
        };
        
        // Batch items if batch_size specified
        let batches = if let Some(batch_size) = task.batch_size {
            items.chunks(batch_size).collect::<Vec<_>>()
        } else {
            items.iter().map(|i| vec![i]).collect::<Vec<_>>()
        };
        
        for (batch_idx, batch) in batches.iter().enumerate() {
            for (item_idx, item) in batch.iter().enumerate() {
                // Create item-specific context
                let mut item_context = context.clone();
                item_context.add_item_vars(item, item_idx, batch, batch_idx);
                
                // Render task inputs
                let rendered_input = self.render_task_input(task, &item_context)?;
                
                // Create child execution
                let execution = self.create_task_execution(
                    wf_exec.id,
                    &task.action.unwrap(),
                    rendered_input,
                    wf_exec.execution,
                ).await?;
                
                // Create workflow_task_execution record
                self.create_workflow_task_execution(
                    wf_exec.id,
                    execution.id,
                    &task.name,
                    item_idx,
                ).await?;
                
                // Publish execution.scheduled message
                self.publisher.publish_execution_scheduled(execution.id).await?;
            }
        }
        
        Ok(())
    }
}

Message Flow

New Message Types

// Workflow-specific messages
pub enum WorkflowMessage {
    WorkflowStarted { execution_id: i64, workflow_id: i64 },
    WorkflowCompleted { execution_id: i64, result: JsonValue },
    WorkflowFailed { execution_id: i64, error: String },
    TaskScheduled { workflow_execution_id: i64, task_name: String },
    TaskCompleted { workflow_execution_id: i64, task_name: String, result: TaskResult },
    TaskFailed { workflow_execution_id: i64, task_name: String, error: String },
}

API Endpoints

Workflow Management

POST   /api/v1/packs/{pack_ref}/workflows          - Create workflow
GET    /api/v1/packs/{pack_ref}/workflows          - List workflows
GET    /api/v1/workflows/{workflow_ref}            - Get workflow
PUT    /api/v1/workflows/{workflow_ref}            - Update workflow
DELETE /api/v1/workflows/{workflow_ref}            - Delete workflow

POST   /api/v1/workflows/{workflow_ref}/execute    - Execute workflow directly
GET    /api/v1/workflows/{workflow_ref}/executions - List executions

Workflow Execution Monitoring

GET    /api/v1/workflow-executions/{id}            - Get workflow execution
GET    /api/v1/workflow-executions/{id}/tasks      - List task executions
GET    /api/v1/workflow-executions/{id}/graph      - Get execution graph
POST   /api/v1/workflow-executions/{id}/pause      - Pause workflow
POST   /api/v1/workflow-executions/{id}/resume     - Resume workflow
POST   /api/v1/workflow-executions/{id}/cancel     - Cancel workflow
POST   /api/v1/workflow-executions/{id}/retry      - Retry failed workflow

Pack Structure with Workflows

packs/
└── my_pack/
    ├── pack.yaml               # Pack metadata
    ├── config.yaml             # Pack configuration schema
    ├── actions/
    │   ├── action1.py
    │   ├── action2.py
    │   └── action.yaml         # Action metadata
    ├── sensors/
    │   ├── sensor1.py
    │   └── sensor.yaml
    ├── workflows/              # NEW
    │   ├── deploy.yaml
    │   ├── backup.yaml
    │   └── migrate.yaml
    ├── rules/
    │   └── on_push.yaml
    └── tests/
        └── test_workflows.yaml

Workflow Pack Registration

When a pack is registered, the system:

1. Scans workflows/ directory for .yaml files
2. Parses and validates each workflow definition
3. Creates workflow_definition record
4. Creates synthetic action record with is_workflow=true
5. Makes workflow invokable like any other action

Implementation Plan

Phase 1: Foundation (Week 1-2)

1. Database Schema

   • Create migration for workflow tables
   • Add workflow columns to action table
   • Create indexes

2. Data Models

   • Add workflow models to common/src/models.rs
   • Create workflow definition parser
   • Create repositories

3. Template Engine

   • Integrate tera crate
   • Implement variable context
   • Create helper functions

Phase 2: Core Engine (Week 3-4)

4. Graph Engine

   • Implement task graph builder
   • Implement graph traversal logic
   • Handle conditional branching

5. Workflow Executor

   • Implement workflow execution logic
   • Handle task scheduling
   • Implement state management

6. Message Integration

   • Add workflow message handlers
   • Integrate with executor service
   • Handle task completion events

Phase 3: Advanced Features (Week 5-6)

7. Iteration Support

   • Implement with_items logic
   • Add batching support
   • Handle concurrent iterations

8. Parallel Execution

   • Implement parallel task type
   • Handle synchronization
   • Aggregate results

9. Error Handling

   • Implement retry logic
   • Handle timeouts
   • Implement failure paths

Phase 4: API & Tools (Week 7-8)

10. API Endpoints

    • Workflow CRUD operations
    • Execution monitoring
    • Control operations (pause/resume/cancel)

11. CLI Support

    • Workflow validation command
    • Workflow execution command
    • Workflow visualization

12. Testing & Documentation

    • Unit tests for all components
    • Integration tests
    • Example workflows
    • User documentation

Testing Strategy

Unit Tests

• Template rendering with all scope types
• Graph traversal algorithms
• Condition evaluation
• Variable publishing
• Task scheduling logic

Integration Tests

• End-to-end workflow execution
• Parallel task execution
• Error handling and retry
• Workflow cancellation
• Nested workflow execution

Example Test Workflows

# tests/workflows/simple_sequence.yaml
ref: test.simple_sequence
label: "Simple Sequential Workflow Test"
tasks:
  - name: task1
    action: core.echo
    input:
      message: "Task 1"
    on_success: task2
  - name: task2
    action: core.echo
    input:
      message: "Task 2"

Performance Considerations

1. Graph Optimization: Cache parsed workflow graphs
2. Template Caching: Cache compiled templates per workflow
3. Parallel Scheduling: Schedule independent tasks concurrently
4. Database Batching: Batch task creation/updates
5. Context Serialization: Use efficient JSON serialization

Security Considerations

1. Template Injection: Sanitize template inputs
2. Variable Scoping: Strict scope isolation between workflows
3. Secret Access: Only allow kv.get_secret() for authorized users
4. Resource Limits: Enforce max task count, max depth, max iterations
5. Audit Trail: Log all workflow decisions and transitions

Monitoring & Observability

Metrics

• Workflow execution duration
• Task execution duration
• Workflow success/failure rate
• Task retry count
• Queue depth for workflow tasks

Logging

INFO  Workflow execution started: id=123, workflow=deploy_app
INFO  Task scheduled: workflow=123, task=build_image
INFO  Task completed: workflow=123, task=build_image, duration=45s
INFO  Publishing variables: deployment_id=456
INFO  Scheduling next tasks: [deploy_containers, health_check]
INFO  Workflow execution completed: id=123, duration=2m30s

Tracing

• Link all tasks to parent workflow execution
• Propagate trace_id through entire workflow
• Enable distributed tracing visualization

Future Enhancements

1. Workflow Versioning: Support multiple versions of same workflow
2. Workflow Templates: Reusable workflow patterns
3. Dynamic Workflows: Generate workflow graph at runtime
4. Workflow Marketplace: Share workflows across organizations
5. Workflow Testing Framework: Built-in testing tools
6. Workflow Debugger: Step-through execution debugging
7. Workflow Visualization: Real-time execution graph UI
8. Workflow Analytics: Performance analysis and optimization suggestions

References

• StackStorm Orquesta
• Argo Workflows
• AWS Step Functions
• Temporal Workflows

Appendix: Complete Workflow Example

See docs/examples/complete-workflow.yaml for a comprehensive example demonstrating all workflow features.