🗺️ Workflow Execution Implementation Roadmap

For The AI Automator Odoo Module

Created: October 1, 2025
Purpose: Detailed implementation plan for n8n-style workflow execution
Status: Ready for Implementation

📋 Table of Contents

Overview
Architecture Summary
Implementation Phases
Database Schema
Python Backend
JavaScript Frontend
Testing Strategy
Deployment Checklist

Overview

🎯 Goal

Implement n8n-compatible workflow execution system within The AI Automator Odoo module, enabling:
- Manual and automatic workflow execution
- Multiple trigger types (manual, webhook, cron, etc.)
- Node-by-node execution with data flow
- Execution history and logging
- Error handling and retry mechanisms
- Workflow activation/deactivation

🏗️ Architecture Approach

Following The AI Automator's Above/Below the Line architecture:

ABOVE THE LINE (n8n Strategy)
├─ n8n execution patterns
├─ n8n trigger concepts
└─ n8n data flow structure

THE BRIDGE (Translation Layer)
├─ workflow_executor.py (Python execution engine)
├─ execution_controller.py (Odoo HTTP controllers)
└─ trigger_manager.py (Trigger management)

BELOW THE LINE (Odoo/PostgreSQL)
├─ executions model
├─ execution_logs model
├─ canvas model (enhanced)
└─ nodes model (enhanced)

Architecture Summary

Core Components

1. Execution Models (PostgreSQL)

executions: Workflow execution records
execution_logs: Per-node execution logs
canvas (enhanced): Activation, settings
nodes (enhanced): Execution configs

2. Execution Engine (Python)

WorkflowExecutor: Main orchestrator
NodeRunner: Individual node execution
DataFlowManager: Data passing between nodes

3. Trigger System (Python)

TriggerManager: Register/unregister triggers
WebhookTrigger: Webhook endpoints
CronTrigger: Scheduled execution
ManualTrigger: UI-triggered execution

4. API Layer (Odoo Controllers)

Execute workflow
Get executions
Get execution details
Retry execution
Activate/deactivate workflow

5. Frontend (JavaScript/Owl.js)

Execute workflow button
Executions list view
Execution details view
Real-time execution status

Implementation Phases

📅 Phase 1: Database Foundation (Week 1)

Goal: Create database models for execution tracking

Tasks:
1. ✅ Create executions model
2. ✅ Create execution_logs model
3. ✅ Enhance canvas model with activation fields
4. ✅ Enhance nodes model with execution settings
5. ✅ Create database views for executions
6. ✅ Add security rules (ir.model.access.csv)

Deliverables:
- models/executions.py
- models/execution_logs.py
- models/canvas.py
- models/nodes.py
- security/ir.model.access.csv
- views/executions_views.xml

Testing:

# Verify models created
executions = env['executions'].search([])
execution_logs = env['execution_logs'].search([])
canvas = env['canvas'].search([], limit=1)
assert hasattr(canvas, 'active_workflow')
assert hasattr(canvas, 'execution_ids')

📅 Phase 2: Core Execution Engine (Week 2)

Goal: Implement basic workflow execution

Tasks:
1. ✅ Create WorkflowExecutor class
2. ✅ Implement node-by-node execution loop
3. ✅ Implement data flow between nodes
4. ✅ Add execution logging
5. ✅ Handle basic node types (manual, code, set, filter)
6. ✅ Create execution record on start/finish

Deliverables:
- lib/workflow_executor.py
- lib/node_runner.py
- lib/data_flow_manager.py

Testing:

# Test basic execution
canvas = env['canvas'].create({'name': 'Test Workflow'})
node1 = env['nodes'].create({'name': 'Start', 'type': 'manual', 'canvas_id': canvas.id})
node2 = env['nodes'].create({'name': 'End', 'type': 'code', 'canvas_id': canvas.id})

from ..lib.workflow_executor import WorkflowExecutor
executor = WorkflowExecutor(env, canvas.id, 'manual')
execution_id = executor.execute()

execution = env['executions'].search([('execution_id', '=', execution_id)])
assert execution.status == 'success'
assert execution.finished == True

📅 Phase 3: HTTP API & Controllers (Week 3)

Goal: Expose execution functionality via HTTP API

Tasks:
1. ✅ Create ExecutionController class
2. ✅ Implement /canvas/<id>/execute route
3. ✅ Implement /canvas/<id>/executions route
4. ✅ Implement /executions/<id> route
5. ✅ Implement /executions/<id>/retry route
6. ✅ Implement /canvas/<id>/activate route
7. ✅ Implement /canvas/<id>/deactivate route
8. ✅ Add CORS support if needed

Deliverables:
- controllers/execution_controller.py

Testing:

# Test API endpoints
response = requests.post('http://localhost:8069/canvas/1/execute', json={'mode': 'manual'})
assert response.json()['success'] == True
execution_id = response.json()['executionId']

response = requests.get(f'http://localhost:8069/executions/{execution_id}')
assert response.json()['data']['status'] == 'success'

📅 Phase 4: Frontend Integration (Week 3)

Goal: Create UI for workflow execution

Tasks:
1. ✅ Add "Execute Workflow" button to canvas
2. ✅ Create executions list view
3. ✅ Create execution detail view
4. ✅ Show execution logs per node
5. ✅ Add retry button
6. ✅ Add activate/deactivate toggle
7. ✅ Real-time execution status (polling or websocket)

Deliverables:
- static/src/js/workflow_executor.js
- static/src/components/execute_button.js
- static/src/components/executions_list.js
- static/src/components/execution_detail.js
- static/src/xml/execution_templates.xml

Testing:
- Click "Execute Workflow" button
- Verify execution appears in list
- Click execution to view details
- Verify logs show per-node execution

📅 Phase 5: Trigger System - Manual & Webhook (Week 4)

Goal: Implement manual and webhook triggers

Tasks:
1. ✅ Create TriggerManager class
2. ✅ Implement ManualTrigger handler
3. ✅ Implement WebhookTrigger handler
4. ✅ Register webhook routes on activation
5. ✅ Unregister webhook routes on deactivation
6. ✅ Handle webhook authentication
7. ✅ Test webhook execution flow

Deliverables:
- lib/trigger_manager.py
- lib/triggers/manual_trigger.py
- lib/triggers/webhook_trigger.py
- controllers/webhook_controller.py

Testing:

# Test webhook trigger
canvas.action_activate()  # Activate workflow

# Call webhook
response = requests.post('http://localhost:8069/webhook/test-webhook', json={'data': 'test'})
assert response.status_code == 200

# Verify execution created
executions = env['executions'].search([('canvas_id', '=', canvas.id), ('mode', '=', 'production')])
assert len(executions) > 0

📅 Phase 6: Trigger System - Cron/Schedule (Week 5)

Goal: Implement scheduled/cron triggers

Tasks:
1. ✅ Create CronTrigger handler
2. ✅ Parse cron expressions
3. ✅ Register cron jobs on activation
4. ✅ Unregister cron jobs on deactivation
5. ✅ Handle timezone settings
6. ✅ Test cron execution

Deliverables:
- lib/triggers/cron_trigger.py
- data/cron_jobs.xml

Testing:

# Create workflow with schedule trigger
schedule_node = env['nodes'].create({
    'name': 'Schedule',
    'type': 'scheduleTrigger',
    'canvas_id': canvas.id,
    'parameters': json.dumps({
        'rule': {'interval': [{'field': 'minutes', 'minutesInterval': 5}]}
    })
})

canvas.action_activate()

# Wait 5 minutes and verify execution created
# (or trigger cron manually for testing)

📅 Phase 7: Error Handling & Recovery (Week 6)

Goal: Implement error handling and retry mechanisms

Tasks:
1. ✅ Implement retry on fail logic
2. ✅ Implement continue on fail logic
3. ✅ Add error output connections
4. ✅ Implement error workflows
5. ✅ Add execution retry from UI
6. ✅ Add exponential backoff
7. ✅ Test error scenarios

Deliverables:
- Enhancements to WorkflowExecutor
- Enhancements to NodeRunner
- Error workflow trigger implementation

Testing:

# Test retry on fail
node = env['nodes'].create({
    'name': 'HTTP Request',
    'type': 'httpRequest',
    'canvas_id': canvas.id,
    'retry_on_failure': True,
    'max_retries': 3,
    'retry_interval': 5
})

# Execute workflow that fails
# Verify retries attempted
# Verify final error state

📅 Phase 8: Wait Node & Resumption (Week 7)

Goal: Implement wait node and execution resumption

Tasks:
1. ✅ Implement wait node execution
2. ✅ Save execution state for resumption
3. ✅ Generate resume webhook URLs
4. ✅ Implement resume endpoint
5. ✅ Handle wait timeouts
6. ✅ Test wait/resume flow

Deliverables:
- lib/nodes/wait_node.py
- controllers/resume_controller.py

Testing:

# Test wait node
wait_node = env['nodes'].create({
    'name': 'Wait',
    'type': 'wait',
    'canvas_id': canvas.id,
    'parameters': json.dumps({
        'resume': 'webhook'
    })
})

execution_id = executor.execute()
execution = env['executions'].search([('execution_id', '=', execution_id)])
assert execution.status == 'waiting'
assert execution.resume_url is not None

# Call resume URL
response = requests.post(execution.resume_url, json={'approved': True})

# Verify execution continued
execution.refresh()
assert execution.status == 'success'

📅 Phase 9: Sub-Workflow Execution (Week 8)

Goal: Implement sub-workflow calling

Tasks:
1. ✅ Implement Execute Sub-workflow node
2. ✅ Implement Execute Sub-workflow Trigger
3. ✅ Pass data between parent and sub-workflow
4. ✅ Handle wait for completion option
5. ✅ Link executions (parent → sub)
6. ✅ Test nested workflows

Deliverables:
- lib/nodes/execute_workflow_node.py
- lib/triggers/execute_workflow_trigger.py

Testing:

# Create parent and sub workflow
parent_canvas = env['canvas'].create({'name': 'Parent'})
sub_canvas = env['canvas'].create({'name': 'Sub'})

execute_node = env['nodes'].create({
    'name': 'Execute Sub-workflow',
    'type': 'executeWorkflow',
    'canvas_id': parent_canvas.id,
    'parameters': json.dumps({
        'workflowId': sub_canvas.workflow_id,
        'waitForCompletion': True
    })
})

# Execute parent
execution_id = executor.execute()

# Verify sub-workflow executed
sub_executions = env['executions'].search([('canvas_id', '=', sub_canvas.id)])
assert len(sub_executions) > 0

📅 Phase 10: Queue Mode (Optional - Week 9-10)

Goal: Implement distributed execution with queue

Tasks:
1. ✅ Install Odoo Queue module
2. ✅ Create queue jobs for executions
3. ✅ Implement worker process
4. ✅ Configure queue settings
5. ✅ Test queue execution
6. ✅ Monitor queue performance

Deliverables:
- Queue job definitions
- Worker configuration
- Queue monitoring dashboard

Database Schema

executions Model

File: models/executions.py

# -*- coding: utf-8 -*-
from odoo import api, fields, models
import json

class WorkflowExecutions(models.Model):
    _name = 'executions'
    _description = 'Workflow Executions (N8N Compatible)'
    _order = 'started_at desc'

    # Basic Information
    execution_id = fields.Char('Execution ID', required=True, index=True)
    canvas_id = fields.Many2one('canvas', string='Workflow', required=True, ondelete='cascade', index=True)

    # Execution Metadata
    mode = fields.Selection([
        ('manual', 'Manual'),
        ('production', 'Production'),
        ('partial', 'Partial')
    ], string='Execution Mode', required=True, default='manual')

    # Status
    status = fields.Selection([
        ('running', 'Running'),
        ('waiting', 'Waiting'),
        ('success', 'Success'),
        ('error', 'Error'),
        ('cancelled', 'Cancelled')
    ], string='Status', required=True, default='running', index=True)

    finished = fields.Boolean('Finished', default=False, index=True)

    # Timing
    started_at = fields.Datetime('Started At', required=True, default=fields.Datetime.now, index=True)
    stopped_at = fields.Datetime('Stopped At')
    duration = fields.Float('Duration (seconds)', compute='_compute_duration', store=True)

    # Wait/Resume Support
    wait_till = fields.Datetime('Wait Until', index=True)
    resume_url = fields.Char('Resume URL')

    # Retry Support
    retry_of = fields.Many2one('executions', string='Retry Of')
    retry_success_id = fields.Many2one('executions', string='Successful Retry')

    # Data Storage
    workflow_snapshot = fields.Text('Workflow Snapshot', help='JSON snapshot of workflow at execution time')
    execution_data = fields.Text('Execution Data', help='Complete IRunExecutionData equivalent')
    error_message = fields.Text('Error Message')

    # Relationships
    execution_log_ids = fields.One2many('execution_logs', 'execution_id', string='Execution Logs')

    @api.depends('started_at', 'stopped_at')
    def _compute_duration(self):
        for record in self:
            if record.started_at and record.stopped_at:
                delta = record.stopped_at - record.started_at
                record.duration = delta.total_seconds()
            else:
                record.duration = 0.0

    def to_n8n_format(self):
        """Export execution in n8n format"""
        self.ensure_one()
        return {
            'id': self.execution_id,
            'workflowId': self.canvas_id.workflow_id,
            'finished': self.finished,
            'mode': self.mode,
            'status': self.status,
            'startedAt': self.started_at.isoformat() if self.started_at else None,
            'stoppedAt': self.stopped_at.isoformat() if self.stopped_at else None,
            'duration': self.duration,
            'waitTill': self.wait_till.isoformat() if self.wait_till else None,
            'resumeUrl': self.resume_url,
            'errorMessage': self.error_message,
            'workflowData': json.loads(self.workflow_snapshot) if self.workflow_snapshot else {},
            'data': json.loads(self.execution_data) if self.execution_data else {}
        }

execution_logs Model

File: models/execution_logs.py

# -*- coding: utf-8 -*-
from odoo import api, fields, models

class ExecutionLogs(models.Model):
    _name = 'execution_logs'
    _description = 'Detailed Execution Logs per Node'
    _order = 'sequence, id'

    execution_id = fields.Many2one('executions', string='Execution', required=True, ondelete='cascade', index=True)
    node_id = fields.Many2one('nodes', string='Node', required=True)
    node_name = fields.Char('Node Name', required=True)

    sequence = fields.Integer('Sequence', required=True)

    # Timing
    started_at = fields.Datetime('Started At', required=True)
    finished_at = fields.Datetime('Finished At')
    duration = fields.Float('Duration (ms)', compute='_compute_duration', store=True)

    # Status
    status = fields.Selection([
        ('success', 'Success'),
        ('error', 'Error')
    ], string='Status', required=True)

    # Data
    input_data = fields.Text('Input Data', help='JSON array of input items')
    output_data = fields.Text('Output Data', help='JSON array of output items')
    error_message = fields.Text('Error Message')

    @api.depends('started_at', 'finished_at')
    def _compute_duration(self):
        for record in self:
            if record.started_at and record.finished_at:
                delta = record.finished_at - record.started_at
                record.duration = delta.total_seconds() * 1000  # milliseconds
            else:
                record.duration = 0.0

Canvas Model Enhancements

File: models/canvas.py (add to existing model)

class Canvas(models.Model):
    _inherit = 'canvas'

    # Activation
    active_workflow = fields.Boolean('Active', default=False, help='Whether workflow is activated')
    activated_at = fields.Datetime('Activated At')

    # Execution Settings
    execution_timeout = fields.Integer('Execution Timeout (seconds)', default=3600)
    save_execution_progress = fields.Boolean('Save Execution Progress', default=True)
    save_manual_executions = fields.Boolean('Save Manual Executions', default=True)
    save_success_executions = fields.Selection([
        ('all', 'All'),
        ('none', 'None')
    ], string='Save Success Executions', default='all')
    save_error_executions = fields.Selection([
        ('all', 'All'),
        ('none', 'None')
    ], string='Save Error Executions', default='all')

    # Error Workflow
    error_workflow_id = fields.Many2one('canvas', string='Error Workflow')

    # Executions
    execution_ids = fields.One2many('executions', 'canvas_id', string='Executions')
    execution_count = fields.Integer('Execution Count', compute='_compute_execution_count')
    last_execution_id = fields.Many2one('executions', string='Last Execution', compute='_compute_last_execution')

    @api.depends('execution_ids')
    def _compute_execution_count(self):
        for record in self:
            record.execution_count = len(record.execution_ids)

    @api.depends('execution_ids')
    def _compute_last_execution(self):
        for record in self:
            last = record.execution_ids.sorted('started_at', reverse=True)[:1]
            record.last_execution_id = last.id if last else False

    def action_activate(self):
        """Activate workflow"""
        self.ensure_one()

        # Validate workflow has triggers
        has_trigger = any('trigger' in node.type.lower() for node in self.node_ids)
        if not has_trigger:
            raise UserError("Cannot activate workflow without trigger nodes")

        # Register triggers
        from ..lib.trigger_manager import TriggerManager
        trigger_mgr = TriggerManager(self.env)
        trigger_mgr.register_workflow_triggers(self.id)

        self.write({
            'active_workflow': True,
            'activated_at': fields.Datetime.now()
        })

    def action_deactivate(self):
        """Deactivate workflow"""
        self.ensure_one()

        # Unregister triggers
        from ..lib.trigger_manager import TriggerManager
        trigger_mgr = TriggerManager(self.env)
        trigger_mgr.unregister_workflow_triggers(self.id)

        self.write({
            'active_workflow': False
        })

    def action_execute_workflow(self):
        """Execute workflow manually from UI"""
        self.ensure_one()

        from ..lib.workflow_executor import WorkflowExecutor
        executor = WorkflowExecutor(self.env, self.id, 'manual')
        execution_id = executor.execute()

        # Open execution view
        execution = self.env['executions'].search([('execution_id', '=', execution_id)], limit=1)
        return {
            'type': 'ir.actions.act_window',
            'name': 'Execution',
            'res_model': 'executions',
            'res_id': execution.id,
            'view_mode': 'form',
            'target': 'current'
        }

Nodes Model Enhancements

File: models/nodes.py (existing fields should already support execution)

# Existing fields that support execution:
# - retry_on_failure
# - max_retries
# - retry_interval
# - continue_on_fail
# - parameters (JSON)

# No additional fields needed for basic execution

Python Backend

WorkflowExecutor Class

File: lib/workflow_executor.py

(See complete implementation in main research document section "Implementation Recommendations > 3. Workflow Executor")

Key Methods:
- execute(): Main entry point
- _create_execution_record(): Create DB record
- _execute_nodes(): Node-by-node execution loop
- _execute_node(): Single node execution
- _execute_node_by_type(): Type-specific logic
- _add_connected_nodes_to_stack(): Queue next nodes
- _finalize_execution(): Mark complete

Execution Controller

File: controllers/execution_controller.py

(See complete implementation in main research document section "Implementation Recommendations > 4. Execution Controller")

Routes:
- POST /canvas/<id>/execute
- GET /canvas/<id>/executions
- GET /executions/<id>
- POST /executions/<id>/retry
- POST /canvas/<id>/activate
- POST /canvas/<id>/deactivate

JavaScript Frontend

Workflow Executor Service

File: static/src/js/workflow_executor.js

(See complete implementation in main research document section "Implementation Recommendations > 5. Frontend Integration")

Methods:
- executeWorkflow(canvasId, mode, triggerData)
- getExecutions(canvasId, limit, offset)
- getExecution(executionId)
- retryExecution(executionId)
- activateWorkflow(canvasId)
- deactivateWorkflow(canvasId)

Testing Strategy

Unit Tests

File: tests/test_workflow_executor.py

from odoo.tests import TransactionCase
import json

class TestWorkflowExecutor(TransactionCase):

    def setUp(self):
        super().setUp()
        # Create test workflow
        self.canvas = self.env['canvas'].create({
            'name': 'Test Workflow',
            'workflow_id': 'test_wf_1'
        })

    def test_execute_simple_workflow(self):
        """Test executing workflow with 2 nodes"""
        # Create nodes
        node1 = self.env['nodes'].create({
            'name': 'Manual Trigger',
            'type': 'manual',
            'canvas_id': self.canvas.id,
            'node_id': 'node_1'
        })
        node2 = self.env['nodes'].create({
            'name': 'Set Node',
            'type': 'set',
            'canvas_id': self.canvas.id,
            'node_id': 'node_2',
            'parameters': json.dumps({'values': {'test': 'value'}})
        })

        # Set connections
        self.canvas.write({
            'connections': json.dumps({
                'Manual Trigger': {
                    'main': [[{'node': 'Set Node', 'type': 'main', 'index': 0}]]
                }
            })
        })

        # Execute
        from ..lib.workflow_executor import WorkflowExecutor
        executor = WorkflowExecutor(self.env, self.canvas.id, 'manual')
        execution_id = executor.execute()

        # Verify
        execution = self.env['executions'].search([('execution_id', '=', execution_id)])
        self.assertTrue(execution)
        self.assertEqual(execution.status, 'success')
        self.assertEqual(len(execution.execution_log_ids), 2)

    def test_retry_on_failure(self):
        """Test node retry mechanism"""
        # Create failing node
        node = self.env['nodes'].create({
            'name': 'Failing Node',
            'type': 'httpRequest',
            'canvas_id': self.canvas.id,
            'node_id': 'node_1',
            'retry_on_failure': True,
            'max_retries': 3,
            'retry_interval': 1,
            'parameters': json.dumps({'url': 'http://invalid-url-that-fails.test'})
        })

        # Execute (should fail after retries)
        from ..lib.workflow_executor import WorkflowExecutor
        executor = WorkflowExecutor(self.env, self.canvas.id, 'manual')

        with self.assertRaises(Exception):
            executor.execute()

        # Verify retries attempted (check logs)
        execution = self.env['executions'].search([('canvas_id', '=', self.canvas.id)], limit=1)
        self.assertEqual(execution.status, 'error')

    def test_continue_on_fail(self):
        """Test continue on fail"""
        # Create workflow: trigger -> failing node -> success node
        node1 = self.env['nodes'].create({
            'name': 'Trigger',
            'type': 'manual',
            'canvas_id': self.canvas.id,
            'node_id': 'node_1'
        })
        node2 = self.env['nodes'].create({
            'name': 'Failing Node',
            'type': 'httpRequest',
            'canvas_id': self.canvas.id,
            'node_id': 'node_2',
            'continue_on_fail': True,
            'parameters': json.dumps({'url': 'http://invalid.test'})
        })
        node3 = self.env['nodes'].create({
            'name': 'Success Node',
            'type': 'set',
            'canvas_id': self.canvas.id,
            'node_id': 'node_3',
            'parameters': json.dumps({'values': {'success': True}})
        })

        self.canvas.write({
            'connections': json.dumps({
                'Trigger': {'main': [[{'node': 'Failing Node'}]]},
                'Failing Node': {'main': [[{'node': 'Success Node'}]]}
            })
        })

        # Execute
        from ..lib.workflow_executor import WorkflowExecutor
        executor = WorkflowExecutor(self.env, self.canvas.id, 'manual')
        execution_id = executor.execute()

        # Verify workflow completed despite node 2 failure
        execution = self.env['executions'].search([('execution_id', '=', execution_id)])
        self.assertEqual(execution.status, 'success')

        # Node 2 should have error log
        node2_log = execution.execution_log_ids.filtered(lambda l: l.node_name == 'Failing Node')
        self.assertEqual(node2_log.status, 'error')

        # Node 3 should have success log
        node3_log = execution.execution_log_ids.filtered(lambda l: l.node_name == 'Success Node')
        self.assertEqual(node3_log.status, 'success')

Integration Tests

File: tests/test_execution_api.py

from odoo.tests import HttpCase

class TestExecutionAPI(HttpCase):

    def test_execute_workflow_endpoint(self):
        """Test POST /canvas/<id>/execute"""
        # Authenticate
        self.authenticate('admin', 'admin')

        # Create workflow
        canvas = self.env['canvas'].create({'name': 'API Test Workflow'})

        # Call API
        response = self.url_open(
            f'/canvas/{canvas.id}/execute',
            data=json.dumps({'mode': 'manual'}),
            headers={'Content-Type': 'application/json'}
        )

        result = json.loads(response.content)
        self.assertTrue(result['success'])
        self.assertIn('executionId', result)

    def test_get_executions_endpoint(self):
        """Test GET /canvas/<id>/executions"""
        # Setup
        canvas = self.env['canvas'].create({'name': 'Test Workflow'})
        # ... create some executions ...

        # Call API
        response = self.url_open(f'/canvas/{canvas.id}/executions')
        result = json.loads(response.content)

        self.assertTrue(result['success'])
        self.assertIn('data', result)
        self.assertIsInstance(result['data'], list)

Deployment Checklist

Pre-Deployment

[ ] All unit tests passing
[ ] All integration tests passing
[ ] Database migrations tested
[ ] Security rules reviewed
[ ] Performance benchmarks met
[ ] Documentation updated

Deployment Steps

Backup Database
bash pg_dump odoo_db > backup_before_execution_module.sql
Update Module
bash odoo-bin -u the_ai_automator -d odoo_db --stop-after-init
Verify Models Created
python # In Odoo shell env['executions'].search([]) env['execution_logs'].search([])
Test Execution
Create test workflow
Execute manually
Verify execution appears in list
Check logs
Test Triggers
Activate workflow with webhook
Call webhook URL
Verify production execution
Monitor Performance
Check execution times
Monitor database size
Check memory usage

Post-Deployment

[ ] User training conducted
[ ] Documentation delivered
[ ] Support channels established
[ ] Monitoring dashboard configured
[ ] Backup schedule verified

📊 Success Metrics

Performance Targets

Execution Start Time: < 500ms
Node Execution Time: < 2s average
Database Query Time: < 100ms
API Response Time: < 300ms

Reliability Targets

Execution Success Rate: > 95%
Error Recovery Rate: > 90%
Webhook Response Rate: > 99%

🎯 Next Steps

Phase 1: Start with database foundation
Phase 2: Implement core execution engine
Phase 3: Build API layer
Phase 4: Create frontend
Phases 5-8: Add trigger systems and advanced features

Estimated Timeline: 8-10 weeks for full implementation

Document Version: 1.0
Last Updated: October 1, 2025
Status: Ready for Implementation