Why Multi-Agent Instead of Single Agent?
A single AI agent generating an entire Odoo module handles everything: architecture, models, views, security, tests. The quality is good but not optimal — no single prompt captures every concern. Multi-agent systems split the work among specialists: each agent is expert in one domain and produces higher quality output for that domain.
The Agent Team
| Agent | Specialization | Output |
|---|---|---|
| Architect | Module design, model relationships | Module spec: models, fields, relationships |
| Model Developer | Python ORM code | models/*.py with all business logic |
| View Developer | XML views and UI | views/*.xml with form, list, kanban, search |
| Security Developer | Access control | security/ir.model.access.csv + record rules |
| Test Developer | Testing | tests/*.py with comprehensive test cases |
| Reviewer | Code quality and anti-patterns | Review report with issues and fixes |
Orchestration Pattern
# Sequential pipeline:
spec = architect_agent.run("Design a module for...")
models = model_agent.run(f"Implement these models: {spec}")
views = view_agent.run(f"Create views for: {spec}")
security = security_agent.run(f"Create access rules for: {spec}")
tests = test_agent.run(f"Write tests for: {models}")
review = reviewer_agent.run(f"Review this module: {models} {views} {security}")
# Fix issues from review:
if review.has_issues:
fixed_models = model_agent.run(f"Fix: {review.issues}")Why This Works for Odoo
- Separation of concerns — Odoo modules have clear file-type boundaries (Python, XML, CSV)
- Predictable structure — Each agent knows exactly what files to produce
- Verifiable output — Each agent's output can be validated independently
- Error isolation — If the view agent produces bad XML, the model agent's Python is unaffected
Implementation with CrewAI
from crewai import Agent, Task, Crew
architect = Agent(
role="Odoo Module Architect",
goal="Design clean, well-structured Odoo 19 modules",
backstory="Expert Odoo architect with 10 years experience..."
)
model_dev = Agent(
role="Odoo Model Developer",
goal="Write production-quality Odoo 19 Python models",
backstory="Senior Odoo developer specializing in ORM..."
)
view_dev = Agent(
role="Odoo View Developer",
goal="Create intuitive, version-correct XML views",
backstory="Odoo UI specialist. Uses <list> not <tree>..."
)
crew = Crew(
agents=[architect, model_dev, view_dev, security_dev, test_dev],
tasks=[design_task, model_task, view_task, security_task, test_task],
process="sequential"
)
result = crew.kickoff()Benefits vs Single Agent
| Aspect | Single Agent | Multi-Agent |
|---|---|---|
| Speed | Faster (one pass) | Slower (multiple passes) |
| Quality | Good (80-90%) | Better (90-95%) |
| Consistency | Variable | High (specialized prompts) |
| Error detection | Post-hoc review | Built-in (reviewer agent) |
| Complexity handling | Struggles with large modules | Handles well (divide and conquer) |
When to Use Multi-Agent
- Large modules with 10+ models and complex relationships
- Modules requiring sophisticated security rules
- High-quality requirements (production code, not prototypes)
- Batch module generation (multiple modules in parallel)
Getting Started
Start with a single agent (Claude Code) for small modules. Graduate to multi-agent systems when you need higher quality or are generating complex modules. Deploy the results to DeployMonkey for testing and production hosting.