Building a Multi-Agent Service Architecture with Laravel Queue Workers for CFD Simulations

In the world of engineering simulations, workflows are rarely linear. Especially in Computational Fluid Dynamics (CFD), tasks like meshing, solving, and post-processing each come with their own complexity, tools, and hardware requirements. To efficiently manage this complexity, I've developed a modular, multi-agent service system using Laravel queue workers. The goal: create a flexible, scalable architecture that handles distributed simulation tasks automatically and efficiently.

Why Multi-Agent Systems for CFD?

In my current project, each CFD workflow is split into three distinct stages: meshing the geometry, solving the fluid equations, and finally analyzing the results through post-processing. Each of these stages is computationally intensive and benefits from being executed on specialized systems optimized for their specific requirements.

A multi-agent service architecture allows us to treat each stage as a separate "agent" or service, each with its own queue workers and logic. These services communicate asynchronously through Laravel queues, providing fine-grained control and enabling parallel execution across multiple machines or containers – significantly reducing total simulation time.

Laravel Queues: A Natural Fit

Laravel's robust queue system makes it straightforward to dispatch, delay, and manage background jobs across a distributed system. I'm using a SQL database as the queue backend for reliable job persistence, combined with Laravel's queue monitoring for insights. Each agent — the mesher, solver, and post-processor — listens to a dedicated queue and performs its specialized function independently.

Architecture Overview

• Mesher Agent: Generates computational meshes based on CAD geometry, using tools like snappyHexMesh or STAR-CCM+ meshing scripts. Optimized for memory-intensive operations.
• Solver Agent: Launches CFD solvers with appropriate physics models and boundary conditions, utilizing either single-node or multi-node configurations for parallel computation.
• Post-Processor Agent: Runs result extraction, data aggregation, and generates visual outputs (e.g., contour plots, streamlines, or force/moment time histories).

Each agent is deployed in a separate container or VM, strategically allocated based on computational load requirements and licensing restrictions of the specialized software being used.

Communication & Synchronization

A central Laravel controller orchestrates the job flow, ensuring dependencies between stages are respected. For example, the solver will only start once the mesher agent marks its job as complete. This logic is handled through chained jobs and event listeners in Laravel, creating a robust workflow pipeline.

Data is passed between agents using a shared file system (e.g., an NFS mount or cloud storage) where job folders contain all necessary input and output files. This approach maintains simplicity while ensuring all agents have access to the required data.

Code Example: Job Chaining and Dependency Management

Here's a simplified implementation of how the job chain is created in the simulation controller:

Bus::chain([
    new MeshGeometryJob($simulation),
    new SolveCfdJob($simulation),
    new ProcessResultsJob($simulation)
])->dispatch();

Scalability & Optimization

One of the key advantages of this approach is dynamic scalability. When processing multiple simulations concurrently, I can instantly spin up additional queue workers on available VMs, or prioritize urgent simulations using Laravel's queue prioritization system.

I've implemented auto-scaling based on workload — automatically deploying additional solver workers when job queues exceed certain thresholds, and gracefully shutting them down during idle times to optimize resource utilization and reduce costs.

Code Example: Multi-Queue Worker Configuration

The following shows how queue workers are configured with different settings based on their roles:

'supervisor-1' => [
    'connection' => 'database',
    'queue' => ['meshing'],
    'maxProcesses' => 3,
    'timeout' => 3600, // 1 hour timeout
]

Challenges & Learnings

One of the biggest challenges has been managing software licenses across distributed nodes, especially for commercial CFD solvers with restrictive licensing models. I've implemented a license-aware scheduler that intelligently queues jobs only when sufficient licenses are available, preventing wasteful wait times.

while (!$this->licenseManager->isLicenseAvailable($type)) {
    sleep(300); // Wait 5 minutes and check again
}

Another critical learning has been designing for fault tolerance. Simulation jobs can fail at any stage — mesh generation errors, solver convergence issues, or corrupted output files. Each agent now incorporates sophisticated retry mechanisms, error logging, and notification systems, making the workflow significantly more robust and self-healing.

Conclusion

Building a distributed, multi-agent system for CFD simulation workflows has been a challenging yet rewarding endeavor. Laravel's ecosystem has provided the ideal combination of simplicity, extensibility, and monitoring capabilities to make this architecture feasible and effective.

As engineering simulation workflows continue to grow in complexity and scale, modular service-based architectures like this will be increasingly crucial for maintaining scalability, efficiency, and reliability. The lessons learned here extend beyond CFD into other computationally-intensive domains such as machine learning pipelines, large-scale data processing, or any workflow requiring distributed execution.

If you're working on similar distributed systems — whether for engineering simulations, data processing, or other complex workflows — I'd welcome the opportunity to connect and exchange ideas.