Welcome to Composabl
What is Composabl?
Composabl is a platform for building, training, and deploying multi-agent AI systems that optimize physical and industrial processes.
At its core, Composabl connects data, simulations, agents, training infrastructure, and runtime environments into a cohesive system that turns real-world domain knowledge into autonomous behavior.
The architecture below provides an overview of how the Composabl platform works:
🧩 Platform Overview
1. Import Data to Build Simulations
Composabl supports two ways to generate simulations:
Upload historical industrial data (CSV format) to generate data-driven simulations automatically.
Connect an existing simulation, such as a physics model or emulator, using a Docker container.
These simulations provide a safe environment for skill agents to learn and be evaluated.
2. Create Skill Agents in the No-Code UI
In the Agent Orchestration Studio, you can create skill agents without writing code by defining:
Goals – what the agent is trying to achieve (e.g., maximize throughput, maintain temperature)
Constraints – rules the agent must follow (e.g., stay within safe operating bounds)
Success Criteria – signals that the agent is performing correctly
This no-code method allows domain experts to encode their expertise directly into agent logic.
3. Import Skill Agents and ML Models
You can bring in your own logic and models using the Agent Import API.
Examples include:
Python-based MPC and PID controllers
LLM-powered agents
Custom applications and heuristics
Pretrained ML models
Once imported, these skill agents are available in the Agent Orchestration Studio—a no-code interface for assembling agents into a multi-agent system.
3. Design Multi-Agent Systems
Use the Agent Orchestration Studio to visually compose and coordinate skill agents, perceptors, and orchestrators. This is where domain experts design system-level behavior using drag-and-drop components.
4. Train Agents with Composabl’s AI Engine
Once orchestrated, your multi-agent system connects to Composabl’s AI Training Engine, which leverages scalable infrastructure—such as Microsoft Azure or AWS Kubernetes clusters—to train and evaluate agents in simulation.
Training uses a reward-driven learning process that improves agent performance over time.
5. Export to Runtime and Edge Environments
Once trained, agents can be exported for deployment:
Into real-world production environments
To external Edge IoT platforms
Or into downstream runtime applications that need to embed intelligence
This closes the loop from simulation to action—turning data into autonomy at scale.
Why Composabl?
By unifying simulation, orchestration, training, and deployment into a single platform, Composabl enables teams to:
Reduce time to deploy autonomous agents
Leverage existing models, data, and controls
Combine rule-based, learned, and ML-driven behavior
Bridge the gap between operational expertise and AI
Quick Links
Find articles on key topics with these links.
Try Out a Tutorial
Import Agents and ML-Models to Composabl
You can use any model, API, or Python algorithm with Composabl for training agent systems, adding perception, analysis, and communication, and making decisions. See how to configure different types of modules in the UI and by publishing them via the data science workflow for agent system design, training, and deployment.
Create a data-driven simulation
Use your historical dada and our no-code simulation tool to create a data-driven simulation.
Integrate a machine learning model
Use existing ML models for machine vision, prediction, or analysis in Composabl agent systems
Create Modular Skill Agents
Composabl multi-agent systems are built on modular skills that break down a task into separate parts. Learn how to create skill agents to train with deep reinforcement learning.
Create skill agents with goals and constraints
Create skills agents with subject matter expertise by configuring goals and constraints for learning
Deploy Multi-Agent Systems
Once Composabl agentic systems are designed and trained, you can export them to the Composabl runtime to connect with your system. Learn how to deploy an agent within the runtime container and how to use Composabl's tools to analyze agent behavior during both training and deployment.
Evaluate the performance of your multi-agent system
Evaluate performance using the Composabl benchmarking feature
Deploy a Composabl multi-agent system
Export a multi-agent system and connect to the Composabl runtime container
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