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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


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 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.

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.

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