Composabl Workflow

A Two-Part Platform

Composabl is a two-part platform, with a no-code UI and a Python SDK and CLI. The interplay of these parts is what gives Composabl its combination of usability and power.

The two parts enable teams to work together effectively. People and roles who primarily use code, such as data scientists and controls engineers, use the SDK to create components of agents like ML models and deep reinforcement learning skills. Then subject matter experts, process engineers, and others can use the no-code interface to easily create agents from these modular building blocks and train them to succeed.

We designed the platform this way because for complex, high-value processes, there will be some tasks that can only be done through coding - and some team members who prefer to work in code - and other tasks that are better performed through a visual interface. Both parts of the platform work together

Most users use both parts of the platform to some extent, but spend more time in either the no-code UI or the SDK depending on their expertise and role. How you will use the platform depends on your role and what part of the process you are working on.

Workflow Steps

The main workflow for Composabl is:

• Step 1: SDK | Create agent components

  • Create skill to train with rewards using deep reinforcement learning.

  • Create or package ML models to import to UI to add advanced perception to agents.

  • Create or package LLMs to import to UI add natural language to agents.

  • Create or package controllers and optimization algorithms to import to UI to add programmed decision-making to agents.

  • Connect simulators to Composabl.

• Step 2: SDK | Publish agent components to the UI with one CLI command

• Step 3: UI | Orchestrate modular components together to create agents in the UI

• Step 4: UI |Train agents at scale with one click using the UI

• Step 5: UI and SDK | Export trained agents and connect them to the Composabl runtime for deployment