Welcome to AMESA
What is AMESA?
AMESA is a platform for building, training, and deploying multi-agent AI systems that optimize physical and industrial processes.
AMESA 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 AMESA platform works:
Why AMESA?
By unifying simulation, orchestration, training, and deployment into a single platform, AMESA 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
From Data to Autonomy
AMESA supports a complete process from data to autonomy: first, historical data is used to create a simulator for agent feedback and to identify required agent skills. Then, using expert knowledge, agents are built, configured and trained on the simulation, learning the skills they need to control the system autonomously. Finally, agents are connected to live enterprise systems for closed-loop control or operator support.
Read a whitepaper to learn more.
🧩 Data to Autonomy Overview
1. Establish Agent Feedback (Data and Simulation)
AMESA supports two ways to generate simulations:
Upload historical industrial data (CSV format) to generate data-driven simulations automatically using the AMESA Simulation Lab.
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. Identify Agent Skills (Activity Clusters and Scenarios)
Activity clusters represent different operating regions where the team of agents needs to behave differently to succeed. AMESA automatically identifies activity clusters by analyzing the historical data. These activity clusters can then be entered into the platform as different scenarios for agent training.
3. Build Agents (Knowledge Components)
AMESA allows you to build agents using expert knowledge components with one of three workflows:
A. No-code workflow: 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.
B. Publication workflow: 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.
C. Coding agent workflow: Using the AMESA Autonomy MCP Server, you can use coding agents to write agent components and publish them to the Agent Orchestration Studio to use in agent designs.
Then 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.
Once orchestrated, your multi-agent system connects to AMESA’s Agent Cloud, 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.
4. Deploy Agents
Once trained, agents can be exported for deployment and connected to the production system using either:
The AMESA runtime server connects to the production environment using MQTT, OPCUA or HTTP, for use with HMI, DCS, SCADA, or any IoT platform
The AMESA runtime app connects directly to the PLC
This closes the loop from simulation to action—turning data into autonomy at scale.
Quick Links
Find articles on key topics with these links.
Try Out a Tutorial
Import Agents and ML-Models to AMESA
You can use any model, API, or Python algorithm with AMESA 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
AMESA 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 AMESA agentic systems are designed and trained, you can export them to the AMESA runtime to connect with your system. Learn how to deploy an agent within the runtime container and how to use AMESA's tools to analyze agent behavior during both training and deployment.
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