Head of Learning
DeepSeek dominated headlines last month highlighting its impressive performance compared to other GPTs on the market. But beneath the surface, it uncovered something we've known all along about building effective AI systems: methodology matters.
Deepseek is a multi-agent system that combines multiple “skill agents,” each designed to excel at specific tasks. Think of it as building a football team. Each player is a specialist. When working together, these specialists build a competitive roster.
If the system is a team, orchestration is the coach. The orchestrator learns through practice to implement various strategies and make better decisions over time.
These techniques form the core of the machine teaching methodology that powers Composabl's platform. The result is a purpose-built system that delivers tangible business impact by addressing the complex realities of industrial operations.
In a chemical manufacturing facility, specialized agents control a thermal reactor's temperature – one for startup, another for transition periods, and a third for steady-state production.
Result: 95% conversion rate with no thermal runaway risks, versus 82% with traditional methods.
In an industrial bakery, a multi-agent AI system practiced millions of decisions in simulation, learning when to prioritize products and how to respond to changing conditions.
Result: 28% profit margin compared to 7% with conventional optimization.
By applying these techniques through machine teaching, Composabl enables engineers to build multi-agent AI systems that deliver tangible business results: reduced downtime, balanced operational priorities, real-time adaptability, and improved consistency across your operations.
For a deeper dive into these real-world applications, read our full use cases to see how these techniques translate into measurable ROI.
