Events

Agent Benchmarking

Build & Compare Winning Multi-Agent Systems with Benchmarking

April 3^rd , 8am PST

Objectively measure, compare, and optimize multi-agent AI system designs.

Just as championship teams require the right structure and clear performance metrics, your multi-agent AI systems need objective measurement to achieve optimal performance. The challenge lies in knowing which configuration truly delivers the best results before real-world deployment.

Walk away knowing how to:
> Set up performance benchmarks using your own KPIs
> Automatically test multiple multi-agent system designs in simulation
> Visualize and compare performance data across different agent configurations
> Quantify the financial impact of your AI improvements (e.g., "a 2% improvement equals $1M per year")
> Make data-driven decisions about which multi-agent system design to deploy

AI that Works

Multi-agent AI Systems 101

April 17^th, 8am PST

Discover how well-engineered multi-agent AI systems unlock real-world ROI.

Why don't the most popular AI solutions work in industrial settings?

It's rarely about the raw capability of the technology and almost always about its application.When AI is engineered like other mission-critical systems – modular, composable, and taught by experts – it delivers transformative results without sacrificing control or transparency.

Walk away knowing how to:
> Why industrial AI needs multi-agent AI systems
> How purpose-built multi-agent systems succeed in industrial environments
> A framework for capturing and scaling operator expertise through machine teaching
> Practical approaches to building multi-agent AI that delivers measurable results

Platform Walkthrough

Build a Million Dollar Industrial Agent!

April 24^th, 8am PST

Come watch us LIVE build an agent that makes million-dollar decisions 13% better than industry benchmarks.

Invite your engineering teams, your expert operators, data scientists, machine learning gurus and R&D teams.

Walk away knowing how to:
> Design multiple agents for a real industrial use case
> Analyze agent behavior
> Train agents to perform high value tasks
> Use your own Python algorithms and machine learning models in Composabl
> Deploy trained agents to control real equipment