Multi-agent coordination
Orchestrate multiple AI agents working on shared objectives. Manage dependencies, handoffs, and parallel execution across heterogeneous models and providers.
Arai
Coordinate multiple AI agents across complex workflows. Arai manages context propagation, resource allocation, and inter-agent communication with transparency and control.
Performance targets
Research-stage performance targets for agent orchestration at scale.
Key features
Context-aware routing
Route tasks to the most appropriate agent based on capability, context history, and current workload. Context propagates between agents without loss or corruption.
Transparent operations
Full visibility into agent decision-making, inter-agent communication, and workflow progression. Every step is logged, traceable, and explainable.
Self-hosted control
Run the entire orchestration layer on your own infrastructure. No external dependencies for coordination, scheduling, or state management.
Workflow optimisation
Adaptive scheduling and resource allocation based on workflow characteristics. Minimise latency and cost while maximising throughput across agent pools.
Privacy-preserving
Agent communication and context sharing respect data boundaries. Sensitive information is isolated, and cross-agent data flow is governed by policy.
Arai is in development
Arai is in early research and development. Register your interest to follow progress and participate in shaping the platform.
Development updates will be shared with registered participants.