Research
Research
Our research focuses on pushing the boundaries of agent-based systems, coordination protocols, and evaluation methods for AI infrastructure. This section outlines the foundational experiments, architectures, and internal protocols shaping the next generation of multi-agent intelligence at TensorOne.
Explore Our Research Areas
Investigates large-scale collaboration between autonomous agents, focusing on distributed planning, agent persona diversity, and real-time decision-making in uncertain environments.
Key topics:
- Role-based agents
- Task routing and delegation
- Emergent coordination behavior
MCP (Model Coordination Protocol)
Our internal protocol for routing and orchestrating model interactions across backends. MCP enables:
- Dynamic load balancing
- Multi-modal handoffs
- Graceful degradation with fallback logic
The A2A protocol defines how agents communicate, argue, and collaborate via structured message schemas. Inspired by language games and dialogue trees, A2A supports:
- Goal-alignment through negotiation
- Hierarchical conversations
- Chain-of-command escalation
Graphs and Finite State Machines
Explores how finite state machines and directed graphs can model agent cognition and control flows. Includes:
- Declarative graph-based behavior trees
- Visual planning interfaces
- Rule-engine driven state transitions
A custom evaluation suite for benchmarking model performance, chain reliability, and agent behaviors under different conditions. Features:
- Scenario-driven testing
- Metric hooks (latency, accuracy, fallback success)
- Prompt mutation and stress testing
This research forms the experimental core of our AI stack, guiding real-world implementations in serverless endpoints, autonomous agents, and coordination-aware model design.