Research

Our research focuses on advancing 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

Multi-Agent Applications

We explore large-scale collaboration between autonomous agents, emphasizing distributed planning, persona diversity, and decision-making under uncertainty.

Key topics:

• Role-based agents
• Task routing and delegation
• Emergent coordination behavior

MCP (Model Coordination Protocol)

TensorOne's internal protocol for orchestrating model interactions across backends.

Enables:

• Dynamic load balancing
• Multi-modal handoffs
• Graceful degradation via fallback logic

Agent2Agent (A2A) Protocol

A structured communication protocol allowing agents to interact, negotiate, and align goals.

Core features:

• Goal alignment through negotiation
• Hierarchical dialogue and escalation
• Structured message schemas inspired by language games

Graphs and Finite State Machines

We use finite state machines and graph structures to model agent cognition and control flows.

Includes:

• Declarative graph-based behavior trees
• Visual planning and debugging tools
• Rule-driven state transitions

TensorOne Evals

A custom benchmarking suite for measuring model and agent performance under realistic and adversarial conditions.

Features:

• Scenario-based evaluation
• Metric hooks (latency, accuracy, fallback rates)
• Prompt mutation and chain stress testing

These research efforts form the experimental foundation of our AI stack, directly influencing real-world systems like serverless endpoints, multi-agent runtimes, and coordination-aware model design.