Foundations
Multi Agent Applications
Multi-agent systems (MAS) are a central pillar of our research, aimed at enabling autonomous agents to reason, communicate, and coordinate toward shared goals in dynamic environments.
We create systems that are more adaptive, scalable, and contextually intelligent than single-agent architectures by modelling groups of specialised AI agents.
Why Multi-Agent Systems?
Traditional LLM deployments are typically stateless and isolated. In contrast, multi-agent systems introduce:
- Persistent roles and memory
- Organizational logic and hierarchy
- Task delegation based on agent capabilities
- Emergent behavior from interaction patterns
- Redundancy and fallback mechanisms
- Parallel processing via asynchronous agents
Core Patterns
Role-Based Agent Design
Agents are assigned distinct roles—such as Planner, Researcher, Critic, or Synthesizer—each with its own prompt schema, memory scope, and communication protocol.
cssCopyEditUser → Manager Agent → [Planner → Researcher → Critic → Synthesizer] → Output
This architecture enforces responsibility boundaries and modular agent logic.
Dynamic Agent Swarms
We support dynamic agent creation where new agents are spawned based on task complexity. Agents collaborate, vote, and negotiate to:
- Reach consensus
- Trigger re-planning in deadlocks
- Escalate via chain-of-command
Frameworks We Use
Our MAS systems are powered by the following tools:
- CrewAI – Multi-agent manager for LLM workflows
- LangGraph – Flow control and memory orchestration
- A2A Protocol – Internal structured messaging layer
- TensorOne MCP – Load-balanced compute routing across clusters
Use Cases Explored
- Distributed reasoning: Agents solving complex multi-hop questions
- Chain of thought with checkpoints: Verification between agents before progressing
- Real-time feedback loops: Execution agents audited by critics
- Persona fusion: Dynamic skill adoption via memory retrieval