Multi-Agent Platform

The next leap in applied AI is not a bigger model — it is many models working together. Our multi-agent research studies how autonomous agents can plan collaboratively, share memory, negotiate roles and recover from failure without a human in the loop for every step.

We investigate orchestration patterns — hierarchical commanders and workers, peer-to-peer swarms, and market-based task allocation — and the communication protocols that keep them coherent at scale. The goal is a platform where specialised agents can be composed like building blocks into reliable, auditable systems.

Key themes: agent memory and grounding, tool-use safety, conflict resolution between agents, and measurable evaluation of collective behaviour.