Research
Research · Flagship
DCNA
Decentralised Cognitive Agent Network Architecture: a distributed framework of autonomous AI agents that coordinate, learn and make decisions across operations, without central control.
DCNA is DaveLabs’ most ambitious research. Instead of one monolithic system that needs constant human oversight, it builds a self-organising ecosystem of specialised agents, much like a biological neural network, spread across an organisation’s infrastructure.
Each agent is trained for one domain (logistics, quality control, demand forecasting, resource allocation) and speaks a common protocol, so intelligence flows between domains.
Core architecture
How it is built.
- 01
Distributed intelligence
DCNA spreads cognitive work across a network of specialised agents, each autonomous, all coordinating through a shared protocol layer. - 02
Autonomous decisions
Each agent reasons within its own domain and decides in real time without central oversight. The network organises itself. - 03
Cross-domain coordination
Agents talk across industries: an agriculture agent can coordinate with a logistics agent to optimise a whole pipeline. - 04
Federated learning
Agents improve without centralising data. Each node learns locally and shares only model updates.
Industry applications
Where it applies.
Agriculture
Crop-monitoring agents coordinate with supply-chain agents to predict yield, optimise irrigation and schedule logistics.Energy
Grid agents manage distributed energy, predict demand and negotiate power across microgrids in real time.Manufacturing
Factory-floor agents watch equipment health, predict failures and reroute production.Logistics
Fleet agents optimise routes, predict delivery windows and coordinate with warehouse agents for the last mile.
Interested in DCNA research?
Talk to our research team at research@davelabs.co.