Distributed Artificial Intelligence
The AI subfield where several distributed agents or nodes cooperate or compete to solve problems beyond any single one: from distributed problem solving and multi-agent systems to the 2026 revival with LLMs and federated learning.
Distributed artificial intelligence (DAI) is the subfield of AI that studies how several agents or nodes, spread out and without a single central controller, cooperate —or compete— to solve problems that exceed any one agent. Rather than concentrating all reasoning in a single system, DAI distributes knowledge, decisions and computing power across entities that communicate, coordinate and, at times, negotiate with one another.
Two roots: distributed problem solving and multi-agent systems
The literature distinguishes distributed problem solving, in which several branches cooperate toward a shared goal, from multi-agent systems, which focus on managing the behaviour of several independent entities.
Not the same as distributed computing
DAI should not be confused with plain distributed computing. Splitting a computation across many machines —or training a model on several GPUs— distributes the work, but the nodes decide nothing: they run what they are assigned. In distributed artificial intelligence, by contrast, each agent keeps some autonomy to decide, prioritize or negotiate. The difference is not the hardware but who makes the decisions, and how independently. Documentation: academic multi-agent systems review.
The 2026 revival: LLM multi-agent systems and federated learning
After years in the background, DAI is back at the center. Multi-agent systems built on large language models assign roles to several models —to plan, critique, execute— that collaborate on a single problem, reviving the old questions about coordination. In parallel, federated learning, formulated by Brendan McMahan and colleagues at Google in 2017, trains a shared model without centralizing data: each device learns locally and shares only updates. None of this is settled. Recent surveys show that adding agents does not guarantee better results than a single well-designed agent, and that reliable coordination and the emergence of collective behavior —useful or unwanted— remain open problems. Documentation: original AutoGen paper; original federated-learning paper.
This article was produced with artificial intelligence under human editorial oversight.
