@h100envy: This paper completely changed how I think about a swarm of agents: Describe an agent as a graph -> Nodes are operations…

This paper completely changed how I think about a swarm of agents: Describe an agent as a graph -> Nodes are operations -> Edges are information flow -> Wire agents into one graph -> Optimize the graph itself automatically Here is the 5-step blueprint: Agent as a graph: any LLM agent is represented as a computational graph, where nodes are functions that process data or query the model. Edges as links: edges define the information flow between operations, that is who passes a result to whom inside an agent and between agents. Composition: graphs are recursively assembled into large composite graphs, so a swarm of agents becomes one object instead of a pile of hand-written scripts. Node optimization: the first optimizer automatically tunes prompts at the level of individual nodes. Edge optimization: the second optimizer uses RL to change graph connectivity, that is the orchestration of the swarm itself, cutting useless links and keeping the working ones. Key insight: you do not hand-design a swarm's topology; graph connectivity is a learnable parameter optimized like weights. Two levels of auto-optimization - node prompts and edge connectivity - turn scattered agent frameworks into one optimizable graph. Read this, then check the article below.