multi-agent-token-optimization

Multi-agent token optimization requires a transition from simple prompt trimming to architectural and runtime engineering Verified Answer #1. High token consumption in local multi-agent systems is typically caused by bloated communication topologies, inefficient memory management, and redundant state reconstruction Verified Answer #1.

Structural Topology Optimization

Traditional multi-agent systems frequently utilize fully connected communication meshes where every agent receives the complete history of all other agents Verified Answer #1. This configuration results in exponential token growth Verified Answer #1.

Dynamic Topology Pruning

Dynamic Topology Pruning, also known as AgentDropout, replaces fixed meshes with sparse communication topologies Verified Answer #1. This method identifies and removes redundant agents or communication edges during specific interaction rounds Verified Answer #1. Implementing AgentDropout can reduce prompt token consumption by more than 21% and completion tokens by nearly 18% without compromising task performance Verified Answer #1.

Runtime Supervision

A modular SupervisorAgent framework can be used to implement runtime supervision Verified Answer #1. This framework employs a lightweight, non-LLM adaptive filter to intercept agent outputs at critical points Verified Answer #1. The filter proactively prunes redundant context, purifies observations, and corrects inefficient trajectories Verified Answer #1. On complex benchmarks such as GAIA, this approach has achieved an average token reduction of 29.68% Verified Answer #1.

Communication Protocol Efficiency

Natural language communication between agents is inherently inefficient because it is unstructured and prose-heavy Verified Answer #1.

State-Delta Encoding (SDE)

State-Delta Encoding (SDE) offers an alternative to prose-based handoffs by transferring the sequence of state transition trajectories Verified Answer #1. SDE enables agents to exchange compact, token-efficient representations of reasoning instead of full natural language transcripts Verified Answer #1.