multi-agent-coordination

You can coordinate multiple AI agents by applying architectures like Supervisor or Peer-to-Peer to decompose complex tasks. This approach enforces context isolation, distributing long-context workloads across independent agents to stay within limits while preserving diverse perspectives.

The forward_message protocol is a communication strategy used in multi-agent orchestration to preserve fidelity. It ensures that information passed between agents maintains its accuracy and structure, preventing fidelity loss during complex task decomposition and evaluation.

Hierarchical coordination architectures are used when tasks require structured authority boundaries and deterministic decision trails. They fit complex planning scenarios where independent agent evaluation improves outcomes and prevents bottlenecks by structuring roles clearly.

Set up a supervisor architecture by creating a three-agent coordination plan where a central supervisor decomposes the complex planning task. Route outputs from sub-agents to the central evaluator to ensure independent evaluation and maintain quality assurance.

Multi-agent coordination provides guidance on token economics and resource costs for multi-agent setups. It helps manage efficiency by distributing workloads and allowing you to replace or simplify agents for domain-specific needs to optimize resource usage.

Peer-to-peer agent coordination lacks the centralized authority boundaries of a supervisor model, which can complicate deterministic decision trails. If your task requires strict quality assurance and central evaluation, a supervisor architecture prevents bottlenecks better.