multi-agent-patterns

Overcoming single-agent context limitations requires distributing complex workflows across specialized sub-agents using multi-agent architectures. This approach partitions work into lean, focused context windows, preventing context degradation and reasoning bottlenecks during parallel subtask execution.

Structuring agent orchestration for parallel subtask execution is best achieved using supervisor, swarm, or hierarchical multi-agent patterns. These architectures utilize explicit handoff mechanisms and consensus strategies to manage robust inter-agent coordination and prevent error propagation.

Context isolation improves multi-agent system accuracy by partitioning work to ensure each agent operates with a lean, focused context window. This targeted context management prevents reasoning bottlenecks and stops context degradation across specialized agent roles.

Use hierarchical or swarm coordination protocols based on your task decomposition needs for scalable agent systems. Hierarchical patterns manage top-down specialized agent roles, while swarm patterns facilitate decentralized inter-agent coordination and explicit handoffs.

Limitations of using a single agent for complex task decomposition include context degradation and reasoning bottlenecks from exceeding context window capacity. Multi-agent architectures solve this by distributing tasks across specialized sub-agents with isolated contexts.