multi-agent-orchestrator

Multi-agent orchestration automates the delegation of complex subtasks to specialized AI agents, running them concurrently to speed up workflows. It dynamically spawns agents with custom configurations and aggregates the parallel results into a coherent final output.

AI agent aggregation strategies include synthesis, concatenation, voting, and diffing. These methods allow you to compile and combine outputs from various specialized agents into a single coherent result, such as compiling multiple code review findings into one report.

Yes, the multi-agent delegation workflow requires the @anthropic-ai/sdk dependency to function. You also need the uuid package for managing dynamic agent spawning and session state, plus chalk for logging and debugging audit trails.

The best way to decompose large problems is to break them down into specialized subtasks and delegate them to expert agents. This enables agent specialization where a security analyst checks for vulnerabilities while a code specialist reviews performance simultaneously.

You can track session state and logs for concurrent AI agents through the orchestrator's built-in management system. It maintains comprehensive session state and detailed logs specifically designed for auditing and debugging complex, parallel multi-agent workflows.

You should avoid parallel AI agents for workflow automation when your task cannot be effectively decomposed into independent subtasks. If your workflow requires strictly sequential execution or lacks a clear aggregation strategy like synthesis or voting, a single agent approach is more suitable.