Multi-Agent Systems

A multi-agent system is an AI setup in which several specialized agents coordinate to complete a task no single agent handles end to end. An orchestrating agent breaks down the request, delegates steps to sub-agents, such as sourcing, screening, and scheduling in a recruitment flow, and assembles the results. Each sub-agent runs its own reason-act-observe loop with its own tools.

The hard problem in multi-agent systems is not the delegation but what travels with it. Context, identity, and permissions must pass from agent to agent so a sub-agent cannot do anything the original requester could not. This is where most implementations break, especially across system boundaries. Open standards such as Model Context Protocol (MCP) give agents a consistent way to expose and consume capabilities across systems. Darwinbox's named agents, such as the Recruiter Agent, are composites of coordinated sub-agents rather than single scripts.