Why teams split the agent
One agent with thirty tools and a page of instructions degrades: tool choice gets noisy, context fills with irrelevant capability, and one prompt must explain every job. Splitting restores focus — each agent carries the few tools and the narrow brief its role needs — and unlocks parallelism where subtasks are independent. The standard shapes are a supervisor routing work to specialists, a pipeline where each stage hands to the next, and peer collaboration where agents critique each other's output, the pattern frameworks like CrewAI package as crews and [LangGraph](/compare/langgraph-vs-crewai) expresses as graphs.
Coordination is the real engineering
The model does not get smarter when you add agents; the system gets more articulate about its workflow. What you are actually building is the connective tissue: how state passes between agents, who decides a subtask is done, what happens when two agents disagree, and how the whole run terminates. Teams that skip those decisions get the multi-agent failure classics — circular delegation, lost context at hand-offs, two agents politely deferring to each other forever. Every coordination rule you would write for people doing this workflow needs a machine-readable equivalent.
The estate-management view
Operationally, a multi-agent system is several agents — and the readiness obligations multiply accordingly. Each member needs [its own identity and scoped credentials](/guides/secure-agentic-ai) (a shared token across the crew recreates the shared-service-account mistake at higher speed), the inter-agent hand-offs need tracing if anyone is to reconstruct a run, and injection acquires a new property: content that steers one agent can propagate through the hand-offs to misuse another's tools. Govern the system as one unit with several actors inside it — one registry entry for the workflow, per-agent identities within — and apply the [autonomy dial](/learn/autonomous-ai-agents) to the system's external writes, not to each internal exchange.