Vercel AI SDK Multi-Agent Orchestration

Often not, and we will tell you when a single well-designed agent with good tools would be cheaper, faster, and far easier to debug. Multi-agent architectures genuinely earn their complexity when sub-tasks need isolated context, when work fans out and can run in parallel, or when different steps need different models or permissions. When the real motivation is that the single agent's prompt got unwieldy, splitting it usually multiplies the problem rather than solving it.

Hard limits, set at design time rather than discovered in production. Every agent runs under a maximum step count and a token or cost ceiling, handoffs are acyclic by default with any cycle explicitly justified and bounded, and the orchestrator enforces a global budget for the whole run. We also instrument per-agent cost from the first commit so a runaway shows up on a dashboard rather than on an invoice.

With tracing that records every agent's inputs, tool calls, outputs, and handoffs as a single correlated trace. Without that, diagnosing why a five-agent system produced a bad answer is guesswork, and this is the most common reason these projects stall. We treat observability as part of the build rather than something added after the first incident.

A production system typically takes five to six weeks. The variable is not the agent code, which is usually straightforward, but the evaluation work needed to trust the handoffs. We quote against a defined topology after the first week of decomposition, once we know whether the problem genuinely needs the architecture.