Multi-Agent AI Architecture: How It Works

Three dominant patterns exist: (1) Hub-and-Spoke — a triage agent routes to specialists (used by CallSphere's salon system with 4 agents: Triage → Booking, Inquiry, Reschedule), (2) Hierarchical — nested agent trees where specialists can sub-delegate (used by CallSphere's real estate system with 10 agents including sub-specialist calculators), (3) Pipeline — sequential processing where each agent enriches context (used in CallSphere's after-hours escalation with 7 agents: EmailTriage → VoicemailAnalyzer → HeadAgent → VoiceAgent → SmsAgent → AckMonitor). The optimal pattern depends on whether tasks are parallel (hub-and-spoke), nested (hierarchical), or sequential (pipeline).

Each specialist agent has its own tool set. CallSphere's healthcare agent has 14 tools (lookup_patient, schedule_appointment, get_insurance, etc.). The real estate platform has 30+ tools across property search, suburb intelligence, financial calculators, and tenant management. Tools are defined as function schemas that the LLM can invoke mid-conversation. The key design decision is tool scope — each agent should only have access to tools relevant to its specialty, reducing hallucination risk and improving response quality.

Agent handoffs transfer conversation control from one agent to another. CallSphere uses the OpenAI Agents SDK's native handoff mechanism, which preserves full conversation history across transfers. The triage agent classifies intent and initiates handoff to the appropriate specialist. Handoffs can be explicit (triage detects 'I want to book an appointment' and routes to booking agent) or implicit (booking agent detects insurance question and routes to insurance verification). Critical design rule: always transfer with context summary to reduce re-questioning.

Key lessons from 6 production multi-agent systems: (1) Triage accuracy is everything — if the triage agent misroutes, the user experience degrades, so invest heavily in triage prompt engineering, (2) Tool failures need graceful handling — if a database query fails, the agent should acknowledge and offer alternatives, not hallucinate data, (3) Monitor per-agent metrics — track latency, accuracy, and handoff rates per specialist to identify bottlenecks, (4) Keep agent count minimal — only add agents when a single agent's prompt exceeds useful context or when tools conflict. CallSphere's salon system works perfectly with 4 agents; adding more would only add latency.