Build sophisticated AI systems where multiple specialized agents collaborate to solve complex problems—from property management to marketplace operations.
Single agents have limitations. Multi-agent systems overcome these by enabling specialization, parallel processing, and complex decision-making that mirrors how successful organizations operate.
Each agent excels at specific tasks—pricing, communication, scheduling—rather than being mediocre at everything.
Multiple agents work simultaneously—analyzing data, generating content, and processing requests in parallel.
Coordinator agents can break down complex problems and delegate to specialists, then synthesize results.
Add new capabilities by adding agents, not by retraining or complicating existing ones.
┌─────────────────────────────────────────────────────────────┐
│ COORDINATOR AGENT │
│ (Understands goals, delegates, synthesizes) │
└─────────────────────────┬───────────────────────────────────┘
│
┌───────────────┼───────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ SPECIALIST │ │ SPECIALIST │ │ SPECIALIST │
│ AGENT A │ │ AGENT B │ │ AGENT C │
│ (Pricing) │ │ (Content) │ │ (Analysis) │
└────────┬────────┘ └────────┬────────┘ └────────┬────────┘
│ │ │
└───────────────────┼───────────────────┘
│
▼
┌──────────────────────────┐
│ SHARED CONTEXT │
│ (Memory, State, Data) │
└──────────────────────────┘The orchestrators that understand high-level goals, break them into tasks, delegate to specialists, and synthesize results into coherent outputs.
type: "coordinator"Focused experts that excel at specific domains—pricing optimization, content generation, market analysis, customer communication.
type: "specialist"The memory and state layer that allows agents to share information, maintain consistency, and build on each other's work.
SharedContextManagerRoutes tasks to the most appropriate agent based on capabilities, current load, and task requirements.
TaskDelegatorHere's how a multi-agent system handles a new booking inquiry in a property management scenario:
"Hi, I'm interested in your beachfront property for July 15-22. Do you offer any discounts for week-long stays?"
Coordinator identifies this requires: availability check, pricing calculation, and personalized response. Delegates to three specialists in parallel.
Availability Agent: Checks calendar, confirms dates available
Pricing Agent: Calculates base rate, applies 15% weekly discount, checks competitor rates
Communication Agent: Drafts personalized response based on guest profile
Combines results into a cohesive response with availability confirmation, pricing breakdown, and personalized message.
If discount exceeds threshold or guest is flagged, routes to human for approval before sending. Otherwise, auto-approves and responds.
Result: Guest receives a comprehensive, personalized response in under 30 seconds—what would take a human 10-15 minutes of research and typing.
import { executeMultiAgentTask } from "@/lib/agents/multi-agent-executor"
import { SharedContextManager } from "@/lib/agents/shared-context-manager"
import { TaskDelegator } from "@/lib/agents/task-delegator"
// Initialize shared context for this session
const context = new SharedContextManager({
sessionId: "booking-inquiry-123",
userId: "user_abc",
propertyId: "prop_xyz"
})
// Define the task
const task = {
type: "booking_inquiry",
input: {
message: guestMessage,
guestId: "guest_456",
requestedDates: { start: "2024-07-15", end: "2024-07-22" }
},
requiredCapabilities: ["availability", "pricing", "communication"]
}
// Execute with multi-agent coordination
const result = await executeMultiAgentTask({
task,
context,
coordinatorId: "coordinator-pms-001",
options: {
parallel: true, // Run specialists in parallel
timeout: 30000, // 30 second timeout
hitlRequired: false, // Auto-determined based on risk
maxDelegations: 5 // Limit delegation depth
}
})
// Result includes synthesized output and execution trace
console.log(result.output) // Final response
console.log(result.agentsUsed) // ["coordinator", "availability", "pricing", "comms"]
console.log(result.executionTime) // 2.3 seconds
console.log(result.hitlTriggered) // falseBegin with 2-3 agents, then expand as you understand the interactions
Each agent should have a well-defined scope—avoid overlap
Design your context structure before building agents
Log all delegations, decisions, and outputs for debugging
If a specialist fails, the coordinator should handle it gracefully
Always have HITL checkpoints for high-stakes decisions