Orchestrating Multi‑Agent AI for Airline Seat Reassignment with KaibanJS

Dariel Vila

darielnoel

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Introduction: The Business Challenge

Airlines frequently face last-minute seat availability changes due to cancellations or no-shows especially in premium cabins. These situations pose both a revenue opportunity and a customer experience challenge. Automating the seat reassignment process with a coordinated multi-agent AI system can unlock real-time upselling potential and smoother operations while respecting regulations like ADA compliance.

Why Multi‑Agent AI Matters

Complex workflows such as seat monitoring, passenger matching, messaging, staff alerts, and decision tracking are better handled by specialized AI agents rather than a monolithic model. A multi-agent approach enables each component to focus on a defined role, mirroring how human operational teams effectively manage complex tasks.

KaibanJS: The React‑Powered Agentic Kanban Framework

KaibanJS is an open-source JavaScript framework designed for building and managing multi-agent systems in real time. Key features include:

• Kaiban Board: It functions like Trello or Jira, but for AI agents, showing tasks moving through stages in real time. You define Agents, Tasks, and Teams, and KaibanJS manages the state and orchestration behind the scenes.
• Reactive, Redux-inspired state management: Ensures transparent and synchronized UI and logic for agent/task workflows.
• Seamless integration with React and other JS frameworks, enabling developers to embed AI agents into existing frontends or backends (React, Vue, NextJS, NodeJS).
• Built-in React Agentic Loop: Under the hood, KaibanJS uses a React Agentic Loop with robust state management (inspired by Redux), enabling reactive workflows and dynamic UI updates. This architecture ensures UI + logic remain synchronized and developers have full visibility into agent behavior.

Code Example: Agents, Tasks, and Team Orchestration

Passenger Matching Agent

Responsible for matching available premium seats with eligible passengers:

const passengerMatchingAgent = new Agent({
  name: 'Passenger Matching Agent',
  role: 'Customer Segmentation Specialist',
  goal: 'Match eligible passengers based on loyalty tier, profile preferences, fare class or upsell potential while ensuring UNIQUE seat assignments.',
  background: 'Senior customer analytics specialist with deep expertise ... CRITICAL: Each seat can only be assigned to ONE passenger ...',
  tools: [ new PassengerMatchingTool() ]
});

Matching Task (matchEligiblePassengersTask)

Defines constraints and expected outcomes:

const matchEligiblePassengersTask = new Task({
  title: 'Passenger Matching',
  description: `Match eligible passengers for the available premium seats based on:
    - Loyalty tier (PLATINUM > GOLD > SILVER)
    - Fare class and upgrade eligibility
    - Seat preferences (WINDOW/AISLE)
    - Upsell potential and revenue optimization
    - Special assistance requirements
    - Historical upgrade acceptance patterns

    CRITICAL ASSIGNMENT RULES:
    - Each seat can ONLY be assigned to ONE passenger
    - Never assign the same seat number to multiple passengers
    - Process passengers in priority order: PLATINUM first, then GOLD, then SILVER
    - Ensure all seat assignments are unique across all passengers

    Include passenger names and email addresses in all outputs for identification.
    Before finalizing, verify that no seat number appears more than once in the assignments.`,
  expectedOutput: 'Ranked list of eligible passengers matchingResults with UNIQUE seat assignments...',
  agent: passengerMatchingAgent
});

Multi‑Agent Team Definition

Orchestrates the workflow across agents and tasks:

const team = new Team({
  name: 'Airline Seat Reassignment Team',
  agents: [
    seatMonitoringAgent,
    passengerMatchingAgent,
    messageGenerationAgent,
    staffAlertAgent,
    decisionTrackingAgent
  ],
  tasks: [
    monitorSeatChangesTask,
    matchEligiblePassengersTask,
    generateMessagesTask,
    alertStaffTask,
    trackDecisionsTask
  ],
  env: { /* environment variables like API keys */ }
});

team.start();

Visual Workflow with the Kaiban Board

Once launched, the Kaiban Board presents a dynamic Kanban-style view of your AI workflow:

• Tasks appear as cards that move through columns like To Do, Doing, and Done.
• Inspect task specifics—agent roles, progress, logs, outputs—in real time.
• Human validation checkpoints (HITL) can pause workflows for review, feedback, or ethical oversight before proceeding.
• Ideal for troubleshooting, collaboration, and ensuring transparency across technical and non-technical stakeholders.

From Airlines to Broader Applications

The multi-agent orchestration pattern is widely applicable:

• Hotel dynamic upgrades and room management
• Healthcare patient scheduling and triage workflows
• Logistics and delivery reallocation optimization

KaibanJS enables complex AI coordination with visibility and control, wherever dynamic decision-making is required.

Conclusion

KaibanJS delivers:

• Specialized AI agents tailored to distinct tasks
• Task-driven orchestration with built-in constraints
• A reactive UI with Kanban-style visualization via the Kaiban Board
• Native Human‑in‑the‑Loop support for oversight and compliance

It's a JavaScript-first framework empowering developers to build transparent, scalable, and manageable multi-agent AI systems.

Ready to explore?
Dive into the full working example here: Multi‑Agent Seat Reassignment