Agent-to-Agent Negotiation in Airline Revenue Management
By Tony Wood, AXD Institute · Published 2026-03-01
What is Agent-to-Agent Negotiation in Airline Revenue Management | AXD Institute?
Agent-to-Agent Negotiation in Airline Revenue Management — an AXD Institute resource on agentic experience design, agentic commerce, trust architecture, and human agent interaction. Founded by Tony Wood..
How does AXD differ from traditional UX?
Why is trust architecture important for agentic AI?
Key concepts in Agent-to-Agent Negotiation in Airline Revenue Management | AXD Institute
Agentic Experience Design (AXD)
Trust architecture for autonomous AI
Delegation design patterns
Human agent interaction models
Agentic commerce and machine customers
Agency requires intentional delegation — every agentic system begins with a designed act of delegation
Trust is the primary material — AXD works in trust rather than attention
Absence is the primary use state — the most consequential experiences happen when no one is watching
Relationships have temporality — agentic experiences accumulate history over time
Outcomes replace outputs — AXD designers specify results, not interfaces
Dimension
Traditional UX
Agentic Experience Design (AXD)
Primary material
Attention and affordance
Trust and delegation
User state
Present, navigating
Absent, delegating
Design output
Screens and interfaces
Outcomes and constraints
Temporal model
Session-based
Relationship-based
Success metric
Task completion
Trust calibration
Frequently Asked Questions
What is agent-to-agent negotiation?
Agent-to-agent negotiation is a structured protocol where an airline's pricing agent and a traveller's booking agent engage in automated negotiation - exchanging constraints, preferences, and offers to reach optimal outcomes without human involvement in the transaction.
What is Know Your Agent (KYA)?
Know Your Agent (KYA) is a governance framework that verifies the identity, authority, and delegation scope of AI agents before accepting transactions - ensuring every machine-to-machine commerce interaction traces back to a legitimate human principal.
Key Takeaways
An airline redesigned its revenue management system to accommodate a world where an increasing proportion of ticket purchases were initiated by AI agents acting on behalf of travellers. These machine customers did not browse fare calendars or respond to urgency cues ('only 3 seats left'). They queried availability programmatically, compared across carriers simultaneously, and optimised for complex multi-dimensional mandates (price, timing, loyalty status, carbon footprint, connection risk). The airline's entire commercial architecture was built for human decision-making psychology. The machine customer rendered it irrelevant.