Amex GBT: the X-ray deck

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X-rayed by RiskMandate

Amex GBT

A travel management company whose agents now search, book and service business trips from inside Claude, Google Chat and Teams, on the traveler's own credentials.

Sources read on 2026-11-07 · web-summit.sgit.ai/xray/amex-gbt

What it does

Amex GBT

American Express Global Business Travel (Amex GBT) describes itself as a software and services company for travel, expense, and meetings and events; its products include Egencia, Neo and Complete (built with SAP Concur), and it was taken private by Long Lake, which calls itself an AI holding company, in a $6.3 billion deal completed on 29 September 2026. Its stated AI strategy has three parts: revolutionise the customer experience, power the agentic transformation of B2B travel, and reduce operating costs. In July 2026 it launched an agent-to-agent (A2A) layer that routes traveler requests to specialised agents, with the Egencia AI connector in Claude as its first deployment.

Sources: s9, s1, s3, s11, s12, s10, s2

Its AI agents

What acts on its own

Egencia AI in ClaudeA connector that lets a traveler, or an enterprise AI agent, ask Claude to review their calendar, draft itineraries and complete air and hotel bookings within company policy, with Claude acting on the traveler's behalf using their credentials. Announced 27 July 2026 for Egencia customers in Q3 2026.
  • Reads the traveler's calendar for the coming month
  • Drafts itineraries for upcoming activities
  • Searches, books and manages air and hotel end to end (transacts, not only retrieves)
  • Acts on the traveler's behalf using their credentials
  • Uses Model Context Protocol (MCP) to reach Amex GBT services
A2A orchestration layerThe proprietary agent-to-agent 'thinking layer': it interprets traveler requests, breaks them into tasks and routes each to specialised agents (flights, hotels, trip management), within each company's configured policies, traveler profiles and corporate agreements. Amex GBT says it is live in Egencia AI and across its servicing and booking products.
  • Interprets requests and splits them into tasks
  • Routes tasks to specialised flight, hotel and trip-management agents
  • Applies configured policies, traveler profiles and corporate agreements
  • Sources from the Amex GBT marketplace inventory
  • Feeds transaction data into reporting systems
Egencia AI chatConversational booking in collaboration tools: Egencia AI in Google Chat books and manages trips in one natural-language conversation with the option to reach a travel counselor (early adopters August 2026); a Neo assistant is in pilot in Microsoft Teams. The earnings remarks describe Egencia AI as searching, booking and changing travel while adhering to company policy.
  • Searches, books and changes trips in natural language
  • Works inside Google Chat and, for Neo, Microsoft Teams
  • Applies company policy and personal preferences
  • Connects the traveler to a live travel counselor

Use case · Egencia AI in Claude

Booking business trips from Claude

Reach
  • The traveler's calendar for the coming month data
  • The traveler's credentials: Claude acts on the traveler's behalf system
  • End-to-end air and hotel bookings: transactions, not only retrieval money
  • Company configured policies, traveler profiles and corporate agreements data
  • Amex GBT marketplace inventory of airlines and hotels outside
  • Transaction data fed into reporting systems data
Mandate (illustrative)
  • Illustrative: read calendar entries that imply business travel, to propose trips.
  • Illustrative: book air and hotel only within the company's configured policy, and only trips the traveler has confirmed.
  • Illustrative: no changes to the traveler's profile or account settings.
Gap
  • The connector acts with the traveler's credentials, so what it can do is whatever the traveler can do, while the request was only to help book confirmed trips.
  • Calendar access is the whole calendar, while the task needs only the entries that imply travel.

Use case · Egencia virtual agent

Servicing chat with handoff to counselors

Reach
  • Help-centre content the virtual agent is trained on data
  • Traveler account actions: password resets, adding frequent flyer numbers system
  • Traveler invoices and itineraries data
  • Live travel counselors who join or take over the conversation people
  • Changing booked travel through Egencia AI money
Mandate (illustrative)
  • Illustrative: answer common questions from help-centre content.
  • Illustrative: account self-service and invoice lookup only for the signed-in traveler.
  • Illustrative: changes to booked travel go to a counselor or are confirmed by the traveler.
Gap
  • Account changes and invoice retrieval happen in chat; how the agent ties each request to the signed-in traveler is not publicly documented.
  • Egencia AI can change travel, so a servicing chat reaches money as well as answers.

Use case · Hotel Lobby

Hotel sourcing with Hotel Lobby

Reach
  • Client hotel sourcing data, cleansed by the agents data
  • Negotiated-rate benchmarks across thousands of properties and supplier performance from more than $5 billion of managed hotel spend data
  • Compliance trends across Amex GBT's client portfolio data
  • Wish (target) rates that set the negotiating position money
  • RFPs to hotels, validated by the agents outside
Mandate (illustrative)
  • Illustrative: prepare and validate one client's sourcing data and RFPs.
  • Consultants keep responsibility for strategy, negotiations and exceptions (publicly stated).
Gap
  • Agents working for one client draw on benchmarks and compliance trends from across Amex GBT's whole client portfolio.
  • Wish rates the agents calculate set the starting point of a negotiation the consultant owns.

The simulation · Booking business trips from Claude

Each action, placed against the mandate

  1. Traveler asks Claude to review next month's calendar and draft itineraries in the mandate
  2. Reads every calendar entry, including ones with no travel in them (illustrative) in the gap
    The record: RiskMandate's record shows the whole calendar as reachable data and a mandate that covers only entries implying travel; the read of the rest sits outside it.
  3. A2A layer splits the request into flight and hotel tasks and routes them to specialised agents in the mandate
  4. Checks options against company policy and the traveler profile in the mandate
  5. Books air and hotel for a meeting the traveler marked tentative, not confirmed (illustrative) in the gap
    The record: RiskMandate's record shows a completed transaction on the traveler's credentials for a trip the mandate did not cover, and that the policy check passed because the booking was in policy, not because it was confirmed.
  6. Using the traveler's credentials, updates a loyalty number in the traveler's profile to fit the booking (illustrative) in the gap
    The record: RiskMandate's record shows that the credentials the connector holds reach the traveler's profile as well as booking, and that this change was outside the mandate.
  7. Transaction data flows into the company's reporting in the mandate

What RiskMandate gives

RiskMandate is never in the request path: read-only by design.

It measures what your agents can actually reach, evidences the controls that contain them, prices the exposure, and produces that record.

riskmandate.ai/llms.txt

Sources

Every fact, with its source and the date read

And 9 more on the X-ray page.

The questions we would ask

We have not asked them yet

  1. When the Egencia AI connector acts with a traveler's credentials, what can those credentials reach beyond booking, and can a company restrict the connector to search only or require a confirm step before purchase?
  2. In the A2A layer, is there a record per action of which specialised agent did what, on whose authority, that a customer can export for audit?
  3. How does the chat agent tie a password reset, loyalty change or invoice request to the signed-in traveler?
  4. In Hotel Lobby, how is benchmark and compliance data from other clients separated from the client being sourced?
  5. Under Long Lake, will client travel data be used to improve the Nexus platform shared across Long Lake companies?

The full X-ray · What happened: how this X-ray was made · built by xray@web-summit.dinis

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