// Airline Agent Experience

Scans pass.
Agents still fail.

Today, AI agents are browsing, comparing, and buying for your customers. We send real browser agents on real customer journeys and check the only thing that matters: can they actually complete their tasks?

See the benchmark → How we test

// Benchmark — 7 airlines tested

Who completes the task

# Airline Completed / measurable Blocked Top failing signal
1 Riyadh Air 0 / 15 1 Failure transparency
2 easyJet 0 / 2 — Discoverability
3 Emirates 0 / 2 1 Input affordances
4 Lufthansa 0 / 2 1 Input affordances
5 airBaltic 0 / 1 — Unattributed non-completion
6 Delta 0 / 1 1 Unattributed non-completion
7 Qatar Airways 0 / 1 — Input affordances

Ordered by measurable-run coverage, not completion rate — every airline in this sample completed zero measurable runs. Riyadh Air accounts for 15 of the 24 measurable runs; the other 6 carriers contribute one or two tasks each. 2 additional runs failed on infrastructure grounds and are correctly excluded, not counted as fails.

Methodological information
Measurable completion rate
0%

0 of 24 completed

Measurable runs
24

16 distinct tasks across 7 airlines

Blocked
4

4 of 28 runs

Airlines tested
7

airlines in this sweep

A spot-check, not an industry sweep — concentrated on one carrier. This is 7 airlines, 16 distinct tasks — Qatar Airways, Lufthansa, Riyadh Air, airBaltic, easyJet, Emirates, Delta. A mix of agent models drove — claude-sonnet-4-6 (15), kimi-k2.5:cloud (10), unattributed (6), run 2026-07 to 2026-08.

Top failure signals across the measurable sample: failureTransparency (11) · inputAffordances (5) · discoverability (4) · taskCompletion (4)

Blocked is not failure. A booking-reference lookup that correctly asks for a booking reference is a site working as designed. 4 of 28 airline runs hit an honest access wall — a login, PNR, or payment gate the agent hit as expected. Blocked runs are excluded from the Index — never counted as a completion, never counted against the site.

// The loop

Assess. Diagnose. Optimize.

01 — Assess Run the benchmark Run the test suite against your own website and see where an agent actually completes the job — the full Index, task by task. Open the full Index
02 — Diagnose Read the failure signals Per-run failure signals name what broke — discoverability, understanding, interaction — not just complete or fail.
03 — Optimize Apply the playbook Fixes grouped by the dimension they repair — discoverability, understanding, interaction — and sequenced by what has to be fixed first. Read the playbook
Playground Try it in the playground — Friction Airways, a test airline we own. Open the playground

Takeaway_01

Zero of 7 airlines completed a measurable task.

Takeaway_02

Failure transparency is the top failure signal — 11 of 24 measurable runs went dark instead of erroring legibly.

Takeaway_03

The controlled site we own goes from 0% to 100% — hostile to ready, same tasks, same check.

// WebMCP case study

Judge WebMCP by the outcome it produces

Friction Airways is a controlled airline site we own, published in three WebMCP-readiness profiles. Same site, same 21-task measurable slice — only the profile changes.

Hostile
0% · 0/21
Median
71% · 15/21
Ready
100% · 21/21

Hostile's fails cluster on one signal: 18 of 21 measurable fails returned no machine-readable answer at all; ready has zero measurable fails. This is the assess → diagnose → optimize → retest loop in miniature, run on one controlled site, not an airline-industry claim.