Google Cloud Travel and Hospitality Malaysia

AirAsia Next

IT Operations Automation

AirAsia Next needed to accelerate software delivery and quality assurance for millions of regional customers without adding manual testing bottlenecks or slowing its engineering teams.

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Generates 50%+ production code using Google Antigravity in Gemini Enterprise

Today, more than 50% of AirAsia Next’s production-ready code is generated through these intelligent agentic workflows.

Reported impact

Business outcome

AirAsia Next now generates more than 50% of its production-ready code through agentic workflows, automates QA testing, and optimizes serverless pipelines so teams can deliver reliable digital experiences faster.

Solution

AirAsia Next extended its long-running Google Cloud estate with Google Antigravity and Gemini Enterprise Agent Platform to modernize software engineering and QA across the AirAsia group. The Google Antigravity agentic browser is used to simulate real user environments and run automated quality assurance testing, removing manual testing bottlenecks before releases. Google Antigravity agent manager and Gemini Enterprise Agent Platform coordinate agentic workflows that automate complex coding tasks and generate production-ready code. The resulting changes move through serverless delivery pipelines on Cloud Run, with Cloud Logging providing operational visibility and BigQuery remaining part of the company's data architecture. This setup let AirAsia Next scale governed agentic development workflows while keeping software delivery fast and reliable.

Data flow

Engineering teams use Google Antigravity and Gemini Enterprise Agent Platform to generate and refine code, validate it with the agentic browser, and push the resulting changes through Cloud Run pipelines while Cloud Logging tracks operations and BigQuery remains the surrounding data foundation.

Solution architecture

7 components · 5 layers

Compute
- Gemini Enterprise Agent Platform Supplies the AI platform used to generate code and support intelligent agent workflows.
- Cloud Run Runs the serverless pipelines where AirAsia Next deploys and operates its application changes.
Storage
- BigQuery Provides the underlying data architecture that supports AirAsia Next's broader digital platform.
Serving
- Google Antigravity agentic browser Simulates real-world user environments so teams can run automated QA testing.
Orchestration
- Google Antigravity Provides the agentic development environment AirAsia Next uses to streamline engineering workflows.
- Google Antigravity agent manager Coordinates the agentic workflows that automate coding tasks across the development pipeline.
Governance
- Cloud Logging Monitors the serverless delivery pipelines and captures operational telemetry.

Architecture clues

AirAsia Next references its BigQuery data architecture as part of the broader platform.
AirAsia was already a decade into its Google Cloud journey before adopting Antigravity.
QA automation is performed with an agentic browser that simulates real-world user environments.
Serverless pipelines run on Cloud Run and are monitored with Cloud Logging.

Evidence from the source

AirAsia Next also leverages the Google Antigravity agent manager alongside Gemini Enterprise Agent Platform to streamline its end-to-end development pipeline.

Generates 50%+ production code using Google Antigravity in Gemini Enterprise

This step-change in developer productivity directly improves core business metrics while empowering engineering teams to focus on high-impact innovation across serverless pipelines using Cloud Run, alongside their BigQuery data architecture.

Today, more than 50% of AirAsia Next’s production-ready code is generated through these intelligent agentic workflows.

Using the Google Antigravity agentic browser, engineering teams simulate real-world user environments to run thorough, automated quality assurance testing.

Transparency Evidence & methodology 100/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
2 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence79%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
5 direct source quotes retained.
Date provenance0/10
The publication date has no documented provenance.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Google Cloud
AirAsia Next customer story

First-party
https://cloud.google.com/customers/airasianext

Source type
Vendor-published customer story
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes

Date evidence

Sitemap last modified Aug 10, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
Open date source
Unverified extracted date Aug 10, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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