Checksum is now a Google Partner
・
Checksum AI and Google Cloud: End-to-End Testing AI Innovation
Checksum is now
a Google Cloud Partner
Checksum is now a Google Partner
・
Checksum AI and Google Cloud: End-to-End Testing AI Innovation
The Full Report
of QA Benchmarks
What Actually Breaks in Web Automation and How AI Fixes It
Reliable QA testing benchmarks based on 1M+ real production runs across hundreds of customer applications.
Most web agent benchmarks say AI isn’t ready for production. Real companies tell a very different story.
This report explains why current benchmarks measure the wrong things and what real reliability looks like in QA testing and web automation.
The Full Report
of QA Benchmarks
What Actually Breaks in Web Automation and How AI Fixes It
Reliable QA testing benchmarks based on 1M+ real production runs across hundreds of customer applications.
Most web agent benchmarks say AI isn’t ready for production. Real companies tell a very different story.
This report explains why current benchmarks measure the wrong things and what real reliability looks like in QA testing and web automation.
Why Web Agent Benchmarks Are
Misleading
Public benchmarks focus on one-shot task completion and raw model accuracy.
But production automation isn’t about whether something works once. It’s about whether it keeps working over weeks and months.
In real systems:
Workflows span dozens of steps
UI changes constantly
Failures must be detected, diagnosed,
and repaired
Deterministic code remains the
source of truth
Reliability is not about whether an agent succeeds once. It's about whether a workflow stays operational over weeks and months or as long as the web page and platform exists!
What Actually
Breaks in Production
We analyzed over one million end-to-end automation runs across hundreds of production web applications.
1M+
real production runs
Failures are surprisingly predictable:
32%
selector changes
27%
flow changes
22%
environment instability
19%
loading
and timing issues
Most failures happen in layers AI can understand and fix: selectors, DOM structure, and flow logic.
The Hidden Cost of Broken Automation
Automation maintenance quietly consumes engineering time:
A team with 500 tests spends $1.7M+ annually
Equivalent to 1+ full-time engineer
Releases are delayed
Trust in automation erodes
Coverage stagnates or declines
And most of that time is spent not fixing, but investigating what broke.
The Hidden Cost of Broken Automation
Automation maintenance quietly consumes engineering time:
A team with 500 tests spends $1.7M+ annually
Equivalent to 1+ full-time engineer
Releases are delayed
Trust in automation erodes
Coverage stagnates or declines
And most of that time is spent not fixing, but investigating what broke.
What Changes When AI Maintains Your
With AI-driven recovery and auto-healing:
~70%
Failures resolve autonomously
~30%
Need only quick human review
x10 faster
Time per failure drops from hours
to minutes
80%
Reduction in failure rates
The result: stable automation that survives UI changes instead of breaking every week.
What You’ll Get
in the Full PDF
Why popular web agent benchmarks fail to predict production success
Real failure distributions across test complexity levels
Where engineers actually spend their time during failures
Why deterministic code + AI maintenance beats pure agent approaches
The compound action problem (why 85% accuracy isn’t enough)
Median repair times by failure type
Autonomous recovery vs auto-healing success rates
What better benchmarks should measure instead
This report is based entirely on real customer data, not synthetic evaluations.
Who Should Read This
QA engineers tired of flaky tests
Engineering managers owning CI reliability
Platform and infra teams supporting automation at scale
Anyone evaluating AI agents for real production workflows
If you care about reliable automation, this report is for you.
The Full Report
of QA Benchmarks
What Actually Breaks in Web Automation and How AI Fixes It
Reliable QA testing benchmarks based on 1M+ real production runs across hundreds of customer applications.
Most web agent benchmarks say AI isn’t ready for production. Real companies tell a very different story.
This report explains why current benchmarks measure the wrong things and what real reliability looks like in QA testing and web automation.
The Full Report
of QA Benchmarks
What Actually Breaks in Web Automation and How AI Fixes It
Reliable QA testing benchmarks based on 1M+ real production runs across hundreds of customer applications.
Most web agent benchmarks say AI isn’t ready for production. Real companies tell a very different story.
This report explains why current benchmarks measure the wrong things and what real reliability looks like in QA testing and web automation.
Why Web Agent Benchmarks
Are Misleading
Public benchmarks focus on one-shot task completion and raw model accuracy.
But production automation isn’t about whether something works once. It’s about
whether it keeps working over weeks and months.
In real systems:
Workflows span dozens of steps
UI changes constantly
Failures must be detected, diagnosed, and repaired
Deterministic code remains the source of truth
Reliability is not about whether an agent succeeds once. It's about whether a workflow stays operational over weeks and months or as long as the web page and platform exists!
What Actually
Breaks in Production
We analyzed over one million end-to-end automation runs across hundreds of production web applications.
1M+
real production runs
Failures are surprisingly predictable:
32%
selector changes
27%
flow changes
22%
environment instability
19%
loading
and timing issues
Most failures happen in layers AI can understand and fix: selectors, DOM structure, and flow logic.
The Hidden Cost of Broken Automation
Automation maintenance quietly consumes engineering time:
A team with 500 tests spends $1.7M+ annually
Equivalent to 1+ full-time engineer
Releases are delayed
Trust in automation erodes
Coverage stagnates or declines
And most of that time is spent not fixing, but investigating what broke.
The Hidden Cost of Broken Automation
Automation maintenance quietly consumes engineering time:
A team with 500 tests spends $1.7M+ annually
Equivalent to 1+ full-time engineer
Releases are delayed
Trust in automation erodes
Coverage stagnates or declines
And most of that time is spent not fixing, but investigating what broke.
What Changes When AI Maintains Your
With AI-driven recovery and auto-healing:
~70%
Failures resolve
autonomously
~30%
Need only quick human review
x10 faster
Time per failure drops
from hours to minutes
80%
Reduction
in failure rates
The result: stable automation that survives UI changes instead of breaking every week.
What You’ll Get in the Full PDF
Why popular web agent benchmarks fail to predict production success
Real failure distributions across test complexity levels
Where engineers actually spend their time during failures
Why deterministic code + AI maintenance beats pure agent approaches
The compound action problem (why 85% accuracy isn’t enough)
Median repair times by failure type
Autonomous recovery vs auto-healing success rates
What better benchmarks should measure instead
This report is based entirely on real customer data, not synthetic evaluations.
Who Should Read This
QA engineers tired of flaky tests
Engineering managers owning CI reliability
Platform and infra teams supporting automation at scale
Anyone evaluating AI agents for real production workflows
If you care about reliable automation, this report is for you.
The Full QA Benchmark Report
What Actually Breaks in Web Automation and How AI Fixes It
Reliable QA testing benchmarks based on 1M+ real production runs across hundreds of customer applications.
Most web agent benchmarks say AI isn’t ready for production. Real companies tell a very different story.
This report explains why current benchmarks measure the wrong things and what real reliability looks like in QA testing and web automation.
Why Web Agent Benchmarks Are Misleading
Public benchmarks focus on one-shot task completion and raw model accuracy.
But production automation isn’t about whether something works once. It’s about whether it keeps working over weeks and months.
In real systems:
Workflows span dozens of steps
UI changes constantly
Failures must be detected, diagnosed, and repaired
Deterministic code remains the source of truth
Reliability is not about whether an agent succeeds once. It's about whether a workflow stays operational over weeks and months or as long as the web page and platform exists!
What Actually
Breaks in Production
We analyzed over one million end-to-end automation runs across hundreds of production web applications.
1M+
real production runs
Failures are surprisingly predictable:
32%
selector changes
27%
flow changes
22%
environment instability
19%
loading and timing issues
Most failures happen in layers AI can understand and fix: selectors, DOM structure, and flow logic.
The Hidden Cost of Broken Automation
Automation maintenance quietly consumes engineering time:
A team with 500 tests spends $1.7M+ annually
Equivalent to 1+ full-time engineer
Releases are delayed
Trust in automation erodes
Coverage stagnates or declines
And most of that time is spent not fixing, but investigating what broke.
What Changes When AI Maintains Your
With AI-driven recovery and auto-healing:
~70%
Failures resolve autonomously
~30%
Need only quick human review
x10 faster
Time per failure drops from hours to minutes
80%
Reduction
in failure rates
The result: stable automation that survives UI changes instead of breaking every week.
What You’ll Get in the Full PDF
Why popular web agent benchmarks fail to predict production success
The compound action problem (why 85% accuracy isn’t enough)
Real failure distributions across test complexity levels
Median repair times by failure type
Where engineers actually spend their time during failures
Autonomous recovery vs auto-healing success rates
Why deterministic code + AI maintenance beats pure agent approaches
What better benchmarks should measure instead
This report is based entirely on real customer data, not synthetic evaluations.
Who Should Read This
QA engineers tired
of flaky tests
Engineering managers owning CI reliability
Platform and infra teams supporting automation at scale
Anyone evaluating AI agents for real production workflows
If you care about reliable automation this report is for you.
