One of the most significant bottlenecks in software delivery: manual User Acceptance Testing UAT is slow, labor-intensive, and often fails to catch critical bugs or verify every acceptance criterion before release.

Founders, Product Managers, and their teams spend well over 100 hours each month on manual acceptance testing, yet major issues still slip through – especially in regulated industries like fintech, insurance, and healthcare, UAT cycles can drag on for weeks or even months, with real consequences when things go wrong.

In one notable 2015 incident, a software bug forced Western Union to halt its Spain operations for six weeks, underscoring the high stakes of undetected issues.

Time-Consuming UAT with Missed Bugs

Software teams have long struggled with the time and complexity of manual UAT. Before each release, Founders, Product Managers, QAs and cross functional teams methodically click through new features to ensure everything works as expected – a process that often takes days or weeks per release. This translates to dozens of person-hours per week (often 100+ hours per team per month) dedicated to UAT, pulling staff away from other critical work. Despite this heavy investment of time, manual testing still misses many issues.

Recent analysis of over 100,000 GitHub issue reports (2021–2024) found that 61.3% were non-crash functional bugs, and nearly half of those were labeled “important” – bugs that impact functionality or user experience but aren’t outright crashes. In other words, the majority of significant bugs are subtle functional problems that manual UAT frequently fails to catch. This gap between effort and outcome leaves organizations vulnerable. For example, a single overlooked UAT bug in a financial system can wreak havoc – as seen when a software miss in Western Union’s systems shut down its Spanish operations for six weeks in 2015, causing massive disruptions.

Current behavior to mitigate this risk is painfully inefficient. Teams in high-stakes sectors often extend UAT cycles or add more reviewers in hopes of catching every issue, slowing down releases to a crawl. It’s not uncommon for enterprises to delay a launch by months solely due to protracted manual UAT. Yet even these drawn-out processes can leave acceptance criteria only partially verified, especially when requirements are complex or involve multiple systems. The outcome is a lose-lose: slow time-to-market and still the chance of critical bugs in production (over 61% of serious bugs appear to be functional issues that weren’t caught pre-release). This status quo is unsustainable for modern software organizations, particularly those that must meet strict regulatory or quality standards.

Shortcomings of Existing Testing Methods

Before Quell's AI Agents, teams had limited options to execute or improve UAT, each with major shortcomings. Manual testing by business users or QA analysts remains the most common approach, but it’s error-prone and inconsistent by nature – human testers can cover only so many scenarios and might overlook edge cases or misinterpret expected behavior. This leads to limited test coverage and human error, where many usage paths aren’t exercised and subjective judgments cause inconsistent feedback. Manual UAT also tends to create communication gaps (e.g. between technical and non-technical stakeholders) and slows down delivery timelines.

Some teams attempt to automate UAT with scripts using tools like Selenium, Cypress or Playwright. While helpful for regression checks, these code-based frameworks require developers to write and maintain test scripts for each feature – a labor-intensive process in itself that demands programming expertise and constant upkeep as the product evolves. Such scripted tests typically cover only predefined “happy path” scenarios; they often fail to detect unexpected functional bugs or ensure that every acceptance criterion in a user story is met. Maintaining these test suites can become a project as large as the software itself.

“Traditional UI test automation (e.g. Cypress, Playwright) stays closer to code, requiring engineers to script test cases, and can miss business-level issues,” notes the Quell's Founder Alex Christian.

Furthermore, coded tests lack adaptability – they don’t easily handle changes in requirements or UI unless manually updated.

Other organizations rely on external QA services or crowdsourced testing. This can expand coverage somewhat, but outsourcing UAT has drawbacks: external testers may lack context about the product’s specific acceptance criteria, and coordinating test cycles introduces delays. Security and compliance concerns also arise when sharing pre-release software with outsiders. In regulated industries, third-party testers must be carefully managed under NDAs and oversight, which is cumbersome.

In short, existing alternatives either consume excessive time (manual testing), require heavy upfront effort and maintenance (scripted automation), or fall short in context and agility. None effectively ensure that a new feature truly meets all acceptance criteria across UI, backend, and business logic in a fast, reliable way. This is the gap Quell AI Agents were designed to fill.

Try out Quell's UAT AI Agents for free at Quellit.ai.