If you're reading this, there's a good chance your team has hit the same wall every growing engineering org eventually hits: manual QA can't keep pace with how fast you want to ship.
Maybe your QA team is buried in regression testing before every release. Maybe bugs are still slipping through despite everyone's best efforts. Maybe you're wondering whether the cost of your QA process is proportional to the value it delivers.
It’s time to consider alternatives, or at least ways to supplement manual QA. This post walks through the main options teams typically evaluate, assessing where each one helps and where it falls short.
Key takeaways
- More testers won't fix a speed problem. Scaling up an in-house QA team or outsourcing to a crowdtesting vendor adds capacity, but neither solves the speed and maintenance issues that make manual QA unsustainable for teams shipping multiple times a day.
- Writing more tests still leaves your team fixing them. Traditional test automation (Selenium, Cypress, Playwright) and AI coding agents both produce real tests in your codebase, but both leave engineers responsible for rewriting those tests every time the UI or flow changes. This only relocates the maintenance burden rather than removing it.
- Continuous, auto-healing E2E coverage removes the maintenance tax. AI-native test agents provide continuous verification with auto-healing tests that adjust to UI and flow changes without the need for manual rewrites.
Why teams look beyond manual QA
Teams that rely on manual QA run into a myriad of issues, finding that it is:
- Slow relative to release velocity. If your team ships multiple times a day but a full manual regression pass takes two days, manual QA becomes the bottleneck or gets skipped altogether.
- Expensive to scale. Doubling your test coverage with manual QA usually means doubling your QA headcount.
- Reactive by nature. Manual testers find bugs after a build is ready, which means fixes happen late in the cycle when they're most expensive.
- Inconsistent. Even with detailed test scripts, human testers vary in thoroughness, especially under deadline pressure.
This doesn’t detract from the value of manual QA altogether. There's a strong case for keeping humans in the loop for certain kinds of testing (more on that below). But it does mean that for most teams, manual QA alone isn't a sustainable foundation for testing infrastructure.
With that in mind, here are five ways that your team can move beyond manual QA, as well as the pros and cons of each option:
Option 1: Scale up your in-house manual QA team
While not necessarily an alternative to manual QA, the most straightforward response to "manual QA can't keep up" is to hire your way out of the problem.
Pros:
- Human testers are excellent at exploratory testing, finding the weird and unscripted edge cases that automated tests don't think to check
- Strong for usability and UX feedback, where judgment matters more than pass/fail criteria
- No tooling investment or technical setup required to get started
Cons:
- Cost scales linearly with coverage because more features and more flows mean more testers or more hours
- Doesn't solve the speed problem; manual regression testing remains a pre-release bottleneck
- Knowledge often lives in testers' heads rather than in reusable, version-controlled assets
- Turnover means re-training and inconsistent coverage over time
Best for: Exploratory testing, usability testing, and edge cases that are genuinely hard to specify in advance but not as your primary regression testing strategy.
Option 2: Outsource or crowdsource QA
Many teams turn to outsourced QA vendors or crowdtesting platforms, where a distributed pool of testers runs through test cases (often across many devices, browsers, and locales).
Pros:
- Access to broad device/OS/browser coverage without maintaining your own test lab
- Can flex up or down based on release schedules, avoiding fixed headcount costs
- Useful for localization and region-specific testing
Cons:
- Turnaround time is still measured in hours or days, not minutes and this doesn't fit continuous deployment workflows
- Quality and consistency depend heavily on the vendor and the specific testers assigned
- Context-switching cost: external testers don't have the product knowledge an in-house team builds over time
- Bug reports often need clarification or reproduction before they're actionable, creating inefficient communication overhead
Best for: Periodic, broad-coverage testing (major releases, device compatibility sweeps) but less suited as a continuous, every-PR safety net.
Option 3: Traditional test automation (Selenium, Cypress, Playwright, etc.)
This is the most common "next step" for teams trying to move past manual QA: writing automated test scripts that run in CI.
Pros:
- Tests run in minutes, integrate directly into CI/CD, and can run on every commit
- Once written, a test can run thousands of times for effectively zero marginal cost
- Mature ecosystem, well-understood patterns, large talent pool familiar with these tools
Cons:
- Someone has to write every test, meaning that coverage is bounded by how much engineering time you can dedicate to test authoring
- Tests are tightly coupled to implementation details (selectors, element IDs, page structure), so UI changes routinely break them
- Maintenance becomes its own job where teams report spending as much time fixing broken tests as writing new ones
- Flaky tests can erode trust over time; equally, too much confidence in automated test scripts can let production failures through that tests couldn’t see
Best for: Teams with the engineering capacity to both build and continuously maintain a test suite. We’ve found that, in practice, this is a smaller group than it sounds like—especially as products evolve quickly.
And this gets to the crux of the issue: traditional automation solves the speed problem but introduces a maintenance problem that often ends up looking a lot like the original bottleneck, just relocated to a different team.
Option 4: Using AI coding agents to generate tests on demand
It’s tempting to think that AI coding agents are an automatic fix for testing’s maintenance problem. And for engineering teams that have made the shift to AI coding agents, Cursor, Claude Code, and Copilot are popular options. Users can write tests the same way they write product code: prompt for a test, get a draft, tweak it, commit it.
Pros:
- No new tooling because the same agent your team uses for development can write tests
- Fast first draft, especially for straightforward flows
- Tests land in your existing framework and language, so they fit your codebase
Cons:
- On-demand only. The agent writes a test when someone asks then moves on, rather than running, watching, or maintaining that test on an ongoing basis
- Coverage depends on individual habits (who remembered to ask, and when), not on what the application actually needs
- When the UI or flow changes, tests break like any other. Fixing them means going back to the agent (or doing it by hand), so the maintenance burden shows up again later
- Output quality varies with how the agent is prompted and how much context it has about the app
Best for: Quickly filling a coverage gap on a single PR, not for building or maintaining a durable test suite.
Option 5: AI-native, continuously verifying test agents
This category (where Checksum's E2E Agent sits) takes a different approach. Unlike Option 4, which uses an AI agent to write a test and move on, here the agent keeps running and keeps the test working as the product changes—maintaining end-to-end tests across your application
Pros:
- Tests run against real user flows and production-like conditions, catching failures that pass unit tests but break in production
- Auto-healing tests adjust to UI and flow changes without manual rewrites. When a button moves or a flow gets restructured, the test adapts rather than breaking
- Broad E2E coverage without a dedicated team writing test code line by line
- Runs continuously as ongoing verification of how the product actually behaves
- Shifts QA effort from "writing and fixing tests" to "reviewing and refining what the agent finds"
Cons:
- Newer category with less of an established track record than Selenium-era tooling
- Some upfront setup to point the agent at the right flows and environments
- Doesn't fully replace human judgment for subjective UX or exploratory testing (see Option 1)
Best for: Teams that want broad, durable E2E coverage without the ongoing tax of test maintenance, particularly teams that have already felt the pain of Option 3.
How to think about choosing
No single option here is a complete QA strategy. Scaling up manual QA (Option 1) and outsourcing (Option 2) add capacity, but capacity isn't really the constraint most teams are fighting; speed and maintenance are. Traditional automation (Option 3) and AI coding agents (Option 4) both produce real tests, but both still depend on someone fixing those tests when the product changes. The maintenance tax remains.
In practice, the strongest setups combine:
- Continuous, auto-healing E2E coverage (Option 5) as the foundation, which catches regressions across critical user flows without constant upkeep, and absorbs the maintenance work that Options 3 and 4 leave with engineers
- Targeted manual or exploratory testing (Option 1) for the things humans are still better at: judgment calls, new feature exploration, and usability
- Outsourced testing (Option 2), used selectively for periodic, broad-coverage needs like device, browser, and locale sweeps
- AI coding agents (Option 4) for quickly patching a coverage gap on a single PR, not for carrying the suite long-term
The goal of an alternative to manual QA isn't to eliminate testing effort, it's to stop spending that effort on maintenance. Continuous verification—testing infrastructure that keeps itself current by seeing what production sees—is what makes that possible. It's the layer that goes beyond CI/CD, running ongoing checks rather than one-off gates.
Where Checksum fits
Checksum's E2E Agent creates AI-generated, auto-healing end-to-end tests that provide continuous verification across your application, without the maintenance burden that makes traditional automation hard to sustain. Because the Agent has both code and browser understanding, quality is grounded in both what users see and what the code is doing.
The results can be measured both in time savings and increased test suite trust. For Söderberg & Partners, a Swedish financial services firm offering wealth advice and pension planning, every release was gated on a long, handwritten manual test pass. The process was so heavy that releases became rare and stressful. With Checksum, the team now has a persistent end-to-end suite that runs automatically before every release, saving 90 hours of manual testing a month with zero issues missed.
Robert Gergeo, AI Lead at Söderberg & Partners, describes the transformation: “Manual testing made releases painful, so we couldn't deploy as often as we wanted. With Checksum, we have full coverage, we trust the releases, and we can focus on more important stuff than writing boring tests.”
If your team has outgrown manual QA and is looking for an alternative that enables your team to ship faster and with confidence, take a look at Checksum. Get started →
FAQs
What's the best alternative to manual QA?
There's no single replacement for manual QA and most growing engineering teams need to combine approaches. The strongest setups use continuous, auto-healing end-to-end test coverage as the foundation layer, and use exploratory testing for judgement calls, outsourced testing for periodic, broad-coverage needs, and AI agents for patching a coverage gap on a single PR.
Can AI coding agents like Copilot or Claude Code replace manual QA?
Not on their own. AI coding agents are useful for quickly writing a test on demand, but they don't run, monitor, or maintain that test on an ongoing basis. When the UI changes and the test breaks, someone still has to go back and fix it.
What is continuous verification, and how is it different from CI/CD testing?
Continuous verification is testing infrastructure that keeps itself current as the product evolves, running ongoing checks rather than one-off CI/CD gates. Unlike traditional automation, where static tests break whenever the UI changes, continuous verification relies on auto-healing tests that adapt to UI and flow changes without manual rewrites.
