Agentic AI Reliability: Automating Agentic QA

TL;DR

Creation got cheap. Checking did not. When agents produce work faster than humans can review it, the review queue becomes the constraint on everything, and quality quietly degrades while velocity metrics look excellent.

Hand written end to end tests cannot keep pace. They are expensive to write, brittle by nature, and the first thing teams abandon. Half of QA leaders using AI in test automation name maintenance burden and flaky scripts as a key challenge, according to the Capgemini World Quality Report.

Agentic QA gives the agent a browser and a test runner. It explores the application to learn how it actually behaves, then writes and iterates on Playwright tests against that understanding.

Tests are extracted as artefacts and run without an agent in the loop. The self healing loop only wakes the model when a test breaks, so the model is not in the core testing path burning tokens on every run.

When it breaks, something decides whether that is a bug or a change. Legitimate failures fail. Cosmetic drift gets patched and saved for next time.

Our Argument:

The last two years reorganised software engineering around a simple fact: writing the thing is no longer the slow part. Reviewing it is.

Every team that adopted AI assisted development has met the same wall. Output per engineer climbs. The test suite was sized for the old rate. New features arrive without coverage, existing tests break against changed code, and the pragmatic response is to skip the flaky ones. Coverage erodes without anyone deciding to erode it. A survey of software leaders published by SmartBear this May found 70 percent saying application quality has already degraded as AI accelerated their development, with 60 percent reporting quality issues because code creation outpaced testing capacity.

Agentic systems make this sharper, because the thing being produced is not only code. It is a report, a reconciliation, a creative asset, a territory plan. Somebody has to confirm the artefact does what it was supposed to do, and "somebody reads it carefully" does not scale past the first few hundred runs.

The traditional answer is browser automation, and it fails for a well known reason. A hand written end to end test encodes assumptions about selectors and layout that the application will invalidate next sprint. The test breaks, nobody has time to investigate, it gets disabled. Most automation suites stop being maintained in their second year, not because they were badly built, but because maintenance was never funded.

Explore, then test, then stop paying for it

Our Agentic QA module works the way a good tester works, in that order.

First it explores. A suite of browser tools lets the agent drive the application and learn its dynamics rather than guessing from a specification. That exploration is what makes the resulting tests non brittle: they are written against observed behaviour.

Then it tests. A fully managed Playwright setup with agent native tooling lets the agent write, run and iterate on tests quickly, validating the behaviour that matters once it understands the application.

Then it gets out of the way. Tests are extracted as artefacts and executed directly, with no agent involved. This is the part that makes the economics work. The model is used to author the test and to diagnose failures, never to run the suite, so tokens are not spent re-deriving the same test on every execution.

The self healing loop closes it. When a stored test fails, the loop evaluates whether it found a real regression or whether the underlying application simply moved. Real failures fail loudly. Cosmetic drift is patched and the updated script is saved automatically.

Interactive test mapping is next: the agent watches a real user complete a task and writes the matching test.

Speed without verification is not speed. It is unmeasured risk with better dashboards.