QA that thinks.
Tests. Fixes itself.
We don't just automate test cases - we build AI testing agents that understand your application, adapt to unpredictable outputs, and validate at a scale no human team can match. This is what AI-powered software testing looks like when it is built for production.
Agentic QA Loop
RunningUnderstand
Agent reads your app context, routes, and past defects
Plan
Generates test scenarios including edge cases you missed
Execute
Runs tests in parallel across all flows simultaneously
Validate
AI validator checks outputs with no fixed expected result
Self-heal
Flaky selectors detected and fixed automatically
2,000+
Flows validated
10X
Faster than manual
0
Flaky test backlog
Traditional QA assumes you know the expected output.
AI-powered applications don't work that way.
Agentic QA is how you test software that thinks.
Four things we've actually built,
not things we plan to.
When your AI's output can't be predicted, rule-based testing fails. We built AI that judges AI.
The Challenge
AI agents generate recommendations based on complex, multi-variable calculations. There's no single correct answer, the same input can produce legitimately different outputs depending on upstream context, user state, and model behaviour. Traditional assertion-based test automation always breaks here. You can't write `expect(output).toBe(x)` when `x` is an intelligent inference.
What We Built
A two-agent setup using the Anthropic API where a primary Claude-powered agent generates financial or analytical recommendations, and a second validator agent semantically evaluates those outputs for correctness, coherence, and domain compliance, without any hardcoded expected values. The validator uses a structured rubric built into its system prompt and returns a scored assessment with reasoning.
The Result
2,000+ flows validated using this setup
Every page. Every metric. Zero manual effort.
The Challenge
Performance audits are run manually, inconsistently, and almost always only on the homepage. The remaining 40 pages of an application accumulate undetected regressions in Core Web Vitals, accessibility scores, and SEO signals, and teams only discover the damage when Google Search Console flags a drop or a user with assistive technology files a complaint.
What We Built
An agent that autonomously maps an application's route structure, generates navigation flows using AI-inferred logic (no pre-written scripts), visits each page, and invokes the Lighthouse API to capture Performance, Accessibility, Best Practices, and SEO scores. Results are compiled into a structured report with regressions flagged and delta-scored against a baseline, runnable on every deployment.
The Result
Full-application Lighthouse audit with zero manual test scripts
Functional testing that runs itself.
The Challenge
A human QA team tests features sequentially, one engineer, one flow, one module at a time. Parallelizing manual testing requires more headcount. For an application with 30+ distinct modules and weekly releases, full sprint coverage is either impossible or it blocks the release cycle.
What We Built
Agents built with Claude Cowork and Claude Code that ingest application context, codebase, documentation, API contracts, and autonomously decompose the test surface into independent modules. The agent then creates parallel testing jobs and executes functional tests across different parts of the application simultaneously, collating defects into a unified structured log with reproduction steps, severity, and module attribution.
The Result
Parallel coverage across entire application within a single sprint
10X faster automation. Self-healing tests. No more flaky nightmares.
The Challenge
Writing automation test cases is slow, requires dedicated engineers, and produces brittle suites that break with every UI change. Flaky tests caused by dynamic selectors, race conditions, and inconsistent test data burn more maintenance time than they save. Most teams end up with half-maintained automation that the team doesn't trust.
What We Built
An AI-assisted automation pipeline using Cursor, Antigravity, Claude Code, and VS Code Copilot that reads application source code and generates test cases contextually. Flaky tests are automatically detected and refactored. Classical automation problems, dynamic waits, resilient selector strategies, test data generation, are solved on the fly by the agent rather than manually by an engineer. Test case generation runs 10X faster than traditional authoring.
The Result
10X faster test generation, self-healing selectors, zero flaky tests
Numbers from production systems.
2,000+
Flows validated autonomously
AI agents validating AI agent outputs, no human reviewer
10X
Faster test automation
AI-generated test cases vs. manual authoring by engineers
Zero
Manual scripts for performance audits
Full Lighthouse coverage across every page, every deploy
Tools we use in production.
Not a vendor logo parade, these are the specific tools behind the four accomplishments above.
AI Agents
Performance
Automation
Functional
Want to see Agentic QA in action?
We'll walk you through a live demo of one of these setups, no slides, no decks. Just the system running.
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