AI AgentQA Agent
QA Agent that automates it & engineering workflows using your data and tools.
The challenge
Regression testing is a manual pass squeezed in before release, so coverage follows whoever wrote the script rather than business risk. Flaky tests get ignored until a real failure hides among them, and defects escape into production with no record of what was actually tested.
The outcome
A Microsoft Foundry agent derives cases from the requirement and the change, Azure OpenAI generates the tests and safe synthetic data, and Azure Container Apps runs the suites in parallel across environments. Real defects are separated from flake automatically, and a human signs off every release.
At a glance
- Type
- ai agents
- Category
- technical
01 — Architecture
End-to-end architecture
A requirement and its change are read for testable risk. The Foundry agent derives cases to cover that risk rather than the code, generates safe synthetic test data, runs the suites across environments and separates real defects from flake — but a human signs off every release decision.
- Team: Tester / Developer
- Source: GitHub, Teams / Boards
- Execution: Azure Container Apps, Azure Load Testing
- AI & agent: Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search, Azure AI Content Safety
- Data & records: Azure SQL, Azure Cosmos DB, Azure Blob Storage
- Decision & insight: QA sign-off, Application Insights, Power BI / Fabric
02 — Workflow
Process & decision workflow
How a change becomes an evidenced release recommendation — analyse, design, generate, execute and triage, then branch. Green gates produce a release recommendation with the coverage published, while a real defect or a coverage gap stops the release and goes to QA for sign-off.
- Analyse: Requirement and change read for testable risk
- Design: Cases derived to cover the risk, not the code
- Generate: Automated tests and safe test data produced
- Execute: Suites run across environments and browsers
- Triage: Failures separated into real defects and flake
- Report: Coverage, risk and release readiness summarised
- Path 1 · gates green — Recommend release: Coverage, risk and evidence published
- Path 2 · real defect or coverage gap — QA sign-off needed: Failure, evidence and risk presented
03 — Components
Key Microsoft components
Test results only matter if they are honest — design, execution, triage and evidence all stay on the Microsoft stack.
GitHubCode, test suites and continuous integration triggers.
Microsoft Teams / BoardsTest cases, defects and release collaboration.
Azure Container AppsParallel test runners across environments and browsers.
Azure Load TestingPerformance and scalability regression runs.
Microsoft Foundry Agent ServiceTest design, execution and triage orchestration.
Azure OpenAI modelsCase derivation, test authoring and defect write-up.
Azure AI SearchRetrieval over specifications, past defects and coverage.
Azure AI Content SafetyGuardrails on synthetic test data and personal information.
Azure API ManagementSecure gateway for test harness and service virtualisation.
Azure SQLTest runs, results, defects and coverage records.
Azure Cosmos DBExecution state, retries and flake history.
Azure Blob StorageScreenshots, traces, logs and test evidence.
Azure Application InsightsProduction signals that close the feedback loop.
Power BI / Microsoft FabricCoverage, escape rate and release-readiness dashboards.
04 — AI
What the agent consumes
The capabilities the agent applies to every release candidate, and the line it does not cross.
AI capabilities embedded in the agent
- Risk-based test design
- Requirement analysis
- Test case generation
- Test data synthesis
- Automated execution
- Flake detection
- Failure triage
- Visual and accessibility checks
- Performance regression detection
- Defect summarisation
- Coverage analytics
- Release-readiness reporting
AI responsibility boundaries
The agent tests and reports; a human signs off every release. It does not delete or weaken a failing test, mark a real defect as flake without evidence, or use production personal data as test data. A gate that is not satisfied stays visibly unsatisfied rather than being reclassified.
05 — Personalization
Personalization & evolving process
The same methodology applies to every agent in the catalog. Tune the product risk, the testing template, the gates and the value model — the page structure stays identical.
Product & risk profile
Define the applications, platforms, browsers, critical journeys and regulatory testing obligations in scope. The personas are the tester, the developer, the release manager and the product owner.
Testing template
One consistent flow for every quality agent: analyse risk, design cases, generate tests and data, execute, triage failures, report readiness and feed production signals back.
Gates & data rules
Declare the coverage thresholds, severity definitions, flake policy, accessibility standards and the test data policy. Production personal data is never used for testing.
Value model
Capture baseline metrics first, then map the expected benefits: regression cycle time, coverage of critical journeys, triage hours, escaped defects and release confidence.
06 — Impact
Key outcomes & business impact
Starting targets for the value case — validate each one against the customer baseline during discovery.
- Test coverageRisk-basedCoverage follows business risk rather than code paths.
- Regression cycle−60%Suites run in parallel on every change, not per release.
- Escaped defects−35%More critical journeys covered before production.
- Triage effort−50%Flake is separated from real failure automatically.
Illustrative improvement index
Manual baseline = 100. Illustrative targets, not a commitment — confirm against the customer baseline.
- Regression cycle time: manual baseline 100, AI-assisted target 40
- Triage hours: manual baseline 100, AI-assisted target 50
- Escaped defects: manual baseline 100, AI-assisted target 65
Related & recommended
Derived automatically from our solution knowledge graph.
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Related quick wins
Ready-made Azure AI to start fast.
Technologies
What powers this solution.
TechnologyAzure AI Foundry
Platform to design, evaluate and operate AI apps and agents.
TechnologyMicrosoft Copilot Studio
Low-code platform to build custom copilots and agents.
TechnologyAzure Functions
Serverless compute for event-driven workloads.
TechnologyAzure OpenAI
Enterprise access to GPT models with governance.
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Keep it running and optimised.
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