The Situation
A large Fortune 500 company in the agricultural industry was in the middle of a sweeping digital transformation. The organization had migrated the majority of its IT environment to the cloud and was investing heavily in AI-powered tools across its operations. Enterprise copilot adoption was underway, and the pace of innovation was accelerating across every function.
But behind the transformation headlines, a quiet crisis was building. Every time infrastructure was patched (a weekly occurrence as changes moved from one environment to the next), a team of testers had to manually verify over 500 applications across multiple enterprise portals. Each portal required Entra ID authentication and SSO into specific applications. Testers navigated to each app, verified key pages loaded, and confirmed core functionality. When a test passed, there was no consistent way to capture evidence. When a test failed, the engineer had to manually document the failure, create a ServiceNow ticket by hand, attach screenshots and environment details, and notify the responsible team.
The regression cycle consumed days of engineering time every week. There was no centralized view of regression health, no standardized evidence format, and no way to correlate test failures with specific infrastructure changes. Release managers had no confidence dashboard; they relied on tribal knowledge and manual status updates. The organization was modernizing everything except the process that verified whether modernization was working.
The Challenge
Our Approach
Aubrant Studios designed and built an end-to-end agentic automated regression system in 90 days across three 30-day iterations, each delivering working software. The system replaced the entire manual workflow: from trigger to authentication, execution, AI-powered analysis, evidence generation, and automated incident management. Every component was built as a reusable module in the organization's Unified Digital Backbone, meaning the investment extends far beyond regression testing.
The architecture is Azure-native, running on AKS with Temporal.io for durable workflow orchestration, Playwright for browser-based test execution across all enterprise portals, LangGraph for AI-powered result analysis, and direct integrations with Azure DevOps (test suite source of truth), ServiceNow (automated incident creation), and SharePoint (patch tracking for on-prem applications and servers).
Temporal.io Orchestration Engine
Durable workflow engine on AKS manages test execution, retries, and parallel runs across auto-scaling Playwright worker pools. Reads test definitions from Azure DevOps and coordinates the entire regression lifecycle with full replay capability.
Reusable Entra ID Auth Module
Custom authentication handler manages SSO flow, token lifecycle, and session management across all enterprise portals. One module, multiple portals, zero credential management overhead. Reusable across every future automation initiative.
LangGraph AI Result Monitor
A LangGraph-powered agent monitors every test execution in real time, evaluating pass and fail outcomes against expected results. On failure, the agent automatically opens a ServiceNow ticket populated with detailed failure context, screenshots, and severity classification. On success, it captures a screen print of the passing test and saves it as structured evidence.
Azure DevOps Test Suite Connector
Pulls 500+ test definitions from ADO, maps them to Playwright execution scripts, and pushes results back with full traceability. Supports both scheduled triggers (CRON) and manual kick-off via REST API.
Stakeholder Evidence Reports
Every regression run produces a stakeholder-ready summary: pass rates, failure analysis, trend data, and direct links to ServiceNow incidents. No more manual evidence assembly or inconsistent formats across teams.
What Made It Work
90-Day Iterative Delivery
Three 30-day iterations, each delivering working software. Iteration 1: 10 tests automated end-to-end as proof of concept. Iteration 2: 250+ tests with AI analysis. Iteration 3: full 500+ suite, fully operational. No big-bang risk.
Reusable Component Architecture
Every component (auth module, orchestration engine, evidence generator, ServiceNow agent, ADO connector) was built as a standalone reusable capability. The regression investment becomes the foundation for enterprise-wide automation.
Shadow Mode Validation
Agents ran alongside manual testers for the first iteration, validating accuracy before the team trusted autonomous results. This built confidence systematically rather than demanding a leap of faith.
Production Trace Integration
The SharePoint patch tracker feeds directly into the regression trigger, so the system knows exactly which infrastructure changes prompted each run and correlates failures to specific patches.
Governed AI Decisions
OPA policies enforce guardrails on every AI decision. Azure Purview maintains full lineage. Every execution, every AI classification, every ticket is auditable from trigger to evidence.
Future-Ready Architecture
The same Temporal orchestration engine and AI agent cluster scale to app integration testing, product team self-service, and PC/device validation without re-architecture. Phase 1 funds the next three phases.
The Outcomes
Reduction in regression time: from days of manual testing to hours of automated, parallel execution across all enterprise portals
Evidence coverage: every test produces stakeholder-ready evidence with screenshots, timestamps, and environment context automatically
Manual ticket creation: failures auto-generate ServiceNow incidents with full context, screenshots, and severity classification
Applications tested per regression cycle with AI-powered analysis determining pass/fail and generating failure explanations
Components built for the Unified Digital Backbone: auth module, orchestration engine, evidence generator, ServiceNow agent, ADO connector, and trigger framework
From kickoff to fully operational: three iterations of working software, not a 6-month waterfall plan with value deferred to the end
Why Others Fail
The Takeaway
Regression testing at enterprise scale is not a scripting problem. It is an orchestration, authentication, intelligence, and evidence problem. When you build agentic AI into the backbone of quality engineering (not bolted on as an afterthought) you transform QE from a bottleneck into a competitive advantage. The same components that automate 500 smoke tests today become the foundation for integration testing, device validation, and self-service quality across the enterprise tomorrow. Aubrant does not leave behind a one-off script. We leave behind a governed, scalable engineering capability.