From manual QA decisions to agent-led testing intelligence

While the client’s QA ecosystem had become increasingly complex, some critical testing decisions continued to remain manual, inconsistent, and difficult to audit.

Key challenges included:

• No intelligent mechanism to determine which tests should run per code change.

• UI selectors frequently broke after routine front-end refactors.
• Manual selector repair consumed hours of engineering effort.
• Flaky tests caused repeated pipeline failures without quarantine mechanisms.
• No shared learning across testing disciplines and agents.
• Risk-based test prioritization was manual, subjective, and difficult to audit. 

Nagarro introduced an agent-based testing framework to automate QA decisions, reduce flakiness, and improve continuously with every release.

The solution provided:

• A MACT framework with seven specialized AI agents collaborating on test decisions.
• Intelligent test selection to decide which tests to run, skip, or prioritize for each code change.
• A three-pass decision protocol – analyze, refine, and consensus – where agents shared signals, refined findings, and voted on the optimal test plan.
• A self-healing agent that detected broken UI selectors and recommended confidence-scored alternatives.
• A continuous learning loop using historical correlation analysis, adaptive thresholds, and trend detection.
• A weighted consensus model with domain-authority tie-breaking and veto power for critical agents such as sanity and security testing.

The AI-led testing framework enabled faster, more reliable releases with complete transparency and compliance readiness.

This engagement delivered:

• 40% reduction in regression execution time through intelligent test selection.

• 50% fewer flaky-test-induced pipeline failures through automated handling.
• Near-zero UI selector maintenance effort through self-healing locator repair.
• Continuous improvement in decision accuracy through correlation analysis and adaptive thresholds refined with each release.
• Full audit trail of test decisions stored as structured JSON artifacts.