Claude Code · Multi-Agent QA Platform
Intelligent QA Automation Platform
Powered by Claude Code
From requirements to release — AI agents that test, verify and guard every layer of your stack.
Platform Design & Data Flow
↓
Intelligent Orchestration Engine
✳️
Claude Code
Anthropic — Extended Reasoning Model
200k Token Context Window
Deep Reasoning & Planning
Test & Code Authoring
Root Cause & Recommendations
Parses Specs, Logs & Code
Writes Tests, Queries & Reports
Diagnoses Failures & Proposes Fixes
Operates as Autonomous QA Agent
↓
🤖 Specialised Testing Agents
↓
📚 Shared QA Knowledge Base (click to explore)
📋Test Case Library
💻Automation Scripts
🗃️Synthetic Test Data
📊Run Results & Metrics
📈Reports & Insights
💡Learnings & Patterns
↓
↓
📊 Deliverables & Quality Dashboard (click to explore)
📋Test Cases
💻Automation Scripts
📊Run Results
⚡Perf Reports
🐞Bug Reports
🤖AI Analysis
✅Pass / Fail
🎯Coverage
🛡️Quality Gate
🚀Accelerated Testing
Design & Execution
🎯Broader Coverage
Deeper Accuracy
🔍Shift-Left Detection
Reduce Release Risk
⚙️Eliminate Manual Toil
Focus on What Matters
♾️Quality at Velocity
Every Commit, Every Build
🧠Data-Driven QA
AI-Informed Decisions
AI QA Agents (7 Specialized)
Click any agent card to view its full capabilities and Python code examples.
Open Full Code Reference →
How Knowledge Flows Through the System
📥 Agents Generate
→
🧠 Claude Enriches
→
📚 Store & Index
→
🔄 Agents Learn
→
📈 Continuous Improvement
📋
Test Cases Repository
Structured test cases generated and evolved by the Test Blueprint Agent. Versioned, tagged, and linked to requirements.
Functional
Regression
Smoke
Edge Cases
Negative Tests
BDD Gherkin
▸ Auto-linked to requirements (traceability matrix)
▸ Priority-tagged: P1 / P2 / P3
▸ Gap analysis run on every sprint
💻
Automation Scripts
Production-ready scripts generated by the Test Generation Agent, self-healing when UI changes break selectors.
Playwright Python
Page Objects
pytest Suites
k6 Scripts
API Collections
▸ Self-healing selectors via Claude vision
▸ CI/CD-ready, version-controlled
▸ Postman collections auto-generated
🗃️
Test Data Repository
AI-generated synthetic datasets, fixtures, database seeds, and boundary value sets covering all test scenarios.
Synthetic Data
Fixtures
DB Seeds
PII-Safe
Boundary Values
▸ Realistic but never real customer data
▸ Environment-specific seeds (dev/staging)
▸ SQL validation queries included
📊
Results & Metrics
Historical test execution data, pass/fail trends, flakiness scores, and coverage measurements across all runs.
Pass/Fail Rates
Flakiness Index
Coverage %
Perf Baselines
Trend Data
▸ Visual regression baselines archived
▸ Chatbot quality scores tracked over time
▸ SLA breach alerts automated
📈
Reports & AI Insights
Structured reports generated after every CI run, enriched with root cause analysis, impact assessment, and Jira tickets.
Failure Analysis
Perf Reports
Security Findings
Jira Tickets
Visual Diffs
▸ Claude-authored root cause summaries
▸ Searchable by component, sprint, severity
▸ Auto-posted to Slack / Teams channels
💡
Learnings & Patterns
Accumulated knowledge about recurring failure patterns, high-risk areas, and optimization strategies discovered by AI agents.
Failure Patterns
Risk Areas
Optimizations
Antipatterns
Runbooks
▸ Informs test prioritisation decisions
▸ Auto-updates agent prompts over time
▸ Shared across all 14 agents
Live Dashboard Metrics
1,247
Tests Auto-Generated
12
Open Defects (AI-filed)
📋
Test Cases (JSON + BDD)
Structured, prioritised, linked to requirements. Ready for Jira or TestRail import.
💻
Automation Scripts
Playwright Python + pytest. CI-ready, self-healing, full Page Object classes.
📊
Test Execution Results
Pass/fail status per test, per run, with screenshots on failure.
⚡
Performance Reports
k6 results, SLA comparison, bottleneck analysis, capacity projections.
📸
Visual Regression Diffs
Baseline vs current screenshots, Claude-annotated layout change reports.
🐞
Defect Reports (Jira-ready)
AI-written summaries, root cause, affected flows, fix recommendations + effort.
🤖
AI Insights & Recommendations
Pattern summaries, risk alerts, sprint-level quality scores, team briefings.
✅
Pass / Fail Summary
Real-time per-suite breakdown. Drill down by agent, component, or sprint.
📈
Trends & Velocity
7-day / 30-day pass rate, flakiness trends, coverage trajectory over time.
🎯
Coverage Analysis
Requirement coverage heatmap, uncovered areas flagged for the next sprint.
🛡️
Risk & Quality Gate
Go/no-go signal for deployments based on AI-assessed quality thresholds.
🔔
Automated Alerts
P1 failure notifications to Slack/Teams within 60 seconds of test run completion.
⬇️
Downloadable Reports
PDF/HTML sprint reports, executive summaries, compliance evidence packs.
📌
Jira Auto-Sync
Failures become Jira tickets automatically; resolved tests close tickets via webhook.
Pipeline & Notification Integrations
⚙️ GitHub Actions
🏗️ Jenkins
💬 Slack
💻 Microsoft Teams
🔷 Jira
🧪 TestRail
📧 Email Reports
📊 Grafana / Datadog
🐍 Python SDK
🌐 REST Webhooks
Speed:
Choose a scenario and click Run Pipeline
➊ Trigger
↑
git push origin main
→
⚙️
GitHub Actions triggered
➋ CI/CD Orchestrator
🚀
CI/CD Orchestrator Agent
Waiting for trigger…
➎ Notifications & Outputs
00:00INFOPipeline ready. Select a scenario and click Run.