Agentic QA & Smart Testing
Our AI agents read your requirements, plan a test strategy, write the test cases, execute them, triage failures, and report results. A QA engineer reviews the plan and signs off on anything that gates a release. You get the volume of an always-on test team, with a human accountable for what ships.
Regular automation makes you write every test. This doesn't.
Even the best test automation frameworks need someone to decide what to test, write the scripts, fix the broken ones, and review the results. Your QA team still spends half their time on maintenance instead of catching real bugs.
Agentic QA takes the volume off that loop. We deploy AI agents that read your requirements, decide what to test, write the test cases, run them, figure out what failed and why, and send you a report. A QA engineer reviews what the agent produced at defined checkpoints and approves anything that gates a release, so nobody is hand-writing scripts and nothing ships unchecked.
This is not a future concept. The agent does the volume, coverage grows every sprint, and the QA team stays the same size.
Everything Included from Day One
A complete, end-to-end QA service: every deliverable, every tool, every report included.
Agent Training & Setup
- Custom AI agent trained on your specific application
- Requirements and user story ingestion pipeline
- Risk-based test strategy generation
- Agent scope and domain knowledge configuration
- Integration with your existing dev workflow
Autonomous Execution
- Self-directed test planning every sprint
- Auto test case generation and execution
- Smart retry logic for flaky scenarios
- Auto defect detection and classification
- CI/CD trigger integration on every commit
Intelligent Analysis
- AI root cause analysis on every failure
- Duplicate defect detection and merging
- Regression impact analysis per commit
- Coverage gap identification and auto-fill
- Flakiness detection and elimination
Smart Reporting
- Natural language test reports after every run
- Defect severity triage with AI scoring
- Trend analysis dashboards
- Sprint quality score cards for stakeholders
Continuous Learning
- Agent improves accuracy with every run
- New features auto-discovered and tested
- Historical defect patterns learned over time
- Adaptive test prioritisation based on risk
Integration & Connectivity
- Jira: auto-created defect tickets with context
- Slack: AI-generated test summaries in your channel
- GitHub: PR status checks and quality gates
- TestRail: automatic test case sync and tracking
How We Actually Work
No black box. Here's exactly what happens from the first call to a fully running test suite, and what you get at each stage.
We feed the agent your requirements
The agent reads your user stories, acceptance criteria, and documentation. It builds a map of what your application does, how users interact with it, and where the highest-risk areas are.
The agent builds a test strategy
Using risk-based analysis, it generates a prioritised test plan, identifying the most critical flows, edge cases, and regression-prone areas. A QA engineer reviews that plan before it runs.
Tests run on their own, on every commit
The agent writes and executes the tests. Every push, every PR, every nightly build: tests run without anyone triggering them, and the results come back to a human who reviews them.
Failures get triaged by AI
When something fails, the agent figures out if it's a real bug or a flaky test. It creates a Jira ticket with full context, screenshots, and a suggested root cause. Your developers get actionable defects, not noise.
Coverage expands automatically
After every sprint, the agent discovers new features and adds coverage on its own. Your test suite grows with your product, without anyone writing new test cases by hand.
The agent gets smarter over time
Each run teaches the agent which areas break most, which tests catch the most bugs, and where to focus next. It adapts its strategy continuously, getting better every sprint.
Tools and Frameworks We Use
Tool-agnostic by design: we select the best technology for your specific stack and workflow.
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Every Layer, Every Flow
Comprehensive coverage across UI, API, data, and security layers, so nothing gets missed.
Web Application Flows
Full user journeys tested autonomously
API Contracts
REST and GraphQL endpoint validation
AI Feature Testing
LLM outputs and AI agent behaviours
Mobile Flows
iOS and Android via Appium integration
Auth & Permissions
Role-based access and session security
Integration Points
Third-party APIs, webhooks, and events
Data Integrity
Database state verified after every action
Error Scenarios
Edge cases, timeouts, and failure paths
Performance Checks
Response time assertions on every action
Real Impact from Sprint One
Measurable outcomes your engineering team and business will feel immediately.
Less Manual Effort
Agents run full test cycles on their own, so your team reviews results instead of running tests by hand.
Continuous Learning
Every test run makes the agent smarter, and coverage improves over time.
24/7 Testing
Agents run overnight, on weekends, and on every commit, giving you quality signal around the clock.
Risk-Based Testing
AI prioritises the highest-risk areas of your codebase, for maximum impact with minimum test time.
Natural Language Input
Feed user stories or requirements directly to the agent, and it generates test cases automatically.
Self-Improving
Agent coverage grows with your codebase, with no test scripts to maintain by hand.
Got questions?
Here are the ones we get asked most. If yours isn't here, just ask; we're happy to talk it through.
Want to talk through your specific setup?
We'll walk you through how this works for your stack, team size, and release cadence, with zero commitment.
It's a system that reads your requirements, decides what to test, writes test cases, runs them, analyses failures, and reports results, with a QA engineer reviewing its output at defined checkpoints. Think of it as a QA engineer that works around the clock and improves with every run, with a senior one checking the work.