For QA Managers, Product Owners, and Engineering Leads
Autonomous Functional Testing: generation, execution, and maintenance with minimal human intervention
We implement and operate an autonomous testing model that turns software functional validation into a continuous, controlled, and measurable process: we analyze your requirements, generate test cases, automate them, execute them in your CI/CD, and activate self-healing when the system changes.
Validating software in environments where functional coverage and delivery speed are priorities
Problems that manual testing and traditional automation do not solve
Three scenarios where software functional validation slows down delivery
Manual or partially automated testing that does not scale
The manual testing cycle takes days or weeks. Each release requires the QA team to manually validate functional flows. Consistency varies depending on the person executing: errors of omission, acceptance criteria interpreted differently, and coverage that depends on team availability.
Tests that break with every interface change
The team has automation, but each release requires someone to review and repair scripts that failed—not because of a real bug, but because the UI changed or business logic was modified. Maintenance costs grow with the product. The automation that was supposed to save time is consuming it.
Requirements that change faster than they can be covered
In agile environments with frequent deliveries, requirements and User Stories evolve sprint by sprint. The QA team lacks the capacity to maintain functional coverage at the pace of development. New features are deployed without sufficient coverage. Testing debt accumulates and is paid for in production.
The three scenarios share the same root cause: without an Autonomous Functional Testing model that analyzes User Stories, generates, executes, and maintains tests continuously and in a controlled manner, the QA team remains the bottleneck for every delivery.
Not adopting Autonomous Functional Testing is not maintaining the status quo: it is accepting cost and slowness as part of the process
$2.4B USD
The annual cost of poor software quality in the U.S., much of it attributable to functional defects that a continuous testing system would have detected in development, not in production
– CISQ / Gartner, 2024
| +cost |
Permanent maintenance, |
| +time |
In each testing cycle |
| +risk |
Dependence on specialized profiles |
Where does the real cost appear in your organization?
Slow testing cycles that delay every delivery, manual functional validation cannot be executed at the pace of sprints. Each release waits for QA to finish manually validating acceptance criteria
Increasing script maintenance costs, traditional automated tests require manual review and repair with every UI or logic change. That cost grows linearly with the product
Inconsistency in manual execution of acceptance criteria, different people interpret and execute criteria differently. Coverage depends on who is available, not on a controlled system
Dependence on specialized profiles to scale QA, the capacity of the quality process is limited by the QA team’s headcount and the learning curve of each new member
Functional defects detected in production, not in development, the cost of a defect detected in production is 100x higher than one detected in development. Shift-left is the principle that Autonomous Functional Testing operationalizes
From requirement to evidence, without manual intervention
We implement and operate Autonomous Functional Testing in your environment: from requirements to tests without manual intervention
Autonomous Testing combines our Functional Testing methodology with generative AI models (LLMs via MCP) to analyze your User Stories or requirements, automatically generate the test plan and automation scripts, execute them in your CI/CD pipeline, and activate self-healing so tests adapt on their own. No additional infrastructure required and aligned with the AI Act and ISO/IEC 42001.
User Story Generation
Self-healing
CI/CD Integration
Concrete Deliverables
When functional validation operates autonomously
What changes in the QA process when tests are generated and maintained on their own
Tests generated from User Stories and acceptance criteria
Each user story generates its set of tests without the team writing scripts. Functional coverage grows at the same pace as development—not months later.
Self-healing: ≥85–95% stable tests
In the face of interface or logic changes, the service updates scripts automatically. The team stops spending time repairing what the system can fix itself.
CI/CD execution in minutes
Tests are executed automatically after each change, in minutes. Functional defects are detected in development—where the correction cost is minimal, not in production.
Real-time functional KPIs
Continuous metrics on functional coverage, defects detected versus those leaked to production, test stability, and average execution time per suite.
Growing functional coverage that keeps pace with sprints
In agile environments, each sprint adds new User Stories. Autonomous Testing generates coverage automatically—without accumulating testing debt or depending on team availability.
QA team reoriented to critical judgment
Autonomous Functional Testing does not replace the QA team: it eliminates repetitive work. The team focuses on exploratory testing, functional risk analysis, and user experience validation.
AUTONOMOUS FUNCTIONAL TESTING IMPLEMENTATION MODEL
Three implementation phases, concrete deliverables at the end of each, and continuous operation from the first month
Tool Selection
Configuration and Implementation
Configuration and implementation of Autonomous Testing in your environment, integrated into existing CI/CD pipelines and without additional infrastructure.
Implementation, Initial Learning
Launch with initial system learning on your application and activation of self-healing.
Unattended Execution and Growing Coverage
Unattended execution in your CI/CD pipeline with automatic start in minutes after each change. The service generates new coverage for each new User Story with autonomous learning sprint by sprint.
Self-healing and Autonomous Adaptation
Self-healing detects interface or logic changes and updates scripts automatically. Stability of 85–95% without manual intervention is maintained as a service SLA.
Reporting and Continuous Improvement
Results reporting in near real-time after each execution. Continuous improvement cycles on coverage and efficiency, aligned with the regulatory evolution of the AI Act and ISO/IEC 42001.
Proven success in agile environments with continuous deliveries
Organizations that moved from manually validating User Stories to having automatically generated coverage
Financial Sector
Financial institution with functional regression cycles of weeks converted into automatic executions of hours
- Functional tests automatically generated from the active sprint’s User Stories starting from week 3
- Active self-healing: ≥85% test stability without manual intervention from the QA team
- Functional defects detected in development—documented reduction of leaks to production
“We went from two-week functional regression cycles to an automatic execution that runs in hours. The QA team stopped spending time on maintenance and started focusing on functional risk coverage.”
— Digital Product Leader
Insurance Sector
Insurer with insufficient functional coverage facing an agile delivery pace with changing User Stories every sprint
- Automatic generation of functional tests for each new User Story—without accumulated testing debt
- Integration into the CI/CD pipeline with execution in minutes after each commit by the development team
- Real-time reporting with quality KPIs presented in each sprint review
The ability to generate functional coverage automatically for each new User Story changed the dynamic between development and QA. For the first time, coverage grows at the same pace as the product.
— QA Assistant Director
Verified operational scale
28 years of specialization in QA
Issues Detected
Automated Test Cases
Testing Hours
Test Cases Designed
Autonomous Testing FAQ
What we are asked most about Autonomous Functional Testing
What is Autonomous Functional Testing and how does it differ from traditional automation?
It is a service that uses AI to design, execute, maintain, and optimize functional tests with minimal human intervention. Unlike traditional automation, it does not require the team to write scripts: the service directly analyzes User Stories or acceptance criteria, automatically generates the test plan and scripts, executes them in the CI/CD, and activates self-healing when the system changes. Functional coverage grows with the product, not with the QA headcount.
What inputs does MTP need to start the service?
The required inputs are: functional description of the system; system access (web); user stories (US) or functional requirements; and specification of target browsers, devices, and operating systems. The AI model is selected jointly during the initial evaluation phase according to the environment and the client’s needs.
What concrete deliverables will my team receive?
At the end of each phase: technical feasibility and evaluation report; OmniaTest® Functional framework implemented in your environment; set of functional tests automatically generated from User Stories; self-maintained tests with active self-healing; continuous execution reports with real-time metrics; and implementation of version control for the generated code. Each deliverable is presentable to management and engineering.
What KPIs does the service measure and how often are they reported?
The service monitors five KPIs in near real-time after each execution: functional test automation percentage; functional coverage by module and sprint; defects detected in testing versus those leaked to production; average suite execution time; and test stability. They are reported automatically after each execution in the CI/CD.
What are the service SLAs?
The service operates under the following SLAs: system availability ≥99%; automatic execution in CI/CD starting in minutes after each change; early detection (shift-left) of defects with immediate automatic reporting; near real-time metrics after each execution; self-healing with stability ≥85–95% without manual intervention; and continuous improvement of coverage and efficiency based on autonomous learning.
How long does the implementation take and when do I start seeing results?
The initial implementation has an estimated duration of 1.5 to 2 months: tool evaluation and selection (2–3 weeks), framework implementation configuration (3–4 weeks), and launch with initial system learning. The first results—tests generated from User Stories, active self-healing, initial coverage metrics—are visible from the first weeks. The estimated time per additional artifact in continuous operation is approximately 1 month.
Do I need specific AI infrastructure to get started?
No specific AI infrastructure is required to start the evaluation. For the implementation phase, an architecture compatible with the service tools is required, which is defined jointly during the initial evaluation. The service adapts to the client’s environment without requiring additional infrastructure. Technological independence is a principle of the service: we do not tie the client to specific platforms.
What does Autonomous Functional Testing not do?
It does not replace the experience, judgment, or critical thinking of the QA team or developers. It does not decide which features to build or what is a business priority. It can analyze functional behavior documented in User Stories and acceptance criteria, but it does not understand undocumented business intentions, advanced UX, or complex human expectations that require qualitative judgment. For those dimensions, the QA team remains indispensable—and with Autonomous Testing, they have more time to focus on them.
Which of the three scenarios is your AI system in?
The 5-business-day Diagnosis maps your current validation level, the highest-risk flows, and the implementation roadmap—with a concrete deliverable to present to management and auditing.