Software delivery is entering a new era — one where speed, intelligence, and adaptability must coexist. IT systems are becoming more distributed, and digital experiences are becoming more real-time. Quality Engineering (QE) now focuses on improving user experience, anticipating risks, enhancing expertise and resilience, and enabling IT delivery to keep pace with business agility.
As enterprises advance toward cloud-native architectures, omni-channel experiences, and AI-driven customer expectations, traditional QA frameworks are being challenged. The next wave of transformation requires an evolution of quality engineering ,in which human and agentic intelligence coexist in harmony to deliver autonomous, adaptive quality engineering services.
This shift in how enterprises approach quality engineering has inspired AppGinieZ Agentic AI Platform, Tech Mahindra’s next-generation intelligent QE platform built for modern engineering ecosystems.
Why Quality Engineering (QE) Needs a Rethink
Multiple rapidly evolving industry practices are coming together to make intelligent and adaptive testing a necessity rather than a “nice-to-have”:
1. On-Demand Release Cycles
Enterprises are moving from scheduled releases to on-demand and frequent releases. QE needs to be continuous, context-aware, and highly integrated with DevSecOps workflows.
2. Complexity Driven by Distributed Architectures
Interconnected IT systems mean that small changes can create multiple downstream impacts. Future-ready QE teams must understand these risks and adapt and evolve.
3. The Rise of Agentic AI
Analysts and industry predictions indicate that agent-driven systems will soon automate a significant portion of functional and technical decision-making. QE is seen as one of the first SDLC domains to have a significant impact.
4. The Need for Preventive and Predictive Assurance
Defect detection is a term of the past — teams want early risk signals, intelligent prioritization, and automated RCAs that minimize rework and reduce risk. QE teams are being measured on prevention, not detection.
These shifts demand a QE strategy that is not just hyper-automated, but is autonomous, adaptive with continuous learning and feedback loops, and collaborative.
Introducing AppGinieZ Agentic AI Platform : Where Humans and Intelligent Agents Evolve Together
AppGinieZ Agentic AI Platform is Tech Mahindra’s solution for the future of engineering quality — an operating model where intelligent systems augment human intelligence without replacing it. Instead of siloed automation efforts or a plethora of disconnected tools, AppGinieZ Agentic AI Platform brings an integrated ecosystem where:
- Humans focus on strategy, business risk, interpretation, and strategic governance
- Intelligent agents support with deeper insights, autonomous orchestration, and repeatable executions
- Teams gain higher confidence to balance between quality and velocity
- Testing adapts to change through self-healing mechanisms instead of breaking because of it
Essentially, the entire framework converges to a Quality Engineering (QE) organization that is resilient, highly responsive, and fully aligned with contemporary engineering realities.
The platform uses advanced multi-agent and reasoning-driven principles; however, the value lies not in the algorithms but in the symbiotic flow that enables seamless planning, design, execution, observability, and analysis in a continuous, intelligent manner.
The Essence of Symbiotic Intelligence in QE
Symbiotic Intelligence is the core of AppGinieZ Agentic AI Platform. It’s grounded in three ideas:
1. Human intelligence is at the center.
Domain knowledge, risk assessment, and quality mindset of SMEs are not replaceable.
2. Intelligent agents remove the heavy and repetitive manual tasks.
They support teams with faster analysis, deeper coverage, and precise recommendations.
3. Continuous learning for both humans and agents.
Every test run, defect, and user journey and behavior becomes an opportunity to refine the next release.
This synergy elevates engineers' roles to the next level while reducing operational overhead that traditionally hampers velocity.
The Vector Squad Model: 10x Productivity through Human + AI Collaboration
To unlock the potential of Agentic AI, organizations must move beyond isolated automation and embrace vector squads—balanced, self-contained teams in which AI agents and humans collaborate seamlessly.
What is a Vector Squad?
A vector squad is an AI-agent-led service pod designed to execute a cluster of test activities. Each squad combines the power of AI Agents and Human Experts (SMEs):
- AI Agents: Orchestrate processes, automate repetitive tasks, and interact with both software and humans.
- Human Experts: Validate outcomes, handle exceptions, and provide domain expertise.
This model enables:
- End-to-end orchestration of test planning, execution, and validation.
- Parallel, 24/7 operations with multiple tasks running simultaneously.
- Rapid adaptation to changing requirements and environments.
Example: Regression Test Squads
- Human Experts provide Domain knowledge, risk assessment, and quality mindset.
- Planner Agents orchestrate execution.
- Testing Engineers collaborate and manage exceptions.
- Sub-agents handle specialized tasks (e.g., data provisioning, script healing).
- Human Executors step in where AI or tools fall short.
Operating Model and Measurable Outcomes
Tech Mahindra’s vector squads deliver autonomous testing services via service tokens—measurable units of delivery that underpin an outcome-driven, pay-per-use pricing model. This approach delivers:
- 30%+ cost efficiencies
- Predictable quality at predictable cost
- Continuous innovation and accountability
Squad Types and Functions
For each value stream, a standardized squad pack may be deployed. The following are some examples:
- Architecture Squad: Validation of Intake, ensuring compliance with Enterprise Architecture
- Planning Squad: Ensuring the availability of the right skills and resources to execute the release.
- Progression Testing Squad: Validation of New features.
- Non-Functional Testing Squad: Performance, security, compliance scenario creation & validation.
- E2E Integration Testing Squad: End-to-end scenario creation & validation.
- Automation & Healing Squad: Upkeep & healing of Automation suites
- Test Data squad: Setup, Provisioning, and Archiving of test data
- Test Environment squad: Configuration, Provisioning, and Validation of Test Environments
Highly Modular, Scalable, and Responsible Strategy
AppGinieZ Agentic AI Platform is built to integrate seamlessly, scale as needed, and operate responsibly, in line with domain-aware security and compliance requirements across the enterprise ecosystem. Although the workings remain proprietary, there are no sticky IPs, and the guiding principles are transparent:
- Modular and Scalable: Teams can implement and adopt capabilities progressively
- Tool-Agnostic: Leveraging industry-leading automation frameworks, test management tools, and CI/CD pipelines.
- Intelligence Layers: Adaptive reasoning and risk assessments, context-aware insights, and continuous learning through feedback loops, strengthening decision-making.
- Embedded Governance: Responsible and ethical AI governance and dashboards ensure transparency, traceability, and alignment with enterprise standards.
- Across Domains and Industry verticals: Designed to work across Banking & Financial Services, Telecommunications, Manufacturing, Healthcare & Life Sciences, and more.
In a nutshell, AppGinieZ Agentic AI Platform is designed and engineered to meet enterprises where they are now— to help them evolve to where they need to be.
Where This Journey Leads
The broader movement toward intelligent and agentic engineering is not just a trend but a structural shift. As enterprises embrace multi- and hybrid-cloud strategies, observability, experience reliability engineering, synthetic data, and agent-driven systems are emerging as primary differentiators for quality engineering.
AppGinieZ Agentic AI Platform, operated in a Vector Squad model, is built to help organizations navigate this shift with confidence by:
- Empowering high velocity and quality releases
- Reducing operational overheads on QE teams
- Improved coverage across complex ecosystems
- Enabling intelligent, context-aware, and real-time decision-making
- QE allowing the site to Xperience Reliability Engineering
The objective isn’t merely increased automation but enhanced business assurance. While automation speeds up processes and AI provides intelligent augmentation, it is Symbiotic Intelligence that drives business transformation.
The Future of QE Is Not Frameworks and Tools. It’s the Ecosystems.
As enterprises transform, the new standard for QE won’t be automation frameworks or siloed tools and utilities. It will be agentic-powered intelligent ecosystems — adaptable, context-aware, collaborative, and deeply integrated into the engineering lifecycle.
AppGinieZ Agentic AI Platform and Vector Squad models are examples of Tech Mahindra’s commitment to shaping this reality from the front, responsibly, intelligently, pragmatically, and innovatively.
We look forward to engaging with engineering and QE communities to challenge, explore, innovate, and co-create with us as we advance this new era of Quality Engineering.
