Abstract
In an era of escalating application complexity and rapid release cycles, Quality Engineering (QE) has become a strategic imperative, with 79% of organizations identifying it as a top priority over the next 24–36 months. The emergence of Agentic AI—autonomous, goal-driven agents—is revolutionizing this landscape by moving beyond simple task automation to orchestrating entire development pipelines and self-healing test scripts. However, while the potential for amplified productivity is vast, this "AI frenzy" introduces significant risks, including model hallucinations, data privacy vulnerabilities, and fragile automation foundations that can undermine organizational trust and scalability.
To harness the full power of autonomous testing, enterprises must transition to an "augmented autonomous" approach that integrates skilled human oversight with AI agents. This IDC Spotlight underscores that the most successful strategies will leverage "human-in-the-loop" controls to provide the necessary judgment, domain expertise, and ethical governance. By adopting innovative models like Tech Mahindra’s Vector Squad—which blends autonomous agents with expert human stewards through a flexible, outcome-based "service token" model—organizations can achieve faster release cycles and broader coverage while maintaining the rigorous control required for digital assurance in a multicloud world.
Key Highlights
Agentic AI’s Impact on Quality Engineering
Organizations are turning fast to agentic AI to accelerate test generation, improve defect detection, and enable autonomous, self‑healing automation driven by rising application complexity and the need for faster, more reliable ecosystems.
Human‑Guided Oversight Remains Crucial
IDC highlights that while agentic AI introduces risks such as hallucinations, false positives, data quality issues, and privacy challenges, these factors make human‑in‑the‑loop controls crucial to ensure accuracy, stability, and trust in AI‑driven workflows.
Vector Squad Model, A Balanced Agentic AI and Human Approach
Our Vector Squad model strikes the right balance by blending autonomous AI agents with skilled engineering talent. This ‘agentic AI and human’ structure delivers speed, broader coverage, and self‑healing capabilities while maintaining governance, human domain expertise, and reliable decision‑making.
Outcome‑Driven Delivery Through Service Tokens
Our Service Token framework measures quality engineering outcomes such as test coverage, build success rate, defect escape rate, and mean time to complete, offering predictable, transparent, and scalable agentic AI‑enabled delivery.
IDC’s Guidance for Scaling Agentic AI in Quality Engineering
The paper recommends that enterprises define clear AI goals, assess current automation maturity, begin with high‑value use cases, and establish strong governance structures in place to scale autonomous testing across the application lifecycle.