A large US-based retail organization is using agentic artificial intelligence to reshape how its software engineering teams work across the entire development lifecycle. The director of software engineering, Prasad Banala, describes a focus on embedding autonomous and semi-autonomous agents into everyday workflows rather than treating artificial intelligence as a separate or experimental capability.
The team applies artificial intelligence to validate requirements at the outset of projects, using agentic systems to review specifications, check for gaps or inconsistencies, and provide structured feedback that engineers and business stakeholders can act on. The same agentic approach extends into testing, where artificial intelligence is used to generate and analyze test cases to improve coverage and identify issues earlier. These agents help accelerate issue resolution by correlating defects with requirements and code changes, giving developers faster, more contextual insights.
Throughout this adoption, the organization maintains strict governance and keeps humans in the loop, ensuring that artificial intelligence outputs are reviewed and approved by engineers and product owners. Measurable quality outcomes are treated as a prerequisite for scaling usage, with controls and oversight built into each step where agentic artificial intelligence operates. The result is a disciplined model that emphasizes reliability, compliance, and tangible improvements in productivity and software quality.
