AI Coding Tools Require Integrated Systems for Software Delivery

Engineering teams utilizing artificial intelligence to generate code are increasingly facing delivery bottlenecks as testing and review processes lag behind, according to Tushar Mathur, CEO of Forge Code. While AI agents can accelerate individual coding tasks, they do not automatically streamline the broader software development lifecycle.

Traditional Development vs. AI Automation

Traditional software engineering focused on efficient coding and architectural design to minimize implementation effort. Mathur stated that as AI takes over coding duties, bottlenecks are shifting toward review backlogs and extended testing cycles. Simply adding more AI agents or hiring additional engineers can increase coordination costs and overwhelm existing systems with unverified code.

The Software Factory Model

At the TechSparks 2026 conference, Mathur proposed a “software factory” approach to coordinate AI agents across the entire development lifecycle. This model begins with triaging requests and creating specifications, followed by automated implementation and verification. Human engineers intervene only when high-level decisions or feedback are required.

Closing Feedback Loops

The system monitors production environments to gather insights that inform future development tasks. This creates a feedback loop allowing the factory to identify errors and optimize workflows autonomously. During his presentation, Mathur noted that Forge Code operated approximately 100 agents simultaneously to manage feature development, compatibility, and bug detection.

Current Status of AI Integration

Effective AI-native engineering requires adhering to six core principles: autonomous operation, scalability, cost efficiency, measurability, self-improvement, and alignment with business goals. Mathur emphasized that organizations must shift their focus from treating requests as isolated coding tasks to prioritizing business outcomes, such as customer retention and compliance.