Although artificial intelligence is capable of generating code at unprecedented speeds, the process of testing, verifying, and deploying it into a working environment remains a separate complex task. Engineering teams are increasingly using AI agents to write code, fix bugs, and create new features, but faster coding does not guarantee faster software delivery.
As code generation gains momentum, the volume of checks increases, testing slows down, and teams find it difficult to coordinate the growing amount of changes.
At the TechSparks 2026 conference, Tushar Matur, CEO of Forge Code, highlighted this issue in his session 'Anatomy of a Software Factory.' He explained how AI-focused engineering organizations can move beyond automating individual coding tasks and start creating systems that manage the entire Software Development Life Cycle (SDLC).
Matur noted that just because code generation has become easier and faster does not mean the overall SDLC will accelerate. He reminded that the traditional development approach focused on writing code efficiently, requiring teams to define requirements, prepare specifications, and design architecture to reduce the load on the implementation phase.
However, since AI takes on much of the coding work, bottlenecks shift to other areas. Matur described a situation where organizations face a huge number of pull requests awaiting review, longer testing cycles, and more errors than existing regression suites can detect.
He emphasized that solving the problem does not necessarily involve hiring more engineers or launching more AI agents. Additional staff can increase coordination costs, and adding agents without effective feedback mechanisms can lead to code that overwhelms the verification and testing systems.
'The reason for this is the lack of signal,' Matur explained, clarifying why simply connecting automated steps does not create an effective software factory.
Matur presented Forge Code as a software factory that coordinates AI agents throughout the development cycle. The process begins with sorting requests, creating specifications, and implementing features. Then, agents check the changes before they are deployed, with human engineers intervening when decisions or feedback are required.
The system also monitors in the working environment, using the gathered data to inform future tasks. This forms a feedback loop, allowing the factory to identify issues, improve its workflows, and generate new work based on the experience gained. During the presentation, Matur reported that Forge Code used about 100 agents in the background to work on features, compatibility, and bug detection.
He outlined six principles for building an effective software factory: autonomy, scalability, cost-effectiveness, measurability, self-improvement, and alignment with business goals. The last principle is particularly important.
The factory should not operate as a task of writing code for every request, but rather be aimed at achieving specific outcomes, such as increasing customer retention or meeting regulatory compliance. Teams can then prioritize features based on the value they bring. In this model, engineers spend less time managing each task and more time observing results, analyzing signals, and improving the system itself. Business leaders can also provide requests and priorities directly, allowing the factory to translate organizational goals into a development backlog.
Code writing speed is good, but creating better software is the real victory. For engineering teams, the key is transforming AI-driven speed into reliable products, measurable progress, and real business results.