Too much code, too little shipped: Why AI needs a software factory

A software factory is an AI-native system that coordinates work across the entire software development lifecycle. It handles requests, specifications, implementation, verification, deployment and production monitoring. This matters because faster coding alone can create more unfinished work instead of faster delivery.

An AI coding assistant mainly helps with individual coding tasks, such as writing features or fixing bugs. A software factory connects those tasks into a managed workflow. Forge Code’s process begins by triaging requests and creating specifications, then uses agents to build and verify changes before deployment. Engineers step in when decisions or feedback are needed.

The factory also learns from production results. It uses those signals to improve workflows and generate future work. Mathur identified autonomy, scalability, cost efficiency, measurability, self-improvement and business alignment as key principles. The goal is not more code, but reliable products and measurable business results.

Forge Code said it was running around 100 AI agents in the background during Tushar Mathur’s TechSparks 2026 presentation. That figure showed the scale of the software factory model being demonstrated. The agents were not described as performing only one repeated coding task.

Mathur said the agents were working on features, compatibility and bug detection. Within the broader factory, agents also support request triage, specification, implementation and verification. Human engineers remain involved when decisions or feedback are needed, so the system is not presented as an unattended replacement for people.

The larger point is that adding agents alone does not guarantee faster delivery. Without effective feedback loops, more agents can generate code that overwhelms review and testing systems. Forge Code’s model attempts to coordinate the agents and connect their work to signals from verification and production.

Generating code faster does not necessarily speed up the software development lifecycle because coding is only one stage of delivery. New changes still need review, testing, coordination and deployment. If those stages cannot absorb the extra output, the overall process slows even while code production accelerates.

The article describes several effects. Pull requests can pile up waiting for review. Testing cycles can become longer, and existing regression suites may fail to catch the growing number of bugs. More engineers may add coordination costs, while more AI agents can produce code that overwhelms review and testing systems.

Mathur said the underlying problem is a lack of signal. A useful software factory creates feedback loops across the lifecycle. It verifies changes, monitors production and uses what it learns to improve future work. The real measure of progress is reliable software, measurable gains and business results, not code volume alone.

When AI produces more code than teams can absorb, the pressure moves to the later stages of development. Pull requests can accumulate because reviewers cannot examine every change quickly. Testing can also slow as teams process more work and try to validate interactions across many changes.

The article identifies several connected bottlenecks. Existing regression suites may miss more bugs. Teams can face longer testing cycles and increased coordination costs. Adding engineers may not solve the problem because more people require more coordination. Adding AI agents can make matters worse when their output overwhelms review and testing systems.

Mathur links these problems to a missing signal. Simply connecting automated steps does not create an effective software factory. The system needs verification, production monitoring and feedback that guide future tasks. That makes the workflow more selective and responsive, rather than turning higher code volume into a larger unfinished queue.

A software factory uses feedback to connect completed work with future development. Agents verify changes before deployment, while the system monitors what happens in production. These signals show whether the work behaves as expected and where problems remain. That matters because automation without feedback can produce code faster without improving outcomes.

For example, a detected bug or production issue can inform later tasks. The factory can use what it learns to improve its workflows and generate new work. The article describes this as a feedback loop: development produces signals, and those signals influence the next cycle of development.

This approach changes the factory from a chain of disconnected automated steps into a system that learns. Mathur said Forge Code was using agents for features, compatibility and bug detection. Engineers still provide decisions or feedback when needed, while the factory uses operational results to make future work more targeted.

In an AI-native software factory, engineers spend less time assigning every individual task. They monitor outcomes, review signals and improve the system that coordinates the work. They still step in when decisions or feedback are needed, so their role shifts toward supervision and quality rather than disappearing.

Business leaders also become participants in the workflow. They can contribute requests and priorities directly. The factory then translates organisational goals into a development backlog. Instead of treating every request as a coding task, it can prioritise work linked to outcomes such as customer retention or compliance requirements.

This arrangement connects technical work with business value. The factory’s principles include measurability, self-improvement and alignment with business goals. Engineers oversee whether the system produces reliable results, while leaders help define what results matter. The focus moves from directing more activity to achieving better software and useful organisational outcomes.

The software development life cycle, or SDLC, is the full process of turning a need into working software and learning from its results. In the article, that process includes triaging requests, creating specifications, implementing features, verifying changes, deploying them and monitoring production. It matters because coding is only one part of shipping software.

Each stage addresses a different risk. Requirements and specifications clarify what should be built. Coding creates the change. Review and testing check its quality and help detect bugs. Deployment makes the change available, while monitoring shows how it behaves in production. Skipping or overwhelming any stage can slow delivery or reduce reliability.

AI can assist across these stages, but the article stresses coordination and feedback. A software factory connects the stages, brings in human judgment when needed and uses production insights to improve future tasks. This turns automation into a learning system focused on outcomes rather than code volume.