Once a system can reliably test and document a feature, the next question naturally follows: could it also help build one?
Testing and development have long been treated as separate stages — one verifies, the other creates. But if an automated testing system can understand what a feature is meant to do and can diagnose precisely where it fails, then it already possesses the reasoning needed to participate in development itself. That’s where the boundaries begin to blur.
AI as a Test-Driven Collaborator
Test-driven development has always relied on a tight loop between definition and validation. You describe what “working” means in a test, then write or adjust code until that test passes. The difference here is that agent can now participate in that loop — both writing the code and evolving the tests that guide it.
There are two main ways this can unfold.
Generate the Feature, Then Build and Refine the Test
In this approach, the agent begins by generating a complete implementation of the feature. Once the initial code exists, the agent begins to produce an acceptance test that defines what success looks like: the expected interface, user interactions, and outcomes.
If, during test generation, a step fails, the agent treats the resulting failure report not as the end of the process, but as feedback. It can repair the feature code to make the failing step succeed, then continue running the remaining steps. The process becomes a self-healing development loop — alternating between running tests, repairing code, and progressing until the full scenario passes.
Build the Feature Incrementally Through the Test
In the second approach, the agent starts with no feature at all — perhaps an empty feature file — and constructs both the test and the implementation step by step. As the first test step runs, any missing functionality triggers the agent to implement whatever code is required for that step to pass. Once successful, it proceeds to the next step, filling in new functionality as needed.
By the end of the test, the system has built enough feature code to satisfy that specific use case — a complete, validated slice of behavior derived directly from test execution. This setup embodies the essence of test-driven development, just with a new degree of automation.
The Human Role in the Loop
The developer’s role remains central but shifts in focus. Instead of writing every line, the human defines goals, reviews AI-produced results, and ensures alignment with design, performance, and style standards.
As the agent grows more capable, the developer’s focus rises to a higher level — shaping goals, defining quality standards, and refining the reasoning frameworks that guide the automation. The agent doesn’t remove human creativity but instead automates the groundwork that enables it.