Your AI coding agent says the task is finished. But did it actually build what you asked for?
Tammuz Dubnov (AI Product Development Expert & CTO) explains why AI-generated code needs more than a simple completion message before it can be trusted.
An AI coding agent might finish writing code and report that the task is complete.
But that doesn't necessarily mean the feature works.
Tests could still be failing, or the actual product might behave differently from what the code suggests.
That's why Tammuz emphasizes the importance of an AI agent harness that verifies the work.
Instead of simply accepting the agent's response, the harness can require it to run the feature, interact with the product, inspect screenshots, and perform actual browser testing.
If something doesn't work, the agent must go back and correct it.
The goal is to make AI coding agents follow a proper software development workflow, even when the user provides only a simple prompt.
Writing code isn't the same as building a working product. AI needs to test what it creates.
Would you trust AI-generated code without seeing the actual feature tested?
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