Your AI Agent Passed the Tests. Did It Build the Product?

AI coding agents have shown they can create code that works, passes tests, and appears legitimate within a pull request. However, merely having green test suites isn't sufficient. A product team must deliver a specific outcome for customers who pay, which originated from a product requirements document (PRD), evolved into user stories, acceptance criteria, security constraints, and product decisions.

The challenging aspect lies in ensuring that those elements remain consistent even after an agent has made fifty revisions to the code. The common failure mode is specification drift.

An agent starts with a clear request, but when it faces an awkward dependency, a failing test, or a missing detail, it modifies an implementation choice. Subsequently, it adapts a test, and further narrows the behavior until the test passes. While no single step may appear alarming, the product may no longer meet the original request. This drift happens without the agent being malicious or "hallucinating"; it merely needs permission to optimize based on the context it has access to—its files, tests, and commands.

For a prototype, this might be acceptable. Yet, for a customer-facing product, it can be costly. Tests provide evidence of execution, not product delivery. They confirm a defined check passed, but they don't inherently verify that the original user story was completed, that security constraints were maintained, or that the agent didn't subtly alter the requirement to simplify the check.

To address this, it's helpful to distinguish between two questions: did the agent perform the work successfully, and did the work still meet the product specification? The first question pertains to tool calls, diffs, builds, and tests, while the second revolves around traceability. Which PRD requirement does the change fulfill? Which acceptance criteria were validated? What constraints must remain intact? What changes occurred when reality prompted a trade-off?

Without this connection, human reviewers waste time reconstructing intent from the code post-factum. This is where much of the cost arises. The primary bottleneck is validation. While an agent can generate vast amounts of code rapidly, someone must still validate whether the code aligns with the product, whether a test accurately tests the intended functionality, whether an exception is justified, and whether the implementation has introduced any security or usability regression.

If these decisions reside only in a chat window or a team member's mind, they vanish as soon as a new context window begins. The team then repeats the same work: reading, guessing, asking, checking, and repairing.

A dependable AI software workflow should preserve more than just code. For every significant change, the team should be able to trace a short chain: the original requirement or user story, the implementation decision made by the agent, the work it performed, the verification proving the relevant outcome, and any unresolved risks or explicit trade-offs.

This doesn't imply turning development into bureaucracy; instead, it means keeping the product contract visible while agents operate. The objective isn't to prevent agents from making decisions. Rather, it's to make those decisions transparent, reversible, and accountable to the original intent.

This discussion highlights the underlying issue behind PAELLADOC. The goal is to establish a controlled product-building workflow where agents can operate swiftly without silently altering the brief. Code, decisions, security constraints, and verification must remain interconnected all the way to a deliverable that a real client can use.

The standard of success isn't "the agent finished"; it's "the product we promised is demonstrably the product we built." Feedback is welcome, particularly from teams using coding agents on real client work.