A recent piece by Andrea Laforgia on Expectation-Driven Development (EDD) made the rounds, and it deserves serious attention. The core argument is compelling: AI agents produce code faster than humans can meaningfully review it, so we need a structured protocol for specifying intent before implementation and demanding evidence of fulfillment afterward. The human developer transitions from author to editor — from writing code to evaluating it.
That framing is right. And the EDD workflow — write expectations in plain text, let the agent implement, ask the agent to prove it, challenge the evidence, iterate — is a real improvement over the current default, which is roughly "trust and hope the CI is green."
But EDD solves a specific problem: the gap between human intention and AI implementation. It does not solve the problem that comes next.
To make this concrete, picture a developer asking an agent to fix how discount codes are applied at checkout. The expectation is precise: discounts apply to the pre-tax subtotal, tax is calculated after, an empty cart returns zero rather than an error. The agent implements it, runs the test suite, and produces evidence — three scenarios with real numbers, matching exactly what was specified. The developer reviews the evidence adversarially, pushes back once on a stacked-discount edge case, gets a revised version, and is satisfied. This is EDD working exactly as intended.
