Start with the next business milestone
Production readiness depends on the product, its users and the consequences of failure. A small internal tool and a customer-facing payment application do not need identical controls. Both need an explicit understanding of their intended use.
Define who will use the next release, what they must be able to do and which information the application will handle. Decide what evidence would make that release acceptable. This turns a broad request to “make it production-ready” into a reviewable scope.
Recover the product’s intended behavior
A prototype may have grown through a series of prompts and local fixes. Before expanding it, record its critical journeys, business rules, roles and data ownership. Check that the code matches those decisions.
Use the running application, code and founder knowledge together. Distinguish behavior that demonstrates the concept from behavior the product must retain. Identify assumptions that have never been tested with users.
A concise requirement baseline gives engineers and agents a stable reference for hardening the application without accidentally changing the idea.
Review access and data boundaries
Check how identity is established and how permissions are enforced. A hidden button is not a substitute for server-side authorization. Exercise attempts to read or change another user’s data, and examine how administrative actions are restricted.
Review input validation, file handling, secret configuration and the external services the application depends on. Understand what data those services receive and what happens if a request fails. The review should follow the actual product risks rather than a generic list of fashionable tools.
Test important behavior and failure paths
Start with the journeys that matter most to the user and business. Add checks for permissions, invalid input and interrupted operations. Make those checks repeatable so the next change can be evaluated efficiently.
AI can help generate tests, but tests must be assessed against the intended behavior. A test suite that reproduces the implementation’s assumptions may miss the same defect as the code.
Where the product uses AI-generated answers or decisions, define a representative evaluation set and a route for handling unacceptable outputs. The evaluation should reflect the task the customer expects the product to perform.
Make operation a deliberate capability
A release needs a repeatable deployment process, useful monitoring and a way to investigate failures. Decide who responds when something breaks and how a bad release can be reversed.
Where persistent data matters, define backup and recovery procedures and verify that recovery works. Understand dependencies, running costs and any limits that could affect the next milestone.
These practices should be proportionate. The aim is a product the team can operate responsibly, not an elaborate platform created before it is needed.
Choose what to retain, improve or replace
An assessment should produce a prioritized plan, not a reflexive rewrite. Preserve useful work, explain material risks and connect remediation to acceptance criteria. Some findings may block release; others can be scheduled after users provide more evidence.
The result is a clearer path from a compelling demonstration to a dependable product - and a foundation that remains understandable as it grows.
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