ProductHarness

Product teams that build before they specify
ship better software.

Working in a developer environment with an AI coding agent, PMs can now generate working prototypes, iterate toward validated solutions, and gather real stakeholder feedback before engineering starts. ProductHarness is the framework that makes that work.

The New PM Capability

The prior model: PMs write requirements documents, hand them to design and engineering, and wait — sometimes weeks — for something to react to. That model has not changed in most orgs, even as AI coding agents have become standard. The result: agents that move fast receiving inputs from a process designed for a different era.

A PM working in a developer environment with an AI coding agent can generate a working prototype of a feature in the same session they're thinking through the product problem. Not a mockup. A working implementation — grounded in the org's actual standards — that stakeholders can use and react to.
Build Working prototypes in the developer environment SAME SESSION Validate Real stakeholder feedback before engineering starts FROM EVIDENCE Derive Requirements, AC, test cases from what was tested

What Makes It Production-Aligned

The capability to generate working prototypes exists in any AI coding agent. What makes those prototypes production-aligned — rather than throwaway — is context. ProductHarness encodes your org's standards in steering documents — files in the repo the agent reads before it generates anything: component library, API patterns, authentication approach, data conventions, CI/CD requirements. Every prototype, every generated artifact, follows your actual conventions by default. That's the harness in the name: like a test harness holds code steady while it's exercised, ProductHarness holds product work inside your org's standards while it's being built.

01
Build to org standards, not general defaults

Steering documents capture the decisions that took years to make. When the agent generates a prototype or code, those documents are in context. Every output follows your conventions — not what the agent would generate from general knowledge. Engineering inherits something they can extend, not rebuild.

02
Requirements from evidence, not speculation

When requirements emerge from iterative prototype work, they're grounded in what was built and tested. Edge cases appear when something is built and the edge is encountered. The repo is the source of truth; downstream systems — backlog, test management, wiki — receive from it automatically.

03
Accountability stays exactly where it is

The PM owns what gets built. The engineer owns the implementation. QA owns the quality signal. ProductHarness expands what PMs can do — it does not reassign the accountability for outcomes that already belongs to your team.

Explore the framework →

The Translation Tax

Every handoff in a traditional SDLC requires a human to convert context from one form to another. Requirements into stories. Stories into test cases. Design intent into engineering context. That overhead falls on the PM — and it compounds as organizations scale. ProductHarness removes it: requirements are authored once in the repo, and every downstream system receives from it automatically. Distribution is what happens after the solution is validated. It is not the point.

The full Translation Tax argument →