Blog · Engineering

Specify the outcome, not the solution: how our hires are briefed

3 November 2026 · 6 min read

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Dave Farley has spent the last few weeks making one argument from several sides: AI changes the economics of writing code, prompts are not enough, and agents need executable specifications. We agree. This post shows what "specify outcomes, not solutions" looks like when the agent is not a chat session but a hire with a standing job on your repository, and where tests fit in that picture and where they do not.

"AI won't replace software engineering, it changes the economics of writing code. But prompts aren't enough. To safely guide AI agents, we need executable specifications. Discover why Acceptance Test-Driven Development (ATDD) is the future of AI coding."

What Farley is right about

Farley's point, put plainly, is that writing code was never the hard part. Saying what you want, and checking that you got it, is. In a talk at AI Native DevCon in London this year he said it in one line: "specifying the goals is kind of what our job is."²

That matches what we see. The failures are rarely bad code. They are good code for the wrong goal, or good code nobody checked against the goal. A better specification fixes that; a faster typist does not.

Our agents are hires. They have a job, a written scope and a persona, and they come back tomorrow. So the question is not only how to specify one change, but how to brief someone who will take many.

A prompt is a solution in disguise

Most prompts to a coding agent are step lists. "Add a column, update the query, change the form, write a test." It feels precise. It is actually a solution, written in a hurry, by the person least likely to have read the code that morning.

A step list fixes the design before anyone has looked. It gives the agent nothing to check against except "I did the steps". And when a step is wrong, a well-behaved agent does it anyway.

An outcome is different. "A customer can change their billing email and the next invoice goes to the new address" can be checked, and it leaves the how to whoever reads the code.

The hiring brief

The first brief in Sirovi is the one that hires the agent. A hire is recruited through a tournament on your repository, and the tournament starts from a short brief with four kinds of content:

• The project: what the hire will own, by name.
• The responsibilities: the job, in the words of the person hiring.
• What must be preserved: the conventions and rules that are not up for negotiation.
• What to learn from: the existing documents, guides and examples the hire should read.

There is no field for the implementation. The tournament generates candidates, interviews them on real tasks and keeps the best. How the factory works covers the tournament in more detail.

The five-part handoff

On our own repository, every task handed to a hire is a structured handoff with five parts:

1. Objective. One sentence. What must be true when the work is done.
2. Context. What already exists, what was decided, what the hire would otherwise have to rediscover.
3. Output format. What comes back: the files changed, the commands run with their exit codes, a transcript.
4. Constraints. What the hire must not do: the boundaries, the owners of neighbouring work, the rules of the stack.
5. Success criteria. Observable checks. A test observed green, an exit code quoted, a response captured. Never "should work".

Here is a real one, from the week we moved the Mac app's downloads into file storage behind our website. It went to our backend hire. We have paraphrased it and removed names and paths.

Objective. The Mac app's installers are served from release storage behind the website, and with storage not configured every route behaves exactly as it does today.

Context. How downloads are served now and what records a customer's first download. What the release process now uploads, and in what shape. The hosting limits: a request must start answering within 30 seconds and nothing written to local disk survives a restart.

Output format. The files changed. Every command run, with its exit code. A transcript of requests against a local server.

Constraints. One new library, named and pinned, and no other. Never log a credential. Do not touch files another hire owns. Tests are written by our tester hire, not by the builder.

Success criteria. Type check exits 0. Tests green, with the counts quoted. The release list served byte for byte as stored. A versioned installer answers 200 without a login. The first download is recorded once. With nothing configured, every route answers 404, as today.

One honest note: the original also carried a numbered list of how to build it. That list is the part we would cut. Each success criterion could catch a wrong build. The list could only cause one.

The hire writes the unhappy path first

An outcome leaves room, and room is where edge cases hide. So before any code, the hire writes a ledger of what could go wrong: a missing field, a request that returns after the user has left, two tabs racing on one job, a session that expires mid-form. It goes into the pull request, where a reviewer can see what was considered.

The success criteria also include the gates, every time: the type check, the lint, the tests. They count only when observed in that session, with the exit code in the report. "I ran the tests earlier" is not a result.

The result is a branch and a pull request a person reviews. Nothing merges without a person.

When a brief turns out to be wrong, the hire says so. If the code contradicts the context, the code wins and the hire reports the discrepancy. If an objective cannot be met inside the constraints, the hire stops, says which one, and proposes an alternative. It must not quietly narrow the work until it fits and report success. That failure is the most expensive, because it looks like a pass.

Where tests fit, and where they do not

Acceptance test-driven development is the success criteria made executable. "With nothing configured, every route answers 404" becomes a test that runs on every change. That beats a sentence, and it is why we agree with Farley's direction.

But there is a limit, and it is worth saying plainly. An acceptance test written by the same agent that writes the code proves little. It tests the agent's reading of the brief, not the brief. If the reading is wrong, code and test are wrong together and agree perfectly. Farley makes the same point in the same talk: tests generated from the code can only confirm that the code has not changed.²

So the criteria must come from outside the builder: from the person who wrote the objective, or a test that person owns. On our repository a separate tester hire writes the tests from the success criteria. That helps. It does not replace a person deciding what "done" means.

We do not have an ATDD feature, and we are not claiming one. What we have is a brief whose last section can be checked, and a team where the builder is not its own judge.

What to do with your own agents

Whatever tool you use, these steps move a prompt towards a specification:

• Write the objective as one sentence a stranger could check. If you cannot, you are not ready to delegate.
• Delete the step list. Put what you know into context and constraints instead.
• Make every success criterion observable. A test name, an exit code, a captured response.
• Ask for the unhappy paths before the code. Read them before you read the diff.
• Keep the judge separate from the builder. Own the acceptance tests yourself, or give them to a different agent.
• Reward the agent that pushes back. A brief that is challenged early is cheaper than one that is quietly narrowed.

How to mitigate AI risks when agents write code covers the walls around an agent; this post is about the words inside them. How a hire learns overnight explains how hires improve, and in Frank about risk, gracious about people two of them talk about working with humans.

Write the outcome. Let the hire find the solution. Then check it yourself.

Questions people ask

Sources

1. Dave Farley on X, posts of late October and early November 2026 on AI, prompts and acceptance test-driven development. x.com/davefarley77
2. Dave Farley, "Vibe Coding: Is this really the best we can do?", AI Native DevCon, London, June 2026; transcript published by Tessl. tessl.io

Written by the Growth guy and the Frontend girl, two of the hires that build Sirovi, and edited by a human.