From git diff to First Customer
The stack after your AI coding agent finishes.
Guides / AI engineering
The stack after your coding agent finishes
CODING AGENT → CRITIQUE → VERCEL → APPGROWKIT → OMENTIR → CUSTOMER
An AI coding agent can produce a working product in a day. That is not a business. The useful stack after “done” is independent review, a real deployment, presentation that matches the product, outbound that finds the people with the problem, and a loop that feeds what they do back into the next change.
Your coding agent finished the feature. That was the easy part.
There has never been a cheaper time to build software. A founder can open Claude Code, Codex, Cursor, or another AI coding agent, describe a product, connect a database, add authentication, build a landing page, and have something recognisable as a SaaS before the day is over.
The bottleneck has moved. The question is no longer just “can AI build this?” It is: can you trust it, ship it, present it, and get someone to use it? The stack around the coding agent is becoming as important as the coding agent itself.
- 01
Build
Your coding agent
Implement the change in the repository you actually ship.
- 02
Verify
Critique CLI
Independent review against the working tree, with evidence—not the writer grading itself.
- 03
Deploy
Vercel
Turn the commit into a preview, then production, instead of a green local terminal.
- 04
Present
AppGrowKit
Make the store listing, screenshots, and first thirty seconds match the product you built.
- 05
Distribute
Omentir
Find the people who have the problem and start conversations, not templates.
- 06
Learn
The customer
Feed what they do and say back into the next agent pass.
Your coding agent is not your reviewer
Claude Code, Codex, Cursor, and other coding agents can produce surprisingly large changes from relatively small instructions. That creates an uncomfortable problem.
The agent that wrote the change usually has the strongest incentive—in context, not emotionally—to believe that its own approach is correct. Ask it whether the feature works and it can inspect the same implementation, follow the same assumptions, and arrive at the same conclusion. That is not independent code review. It is the writer grading its own homework.
This is why we built Critique. Critique is an independent review layer for AI-generated code. Your coding agent remains the builder. Critique comes afterwards. It works against the actual repository state, reconstructs what changed, runs relevant checks, and reviews the implementation independently.
Instead of asking “does this look okay?”, you want evidence for claims such as: does it build? Do the tests actually pass? Did the requested behaviour get implemented? Did the change break something nearby? Are there security or correctness problems? Does the diff contain something the original agent overlooked?
The distinction sounds small. It isn’t. As software development becomes increasingly agentic, generation and verification should become separate jobs. The same model can help you write and review code. Independence still matters when you are deciding whether something should actually ship. That is CLI code review: a second pass the builder does not own.
Critique CLI
npm install --global @critiquedotsh/cli
critique login
critique setup
critique finish --intent "Ship the billing pause flow" --repair pack --env auto --jsoncritique finish is a separate reviewer. It is not another prompt in the same session that wrote the patch.
Get the code somewhere real
A green terminal is not a customer environment. Eventually the application has to leave localhost. For web products, Vercel Git deployments are one of the shortest bridges between a repository and a live application.
Connect a repository and pushes can create deployments automatically. Pull requests and non-production branches receive Preview Deployments; the production branch produces production deployments. That is documented behaviour, not a metaphor.
Vercel CLI
vercel deploy
# share the preview URL, then:
vercel deploy --prodvercel without --prod is a preview after the first production deployment exists. vercel deploy --prod is the live cutover.
Make the product look like something people should trust
Working software and sellable software are not the same thing. Developers notice functionality. Customers notice everything: the icon, the landing page, the screenshots, the empty states, the onboarding, the first thirty seconds after opening the product.
This is especially obvious when shipping mobile apps. You can spend weeks building an excellent application and then represent all of that work with six mediocre App Store screenshots made twenty minutes before submission. That is a strange place to stop caring.
For mobile products, AppGrowKit is a useful example of AI applied after the engineering work is finished. Instead of designing every App Store screenshot from scratch, it can generate a screenshot set from real app screens and leave the result editable. AI gets you to the first version quickly. You still control the final result.
The broader lesson applies outside mobile. The coding agent can get the product working. That does not automatically make someone want it. Treat distribution assets as part of the product, not as a leftover afternoon.
Now comes the part nobody automated for you: customers
At this point you have working code, an independent review, a production deployment, and something presentable. Congratulations. You now have approximately zero customers.
This is where a lot of AI-built products die. The cost of producing software has collapsed faster than the cost of distributing it. There are more people capable of launching software, which means simply launching software becomes less interesting. The scarce resource becomes attention.
You still need to find the people who have the problem you solved, understand whether they are actually relevant, reach them, follow up, and turn a cold interaction into a conversation.
Outbound is becoming agentic too
This is where Omentir fits into the stack. Omentir applies agents to outbound rather than software engineering: finding relevant prospects, researching them, creating contextual outreach, and handling parts of the follow-up workflow. That is more interesting than another AI message generator.
Generating “Hey {{first_name}}, noticed you work at {{company}}…” was never the hard part. The useful system has to understand who is worth contacting, why they are relevant, what context matters, and what should happen next.
| Layer | Engineering | Outbound |
|---|---|---|
| Input | Repository | Market |
| Object | Change / working tree | Prospect |
| Reasoning | Context + tools + execution | Context + outreach + response |
| Check | Independent verification | Human-in-the-loop before send |
Your first customer should change the product
Getting someone to use the product is not the end of this pipeline. It is the beginning of the next one. The first ten users will find things your coding agent didn’t. They will click things in the wrong order, misunderstand something you thought was obvious, ask for features you never considered, and find bugs hiding behind combinations of state you didn’t test. Occasionally they will tell you that the thing you spent three days building does not matter to them at all. That is valuable.
- 01
Customer
Reality
Someone uses the thing, not the demo.
- 02
Feedback
You
Capture the miss: bug, confusion, or a feature that does not matter.
- 03
Build
Coding agent
Implement the next smallest change that answers that signal.
- 04
Verify
Critique
Independent evidence before the next preview.
- 05
Preview
Vercel
Exercise the change somewhere that is not localhost.
- 06
Production
The product
Ship, then go around again.
The post-agent stack
The coding agent gets most of the attention because watching software appear from a prompt feels magical. Increasingly, the interesting infrastructure lives around it. Model rankings will change. Your workflow matters more.
Why these examples
Each tool is here because it owns a bottleneck after generation—not because it needed a logo row.
The model is becoming the interchangeable part
There is a bigger trend hiding underneath all of this. For the first wave of generative AI products, the model itself was the feature. You sent text in. The model generated text out. Today, increasingly capable models are being surrounded by infrastructure that lets them operate on actual systems.
A coding agent does not merely generate TypeScript. It navigates repositories, edits files, invokes tools, executes commands, and reasons over results. An outbound agent does not merely generate sales copy. It operates on prospect data, context, sequences, and responses.
| Question | Why it matters |
|---|---|
| What context does it receive? | Garbage in, confident garbage out. |
| What can it do? | Tools turn a chatbot into an operator. |
| What happens when it fails? | Retries, bounds, and a human who can stop it. |
| Who checks its work? | Generation and verification should not be the same job. |
| How does it touch existing systems? | Repos, deploys, inboxes—not a paste box. |
| Where does a human remain in control? | Ship, send, and spend still need a person. |
AI made building cheaper. It did not make building a business easy.
Suppose an AI coding stack reduces the engineering effort required for an MVP by 80%. That does not reduce every other problem by 80%. You still need to decide what deserves to exist, make technical decisions, know whether the implementation works, deploy it, communicate why anyone should care, and find customers.
AI has not removed the company-building pipeline. It has compressed individual parts of it. Every time one bottleneck gets cheaper, another becomes more visible. Code generation got cheap, so verification matters more. Deployment got easy, so product quality matters more. Building got faster, so distribution matters more. Distribution gets automated, so understanding the customer matters more.
The coding agent saying done is not the end of the process. It is where the interesting part starts.
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