A practical, opinionated walkthrough for going from raw idea to shipped product using AI agents — covering design, architecture, implementation, and iteration.
This guide walks through a real process for building software products with AI agents. The core idea is that building great software requires multiple distinct roles — product strategist, market researcher, brand designer, systems architect, software engineer, QA tester. AI agents can fill all of these roles. Your job shifts from doing the work to orchestrating it.
The process is deliberately structured. Skipping steps — especially early ones — tends to produce generic, hard-to-extend results. The more context and intention you bring up front, the better your agents will perform downstream.
Think of yourself as the project manager. You define the vision, set the guardrails, and hand work between specialists. The specialists just happen to be AI.
Before a single line of code is written, you need to clearly define what you're building and why. This phase grounds everything that follows — it's the foundation that prevents you from building the wrong thing well.
Start with three questions: What platform does this live on? (web, mobile, both?). What are the three core features that define this product? What makes it worth building at all, given what already exists?
With those answers in hand, work with an LLM to produce two foundational documents.
This is your landscape document. It answers: what problem are we solving, who has this problem, what's our solution, who are our competitors and how do we stand out. Optionally: business model and revenue opportunities. Think of this as the context document for the world your product operates in — it's also a fantastic reference to feed AI agents later so they understand the space they're building in.
The PRD is the feature bible. It defines all the things the product does. Crucially, it also defines what's MVP — the minimum set of features that make the product real and usable — versus what's on the roadmap for later. This distinction is essential: you want to build each feature with the full vision in mind, but execute one piece at a time.
Ask your LLM to produce both documents given your idea and your three core features. Let it ask you clarifying questions. The richer these documents are, the better every downstream agent will perform.
Your product pitch and PRD describe what you're building. Your brand defines how it feels. This is the character of the thing — the design language, the emotional tone, the visual personality. It directly informs every UI decision downstream.
A good prompt here is to give the LLM your pitch document and PRD, share any UI references you find inspiring, and ask it to interview you — ask you questions that help it understand your vision — before generating anything. Questions like: Should this feel polished and corporate, or raw and expressive? Minimal or rich? Serious or playful?
Once it has a clear picture, have it produce a brand guideline document covering typography, color palette, visual tone, design principles, and the feelings the product should evoke. This is a living document — refine it, push back on it, give it more references until it feels right.
Brand guidelines serve as guardrails for every agent you use later. Without them, agents default to whatever "looks like a software product" — which tends to be generic. With a well-defined brand document, your agents have explicit rules to follow that produce something with real character and consistency.
With your PRD and brand guidelines in place, you now have enough to generate meaningful mockups before any code is written. This step is easy to skip — don't. Seeing a visual representation of the product before building it lets you catch design problems that are cheap to fix at this stage and expensive to fix later.
Ask the LLM to create a mockup of the MVP — the core features defined in your PRD — using the brand guidelines as the design language. You can ask for HTML prototypes, design descriptions, or use a visual design tool like Pencil.dev which natively integrates with agents and supports design tokens.
Evaluate what you get. Note what you like, what feels off, what's missing. Iterate on the mockup with feedback until the design direction feels solid. This is a creative conversation, not a one-shot prompt.
Once you have a mockup, you can share screenshots directly with building agents (Codex, Cursor) later. Showing an agent what to build — rather than only describing it — dramatically improves fidelity. Keep your mockups or export screenshots specifically for this purpose.
Pencil.dev — agent-native visual design tool with design token support. Works well alongside Claude and Codex. Figma — industry-standard if you want to design manually and hand off to agents. Claude — capable of producing solid HTML/CSS mockups directly in conversation.
Now you build the foundation — not the product itself, but the technical skeleton it will be built on. Think of this like laying the structural framework of a building before the walls go up. Doing this deliberately saves enormous amounts of pain later.
Give an LLM your PRD and platform targets and ask it to scaffold the project architecture. Tell it to ask clarifying questions about data structures, authentication, security, and infrastructure before it proposes anything. A well-structured prompt here might look like:
"Given these product requirements and the platform targets (web + iOS), I want you to propose and scaffold the project architecture. Ask me any questions you have about data design, authentication, security, and infrastructure before proceeding."
Agents will typically recommend a frontend framework (e.g. Next.js, React Native), a backend framework (e.g. Node/Express, FastAPI), and a database solution (e.g. Supabase, Firebase, Postgres). These three layers form the core of most software products. Pay attention to the recommendations — push back if something doesn't fit your needs or technical comfort level.
If your product involves user accounts, stored data, or any persistent state, you need a database strategy from day one. Don't defer this. Agents can recommend the right solution for your use case, but you need to own this decision early. Data security and architecture choices made here are very difficult to change later.
Before you write a single line of code, initialize a Git repository and connect it to GitHub (or equivalent). Version control gives you a complete history of every change, the ability to revert to any previous state when something breaks, and a documented record of architectural decisions over time. When an agent makes a change that breaks something, being able to roll back is invaluable. Many agentic coding tools (Cursor, Codex) integrate with Git natively — use this.
This is where the product gets built — one feature at a time. The key principle here is scope control. Don't try to build everything at once. Pick the first MVP feature, build it well, then move to the next. Each feature should be small enough that an agent can complete it in a single focused session.
The workflow for each feature follows a consistent pattern:
Claude tends to excel at upstream, design-oriented thinking — producing rich, nuanced documents and plans. Codex and Cursor are optimized for codebase execution with large token budgets well-suited for code-heavy sessions. Splitting the work by strength — and across different agents' token budgets — tends to produce better results than doing everything in one place. That said, all of these tools are capable of building, and you should use whatever works best for your workflow.
After each feature build, you test before moving on. Testing happens at two levels: automated and manual.
Agentic testing should be included in your implementation plan. Ask the building agent (Codex, Cursor) to write tests as part of the implementation — unit tests for key functions, integration tests for critical flows. Agents are good at this and it's far easier to write tests alongside the code than to retrofit them later. A well-written implementation plan should explicitly instruct the agent to include a testing step.
Manual testing is also essential. Use the feature yourself. Try to break it. Test the flows a real user would follow — creating an account, completing a core task, hitting edge cases. Note everything that doesn't work or look right.
Group your feedback before sending it. Rather than one fix at a time, collect three to five issues and address them together — it's more efficient and produces more coherent changes. Screenshots are your best tool: when something looks wrong visually, a screenshot tells the agent exactly what you're seeing far faster than a text description. For functional bugs, describe the steps to reproduce and the expected vs. actual behavior.
They will. Especially when building multiple features at once or making large changes. This is normal. Smaller scopes per session reduce breakage frequency. And when something does break significantly — this is exactly why version control matters. Being able to check out a previous commit and start from a known-good state is far faster than trying to debug a broken codebase.
Once a feature is working and feels right, commit the state to Git with a meaningful message, then move on to the next feature and repeat the cycle.
As your project grows, agents lose track of what's been built, why decisions were made, and how things are connected. Two tools keep this from becoming a problem: context documents and skills.
After your first major feature is complete, ask an agent to analyze the codebase and produce a context document — a markdown file that captures the current architecture, key decisions, naming conventions, do's and don'ts, and the overall shape of the project. Going forward, include this document in every new prompt when starting a new feature or major change.
The context document is a living document. After every major feature or significant change, update it. You can instruct your building agent to do this automatically — include a note in the context document itself that says it should be updated after major changes. This creates a self-maintaining record that any agent (or future you) can use to get up to speed quickly.
AI agents have no persistent memory between sessions. Without a context document, every new session starts from scratch — the agent is blind to every decision that came before. A good context document is the antidote: it tells the agent exactly where it is, how the codebase works, and what rules to follow. It's especially valuable if you switch between tools or come back to a project after time away.
Skills are instruction files — typically markdown — that teach an agent how to do a specific type of task. Where a context document captures your project's state, a skill captures best practices for a category of work. Think of them as standing operating procedures that any agent can read and follow.
Examples of useful skills: a frontend design skill that gives explicit rules for how UI should be built and styled, an architecture skill that defines how your codebase should be structured, a brand design skill that encodes your visual and tonal identity in precise rules an agent can act on.
Skills can be created, found, or generated. Some agent platforms ship with built-in skills; others let you write your own. You can also ask an agent to research a topic deeply — say, mobile app design best practices — and then synthesize that research into a skill file you can reuse. The more explicit the instructions in a skill, the more consistent and high-quality the agent's output will be.
Skills work differently across platforms — how they're stored, referenced, and applied varies. It's worth reading the documentation for whatever tools you're using to understand how to set them up. Searching for community-built skill libraries for your tools of choice is also a good starting point.
That's the full loop. Strategy → Brand → Design → Architecture → Build → Test → Maintain context. Then repeat for each new feature.
The process scales well — small features can compress several of these phases, while large or complex features deserve the full treatment. The consistent thread is intentionality: the more clearly you define the vision upfront, the better every agent performs when it's time to execute.
The tools will keep changing. The principles — define clearly, build incrementally, stay grounded in context — won't.