Why you need us in your AI dev project

Nick Beaugeard
3 days ago
14 min read

Launching a software product with advanced AI tools like Agentic AI and the OpenAI Codex CLI is an exciting frontier. But even as AI handles more coding tasks, there’s one element you must not leave out: an experienced software architect as the “human in the loop.” Throughout this guide, we’ll emphasise why non-technical founders need a skilled human orchestrator to translate business vision into technical reality. We’ll see how this hybrid human-AI model accelerates development with clarity and confidence. The message is simple and powerful: the combination of AI and an expert human guide is the next frontier of fast, successful product delivery.

In the sections on Preparation, Execution, Common Pitfalls, and Future Outlook, we integrate this theme with compelling examples and case-style anecdotes. By the end, you’ll understand how having a great software architect in the loop leads to clarity, speed, and ultimately a more successful MVP. Let’s dive in.

Preparation: Setting the Stage with a Human Orchestrator

Preparing an AI-driven MVP isn’t just about picking the right AI tools; it’s about laying a solid foundation where human expertise guides AI from the start. For non-technical founders, this preparation phase is critical. An experienced software architect can translate your business idea into a technical game plan – defining architecture, clarifying requirements, and planning how AI will be used. This human-led planning ensures the AI builds on a strong blueprint rather than making ad-hoc decisions that might later cause trouble.

For example, consider a simple prompt like: “Build a blog post API in Node.js.” An AI agent could generate a quick solution, but likely as a single, monolithic script with no structure: one file handling everything, no separation of concerns, and no proper validation or testing[1]. It might work at first glance – the app runs and replies to requests – but under the hood there’s no architectural rigour. Such an app would have no clear modules, be hard to scale or modify, and essentially bake technical debt into the system from day one[2]. Without guidance, the AI simply optimises for “make it run” rather than “make it right”[3].

Now imagine if you, as a founder, had an architect in the loop during preparation. They would start by asking the right questions: What features are truly needed in the MVP? How should data flow? What are potential load or security considerations? They’d plan the system’s structure and give the AI specific instructions (prompts) to build components in a modular, scalable way. If the AI suggests using an outdated pattern or skipping a critical layer, the architect can catch that upfront and adjust the course. This means before a single line of AI-generated code is run, many high-level decisions are made correctly – frameworks, file organization, database design, error handling strategy, and more.

Setting the stage this way pays off. The AI coding assistant becomes a powerful ally rather than a loose cannon. The architect essentially acts as a project maestro, dividing the work into clear tasks for the AI and verifying the initial outputs. For a non-technical founder, this is invaluable: you have someone ensuring the AI understands what you actually need. It transforms your vision (e.g. “an app to connect volunteers with charities”) into concrete development steps that the AI can execute on. In short, thorough preparation with a human architect means your AI starts off in the right direction, saving you from major rewrites later and ensuring the MVP truly aligns with your business goals.

Execution: Orchestrating AI Development with Human Oversight

When it’s time to build the product, the dynamic between the AI and the human architect becomes the engine of rapid development. Think of the execution phase as AI working at turbo-speed, with the human architect as the skilled driver steering the wheel. AI coding agents (like those in OpenAI Codex CLI or other “agentic” frameworks) can generate code for you at an incredible pace, tackling tasks that used to take days in mere minutes. But speed alone isn’t victory – it’s the human oversight that turns that speed into sustainable progress.

During execution, non-technical founders especially benefit from having an expert “in the loop.” The software architect serves as an interpreter and quality guardian. They craft effective prompts for the AI, asking it to produce code modules or solutions that fit the planned architecture. Crucially, they also review and test the AI’s output in real time. It’s common that an AI won’t get everything perfect on the first try – after all, it cannot fully intuit the subtle business rules or user expectations you have in mind[4]. There may be iterative back-and-forth: the AI writes code, the architect runs it and finds a bug or a missing piece, then refines the prompt or tweaks the code. This iterative loop is where the magic happens. The AI provides the heavy lifting, and the human ensures each piece is correct and cohesive.

Consider a hypothetical scenario: A founder wants an AI to build a customer support chatbot application. Without technical guidance, the founder might instruct the AI generally and get a bot that answers questions – but perhaps it’s not integrating with the company’s database correctly, or it’s overloading on API calls. With an architect guiding execution, they would break down the project: one agent to build the front-end interface, another to set up a secure backend, perhaps another to integrate a third-party API. As each piece is generated, the architect verifies it: Does the front-end code follow our intended user flow? Is the backend properly validating inputs? If something is off, the architect catches it early and adjusts. The result is a far smoother build process. Instead of a non-technical founder wrestling with confusing errors or unpredictable AI outputs, the architect handles those technical details, letting the founder focus on the big picture.

This human-AI teamwork drastically improves clarity and speed during development. The AI can churn out code fast, but the architect keeps everything aligned with the product vision. In practical terms, that could mean launching a functional MVP in weeks instead of months. Importantly, the code isn’t just quick—it’s guided to be clean and maintainable. Industry experience shows that AI coding assistants can accelerate delivery, but without close supervision the quality may suffer[5]. In our model, close supervision is exactly what the architect provides – every step of the AI’s work is monitored and fine-tuned.

Moreover, the architect ensures that technical decisions remain tied to business outcomes. They are constantly asking, “Does this feature or approach solve the founder’s problem? Is it the simplest way to deliver value to users?” This prevents the common issue of AI or junior developers getting lost in interesting but non-essential technical tangents. Instead, the execution stays laser-focused on what matters to the business.

In a nutshell, the execution phase in a human-AI hybrid model feels like having an expert navigator on a high-speed train. The journey is fast and efficient, but also controlled. You get the best of both worlds – rapid coding from AI and critical thinking from a human – ensuring that when the MVP is built, it’s not only done fast, but done right. As one tech writer put it, with these AI tools “the human role is now to understand what to ask the models for the best results, and to iron out the inevitable problems along the way”[6]. That’s exactly the role your software architect plays throughout execution.

Avoiding Common Pitfalls: Expert Guidance Makes the Difference

Building an MVP is never a completely smooth ride – there are bumps along the way. When using advanced AI agents, certain pitfalls can trip up non-technical founders if they go it alone. Here’s where having an experienced software architect as a guide truly pays off: they not only help speed things up, they actively shield you from common mistakes and unforeseen problems that could derail your project.

One major pitfall is the “it works, ship it” trap. AI-generated code often works on the surface – you ask the AI to build a feature, and it produces something that runs. But as we discussed earlier, beneath the surface it might lack solid architecture or best practices. A non-technical founder might see a working demo and assume all is well, only to later discover the app crashes under real user load or is impossible to extend with new features. An architect in the loop prevents this by digging into the AI’s output. They look “under the hood” and evaluate if the code is structured for long-term success or if it’s a brittle quick fix[7]. This proactive inspection is crucial: the architect can refactor or re-prompt the AI to improve code quality before minor issues become major headaches.

Another common pitfall is misalignment between business needs and what the AI builds. AI is extremely literal; it does exactly what you ask, not necessarily what you intend. For example, if a founder says, “AI, build me an e-commerce site,” the AI will do so but won’t inherently know priorities like security for payments, or that the user experience should be tailored to a target demographic. We’ve seen scenarios where an AI-built prototype lacked crucial elements (like input validation or proper error handling) simply because the prompts never specified them – something a non-technical person might not realise is needed. A skilled architect thinks of these implications from the start. They serve as the “translator,” turning business requirements (like “our platform must protect user data” or “it should handle growth if we suddenly get many customers”) into technical requirements that the AI can implement. Without this translation, you risk ending up with a product that technically functions but fails in real-world usage or leaves serious gaps (like security holes or scalability issues).

Perhaps the most dangerous pitfall is overconfidence in the AI. Modern AI agents can be astonishingly confident, even when they’re wrong. They might produce a piece of code or a system design that sounds plausible but is subtly flawed. Non-technical founders may not catch those subtle flaws. For instance, an AI might choose an approach that “favors simplicity or familiarity over best practices”, adopting a pattern that works for a simple demo but isn’t suitable for a growing product[8][9]. Without a human expert to second-guess the AI, these choices go unchallenged until they cause trouble down the line. An architect provides that essential check. They approach the AI’s output with a healthy skepticism, asking the same tough questions they would if a junior developer wrote the code. Is this secure? Will it scale next month when user count doubles? Does it follow the industry’s proven methods or are we veering off-track? This kind of critical oversight ensures that “brittle and hard-to-scale systems” don’t quietly take root in your codebase[10]. In fact, industry experts warn that without an experienced hand ensuring the code is sound, you’re almost guaranteed to hit a wall when you try to expand or scale that AI-generated application[2]. The architect’s guidance steers you away from that wall.

Let’s illustrate with a brief case study of pitfalls avoided by having a human in the loop. Imagine two startups with a similar idea: an AI-driven personal finance app. Both founders are non-technical and use an AI coding assistant to build their MVP. Founder A goes solo with the AI. It quickly generates a working app that connects to bank APIs and shows budgeting charts. Impressed, Founder A onboards some early users. But as soon as 1000 users sign up, the app starts crashing. It turns out the AI had made all network calls synchronously and without proper error handling, so under load the system choked (something Founder A never thought to check). Additionally, a security review later reveals the AI-coded app didn’t encrypt sensitive user data properly. In contrast, Founder B partnered with an experienced software architect from day one. The architect insisted on a quick “architecture check” when the AI proposed a design. They spotted the lack of asynchronous handling and instructed the AI to implement a more resilient pattern (using background jobs and retries for calls). They also ensured that encryption libraries were used wherever sensitive data was stored or transmitted. Yes, Founder B’s MVP took perhaps a day or two longer to tweak and test with the architect’s changes – but when they launched to 1000 users, it ran without a hitch, and investor due diligence was a breeze because the architect had built-in security best practices. The difference was night and day. Founder B avoided the painful rework and credibility damage that Founder A experienced, all because an expert was there to foresee and prevent those pitfalls.

This story is hypothetical, but it reflects real patterns we see in AI-assisted development. Research and industry experience consistently show that having expert supervision dramatically improves outcomes. AI can increase development speed, but if it’s not guided properly, you risk quality issues[5]. Conversely, with “guardrails and solid prompting skills (driven by an engineer/architect’s expertise),” AI agents can build not just quickly, but well[11]. The architect’s role is exactly to provide those guardrails – catching mistakes, asking the right questions, and keeping the build on course. For non-technical founders, it’s like having a safety net below the tightrope of tech innovation. You can move fast, confident that someone knowledgeable will catch you if you stumble.

In summary, most pitfalls of AI-driven development come from missing the human element. Whether it’s architectural flaws, misinterpreted requirements, or overlooked best practices, these are problems a seasoned human in the loop can spot and solve. By investing in that human guidance, you’re not being “slower” or “less AI-driven” – you’re being smart. You’re ensuring that the fast progress AI offers is sustainable progress, leading to an MVP that stands on solid ground. And for a founder, there’s nothing more reassuring than knowing you’re building on rock, not sand.

Future Outlook: Embracing the Human-AI Hybrid Model

As we look ahead, one thing becomes increasingly clear: the future of software development is a collaborative dance between humans and AI. The days of building software purely by human effort are fading, but so too is the myth that AI will replace human developers entirely. Instead, the next frontier is this human-AI hybrid model – and non-technical founders who embrace it early will have a huge advantage in innovation.

What does this future hybrid model look like? In many ways, it’s an evolution of what we’ve discussed: AI taking on more of the grunt work and routine coding, while humans focus on high-level design, critical thinking, and creative problem-solving. One industry vision describes it as the 80/20 split – where AI might handle 80% of the code generation, and the top 20% of work (the tricky, novel, or high-stakes parts) remains with skilled humans[12]. In this scenario, the role of an experienced software architect or senior engineer becomes even more vital. They’re not writing every line of code – they’re curating, reviewing, and guiding the deluge of AI-produced code to ensure it all aligns with the product vision and quality standards. In fact, thought leaders predict that the professionals who thrive will be “those rare individuals who can understand, review, refactor, and create novel implementations with an expert eye for robustness, maintainability, and efficiency”[13]. In plain terms, that’s your human-in-the-loop architect.

For non-technical founders, this hybrid model is wonderful news. It means you don’t have to choose between hiring a big development team or trying to coax an AI to build your product alone. Instead, you can leverage AI as a force-multiplier for a lean human team. Your experienced architect might be able to deliver what used to require a whole team of coders, by intelligently delegating tasks to AI and reviewing the results. It’s a bit like how modern skyscraper construction uses powerful machines plus expert operators: the machines provide strength and speed, but the architects and engineers ensure that strength is applied in the right way to create a safe, functional building. Similarly, AI will be the heavy lifter in software, and human experts will be the architects literally and figuratively – designing, guiding, and validating the work.

Another angle to consider is how this human-AI partnership drives business agility and innovation. In a rapidly changing market, being able to iterate quickly is key. With the hybrid approach, you could try out new features or pivot your product much faster than traditional methods. The AI can whip up a prototype of a new idea overnight, and your human expert can evaluate it the next morning, ensuring it aligns with your business strategy before you invest further. This rapid cycle of idea → AI builds → human checks means you get clarity very quickly on what works and what doesn’t. It’s a competitive edge – you’re effectively running multiple “experiments” in code at high speed, but with a safety mechanism (the human review) that keeps those experiments from going off the rails.

The future outlook also suggests new roles and ways of working. We’re already seeing job titles like “AI software architect,” “prompt engineer,” or “AI project lead” emerging. These reflect the reality that orchestrating AI requires a blend of skills – understanding AI’s capabilities and limitations, plus deep software development know-how. Your project’s “human in the loop” might very well be one of these new-breed architects, who are as comfortable chatting with an AI about code as they are talking to you about user experience and business goals. They act as the bridge between the two worlds. As one architect put it, the collaboration is “symbiotic, with both parties learning and benefiting from each other”[14]. The AI learns from the guidance (it improves with feedback), and the human learns how to get more out of the AI. Over time, this synergy will only improve, as AI tools get better at taking nuanced direction and humans get better at giving it.

We should also talk about trust and control in the future landscape. Understandably, some founders worry: “If AI is doing so much, how do I trust the code? How do I maintain control over my product?” This is precisely why the human-in-the-loop model is so powerful. You maintain control through the human expert. It’s not a black box magically spitting out your app – it’s a well-managed process where the architect can explain and document what’s happening. If regulators or investors ask how a certain feature was implemented or how data is handled, you have answers because a knowledgeable person oversaw it. Google’s AI leadership has echoed this principle, noting that truly autonomous agentic systems still “must have a human in the loop” for oversight in any critical applications[15]. It’s about combining AI’s efficiency with human judgment and ethical considerations.

Looking forward 5 or 10 years, we expect this approach to become the norm. The tools will evolve – today it’s OpenAI Codex CLI, tomorrow it could be even more advanced agentic AIs – but the pattern remains: human creativity and decision-making augmented by AI speed and scale. Non-technical entrepreneurs will not be sidelined by tech complexity; instead, they’ll be empowered by these AI assistants as long as they also enlist the right human expertise to direct those assistants. In practical terms, the successful startups of the future might have very few employees, but among them will be an ace architect working with an array of AI agents to deliver incredible products in record time.

It’s an inspiring vision: imagine founding a company where you and a couple of experts can achieve what once required an entire IT department. By embracing the human-AI hybrid model, that’s within reach. AI is not coming for your idea – it’s coming to accelerate it, and the human architect ensures it accelerates in the right direction. This partnership is the frontier of innovation. It balances the creative intuition and strategic thinking of humans with the relentless execution power of AI. For you as a founder, it means less time lost in translation between your concept and a working product, and more time delighting customers and refining your business.

In conclusion, the future of building AI-driven MVPs is bright for those who harness this approach. The narrative has shifted: it’s no longer AI versus human, but AI plus human. By securing an experienced software architect as the human in the loop, you’re essentially future-proofing your venture. You get to ride the wave of cutting-edge AI development, with an expert hand on the wheel to avoid crashes. That’s a winning formula – one that promises not just faster or cheaper software, but better software, aligned perfectly with what your business and users need. And that is ultimately what turns an MVP into a successful product.

Sources:

Michael Chiaramonte, “The rise of vibe coding: Why architecture still matters in the age of AI agents,” vFunction Blog (July 2025) – discusses how AI “agentic” coding often lacks architecture and why human-guided prompts and oversight are essential[1][2][7][11].
Jason Arbon, “The Agentic Engineering Loop,” Medium (Apr 2025) – highlights the 80/20 future of development where AI does 80% of the coding and exceptional human engineers handle the critical 20%, focusing on architecture and aligning with business objectives[13][16].
Aleksander D’zhongarov, “The role of AI in software architecture,” Delta Source Blog (2023) – emphasises a Human-in-the-Loop approach: AI is like an apprentice that still requires active guidance and oversight by architects to ensure quality and alignment with goals[15][17].
Billy Perrigo, “AI Promised Faster Coding. This Study Disagrees,” TIME (Sept 2023) – notes that using AI in coding without proper guidance can sometimes slow down experienced developers, as the human must clarify prompts and fix AI mistakes, underlining the need for knowing how to work with AI[6][4].
Birgitta Böckeler (Thoughtworks), “Learnings from two years of using AI tools for software engineering,” The Pragmatic Engineer (Jun 2025) – reports that AI coding assistants can speed up delivery but “without close supervision, the impact on quality could be negative”, reinforcing the importance of expert oversight[5].