Manus AI Built Your Project. Here's What's Missing.

You described your product to Manus AI and it generated an entire project.

Files. Folders. Code. Configuration. A working frontend. A backend with routes. A database schema. It looked complete. It felt like magic.

Then you tried to use it.

Your description said “user authentication.” Manus built email and password login. Clean forms, proper validation, session management. Reasonable. But you needed role-based access control with team invitations, single sign-on for enterprise customers, and granular permissions per workspace. That’s not what you got.

Your description said “payment processing.” Manus integrated Stripe checkout. A nice flow — product selection, card entry, confirmation page. But you needed usage-based billing with metering, prorated upgrades, annual plans with discounts, and invoice generation for B2B clients. That’s three months of billing logic, not a checkout page.

Your description said “dashboard.” Manus built a grid of cards with numbers. You needed real-time data, role-specific views, configurable widgets, and export functionality.

Every feature Manus generated was a reasonable interpretation of what you said. None of them were what you meant.

This is the core problem. “Described” and “specified” are different things. A description communicates intent. A specification communicates behavior. Manus works from descriptions. Production software requires specifications.

The gap between what you described and what Manus built is where every bug in your application lives. Not syntax errors. Not crashes. Something worse: software that works perfectly but does the wrong thing.

If you’re sitting on a Manus-generated project right now, wondering why it feels 80% done but the last 20% seems impossibly hard, this post explains why. And what to do about it.

Why Manus AI output needs work

Manus is the most ambitious AI coding tool on the market right now. It doesn’t just write functions or components. It generates entire projects from natural language descriptions. That ambition is also its core limitation.

Natural language is inherently ambiguous. “Build a project management tool” means different things to different people. To a freelancer, it means task lists and time tracking. To an enterprise PM, it means resource allocation, dependency graphs, and Jira integration. To a startup founder, it means whatever their specific workflow demands.

Manus picks one interpretation and commits to it. It has to. It can’t ask you twenty clarifying questions before generating code — that would defeat the purpose. So it guesses. And its guesses are reasonable. They’re based on the most common patterns it’s seen in training data.

The result is coherent but assumption-dense. Every line of code reflects a decision Manus made on your behalf. Some of those decisions align with what you need. Many don’t. And the ones that don’t are buried deep in the implementation, invisible until you hit them in production.

This isn’t a criticism of Manus. It’s a structural limitation of generating software from descriptions. The tool is doing exactly what it was designed to do. The problem is that what it was designed to do isn’t the same as what you need it to do.

We’ve seen this pattern play out with other AI coding tools — Devin, Lovable, and others. The generated output looks like a product. It runs like a product. But it doesn’t behave like your product. The distance between “a product” and “your product” is where the real engineering work lives.

If you’re exploring how to work with Manus more effectively from the start, read our guide on Manus AI best practices. But if you’ve already got generated code and need to figure out what’s wrong, keep reading.

Five gaps in every Manus-generated project

After auditing several Manus-generated codebases, we’ve found the same five categories of problems. Every project has some combination of these. Most have all five.

Assumption density

Every ambiguous requirement in your description triggers a guess from Manus. A simple product description contains dozens of ambiguities. A complex one contains hundreds.

“Users can invite team members” seems straightforward. But: Can anyone invite, or just admins? Is there a limit? Do invitations expire? Can they be revoked? What happens if the invited email already has an account? Does the inviter get notified when the invitation is accepted?

Manus answers all of these questions. Silently. With defaults. Those defaults might match your needs. They probably don’t match all of them. And you won’t know which ones are wrong until a user hits the edge case.

Multiply this across every feature in your app. A typical Manus-generated project contains 50 to 100 silent assumptions. Each one is a potential bug report from a future user.

Generic patterns

Manus generates code based on common patterns. These patterns work for tutorials, demos, and prototypes. Production applications need patterns specific to their scale, their users, and their constraints.

Generic authentication works until you need multi-tenancy. Generic data fetching works until you need pagination with cursor-based navigation. Generic form handling works until you need multi-step workflows with draft saving. Generic error handling works until you need retry logic with exponential backoff.

The patterns aren’t wrong. They’re just the starting point, not the destination.

Shallow implementation

Features look complete from the outside. The UI is there. The happy path works. Click through the main flow and everything feels solid.

But there’s no depth. No edge case handling. No error recovery. No loading states for slow connections. No empty states for new users. No graceful degradation when an API call fails. No input sanitization beyond basic validation.

Shallow implementation is the hardest gap to spot because it’s invisible during demos. It only shows up when real users do unpredictable things. And real users always do unpredictable things.

No domain awareness

Manus doesn’t know your industry. It doesn’t know that healthcare apps need HIPAA compliance. It doesn’t know that financial apps need SOC 2 controls. It doesn’t know that education platforms need FERPA considerations. It doesn’t know that your specific market has specific expectations about how certain workflows should feel.

Domain awareness isn’t just about compliance. It’s about understanding what your users expect. A patient portal has different UX expectations than a developer tool. A B2B SaaS app has different data model requirements than a consumer marketplace. Manus treats all domains the same because it doesn’t know the difference.

Integration gaps

Each generated module works in isolation. The auth module authenticates. The payment module processes payments. The dashboard module shows data. Individually, they function.

But the connections between them are fragile. Does the payment module check the user’s role before allowing a subscription change? Does the dashboard respect the user’s permissions when showing data? Does the auth module properly propagate session state to every API call?

Integration is where complexity lives. Manus generates components. Your product is the integration of those components. That integration layer is almost always incomplete.

What a Manus project audit looks like

A health-tech startup came to us with a patient management system generated by Manus. The founder had described the product in detail — patient records, appointment scheduling, provider dashboards, messaging between patients and providers.

The generated project looked impressive. Clean UI. Logical navigation. Forms for every data type. Dashboard with charts. The founder had spent two weeks trying to get it production-ready and hit a wall.

Here’s what our audit found:

Patient data was stored in plain text. No encryption at rest, no encryption in transit beyond default HTTPS. For a healthcare application, this is a non-starter. HIPAA requires encryption of protected health information at every layer.

There was no audit logging. Healthcare applications need to track every access to patient data — who viewed what, when, and why. The generated code had no concept of access logs.

API endpoints lacked proper authentication middleware. Some routes checked for a session token. Others didn’t. A determined user could access patient records by calling the API directly.

Role-based access was binary — logged in or not. There was no distinction between patients, providers, and administrators. Everyone saw the same data.

The appointment scheduling had no conflict detection. Two patients could book the same slot with the same provider. No waitlist logic. No cancellation policy enforcement.

Here’s the thing: 70% of the generated code was usable. The UI components, the basic data models, the routing structure, the form validation — all solid. But the 30% that needed replacement was the critical 30%. It was the security, the compliance, the business logic that makes a healthcare app a healthcare app instead of a generic CRUD application.

That ratio — 70% usable, 30% critical replacement — is consistent across every Manus project we’ve audited.

How to close the gaps

If you’re sitting on a Manus-generated project, you have three layers of work ahead of you. The good news: you’re not starting from zero. The bad news: the remaining work is the hard part.

Gap analysis

Go through every feature Manus generated and identify the assumptions. Every form, every API endpoint, every data model, every user flow. Document what Manus decided and compare it to what you actually need.

This is tedious but essential. You can’t fix what you haven’t identified. Most founders skip this step and jump straight to fixing things they’ve noticed. Then they discover more gaps in production. Then more. The drip of post-launch bugs erodes user trust faster than a delayed launch ever would.

Selective rebuild

Keep what works. Replace what doesn’t. This sounds obvious but it requires discipline. The temptation is to rewrite everything from scratch because you’ve lost trust in the generated code. Don’t. That 70% of usable code represents real value. Throwing it away means throwing away the head start Manus gave you.

The key is knowing which 30% to replace. That requires experience. It requires understanding what “production-ready” means for your specific domain, your specific scale, and your specific users.

Domain hardening

Add the requirements Manus couldn’t know about. Compliance requirements. Industry-specific user expectations. Competitive feature parity. Operational requirements like monitoring, alerting, and backup procedures.

This is where domain expertise matters most. A generic developer can close syntax gaps. Closing domain gaps requires someone who’s built products in your space before.

How we complete Manus projects

We treat Manus output as a 60-70% head start. Not a finished product. Not a throwaway prototype. A meaningful acceleration that still needs meaningful engineering.

Our process for Manus-generated codebases follows four phases.

Gap analysis. We audit every module, every integration point, every assumption. We produce a document that maps generated behavior to required behavior. This typically surfaces 30 to 60 gaps ranging from minor (wrong default value) to critical (missing security controls). You see exactly what needs to change before we change anything.

Domain-specific hardening. We add the requirements your industry demands. For healthcare, that’s HIPAA controls, audit logging, and encryption. For fintech, that’s SOC 2 alignment, transaction integrity, and regulatory reporting. For B2B SaaS, that’s multi-tenancy, role-based access, and enterprise SSO. This phase transforms a generic application into a domain-appropriate one.

Integration testing. We verify that every module works with every other module. Not just the happy path — the edge cases, the error states, the race conditions, the failure modes. This is where most of the subtle bugs surface. A payment failure that doesn’t properly revert a subscription status change. A permission change that doesn’t invalidate cached dashboard data. The bugs that only appear when two systems interact under specific conditions.

Operational readiness. Monitoring. Alerting. Error tracking. Database backups. Deployment pipelines. Log aggregation. The infrastructure that lets you sleep at night after launch. Manus doesn’t generate ops. No AI tool does. But ops is what keeps your product running after the initial excitement fades.

This is full-stack development in the truest sense — not just writing code across the stack, but taking responsibility for the entire product, from generated foundation to production operation.

We’ve done this with output from Manus, Devin, Lovable, and other AI tools. The source of the generated code matters less than the rigor of the completion process.

Close the gaps before they close your runway

Every week you run a Manus-generated project in production without a proper audit is a week you’re accumulating risk. Security gaps. Compliance gaps. User experience gaps. Each one is a potential incident, a potential churn event, a potential blocker for your next funding round.

The head start Manus gave you is real. Don’t waste it by pretending the output is finished when it’s not.

Get a gap analysis. Know exactly what you’re working with. Know exactly what needs to change. Then make informed decisions about what to fix first, what to fix later, and what to live with.

We do gap analyses for Manus-generated projects in one to two weeks. You get a clear map of every issue, prioritized by risk and effort. No surprises.

Get a gap analysis for your Manus-built project.

Manus AI built your project but something’s off? Variant Systems helps founders close the gaps between AI-generated code and production-ready products.