What Nano Banana 2 and Agent Skills for Firebase mean for app development and online stores

AI becomes more useful when it enters the real workflow

In recent months, Google launched two interesting novelties for the digital development area: Nano Banana 2, the new image generation and editing model based on Gemini, and Agent Skills for Firebase, a system through which AI agents can work better with Firebase projects.

At first glance, they seem like two separate news items.

One is about AI-generated images. The other is about code, applications, and infrastructure.

But for small and medium-sized businesses, online stores, and mobile apps, the direction is the same: AI is no longer just an experimental tool. It is starting to enter production processes directly.

This doesn't mean AI solves everything on its own. Not by a long shot.

It means that certain things that previously required a lot of time, many iterations, and many explanations can be done faster, if there is a clear strategy behind them.

What is Nano Banana 2?

Nano Banana 2 is the friendlier name for Gemini 3.1 Flash Image, Google's image generation and editing model. Google promotes it as being fast, capable of creating high-quality images, and better at respecting context than previous generations.

The interesting part is not just that it can generate beautiful images.

The interesting part is that it can work with more context. It can use real information, better understand concrete topics, and turn ideas or existing content into more useful visual materials.

For an online store, this can mean:

• explanatory images for products;
• banners for seasonal campaigns;
• infographics based on a blog article;
• visual variants for social media;
• adaptations in multiple languages;
• quick concepts for testing before final design.

It does not replace the photographer, the designer, or the marketer. But it can significantly shorten the exploration phase.

Why does it matter for online stores?

In e-commerce, the problem isn't just having products listed on the site.

The real problem is quickly explaining:

• what you are selling;
• who the product is for;
• why it is worth buying;
• how it is used;
• what differentiates the product from alternatives.

Many online stores lose conversions not because the product is weak, but because the presentation is unclear.

Visual AI can help here. For example, an article about choosing a cream, a natural syrup, a technical product, or a service can be turned into a simple infographic. A buying guide can become an image for Facebook or LinkedIn. A long explanation can become a visual that is easier to understand.

But there is an important condition: the AI must be fed with correct information.

If the text, strategy, and positioning are unclear, the image will just be a prettier version of the same confusion.

Firebase AI Logic: when AI enters applications

Through Firebase AI Logic, developers can integrate Gemini models directly into web and mobile applications. This means that functions like image generation, image editing, or AI interaction can become part of the product, not just something done separately in an external tool.

For a mobile app, the examples can be very concrete:

• generating personalized images for users;
• creating visual materials for events;
• quick editing of user-uploaded content;
• generating visual cards for routes, activities, or products;
• localizing images and messages in multiple languages.

For a local tourism app, for example, the AI could generate visuals for attractions, routes, or events. For a sports or community app, it could generate personalized cards with results, challenges, or recommendations.

However, these functionalities must be treated carefully. When users can generate content, questions immediately arise about moderation, costs, copyrights, inappropriate content, and personal data.

This is where the difference between an interesting demo and a real product becomes very clear.

What are Agent Skills for Firebase?

The second novelty, Agent Skills for Firebase, is less visually spectacular, but may be more important for developers.

In short, Agent Skills provides AI agents with official instructions and context on how to work correctly with Firebase. Instead of an AI agent guessing a project's structure, Firestore rules, or deployment steps, it receives better-organized and more relevant information.

This can help with:

• configuring Firebase projects;
• implementing authentication;
• defining the Firestore structure;
• writing security rules;
• deployment on Firebase App Hosting;
• integrating AI functions through Firebase AI Logic.

For teams already using tools like Cursor, Claude Code, Gemini CLI, or other coding agents, this is relevant. It reduces the risk of the AI generating code that looks good but is wrong, insecure, or hard to maintain.

AI does not eliminate architecture

It is worth saying this directly here: the fact that AI can write more code does not mean architecture becomes less important.

On the contrary.

The faster AI can produce code, screens, functions, and automations, the more important it becomes for someone to decide:

• what is worth building;
• what is not worth building;
• where the risks are;
• what data is sensitive;
• what security rules are needed;
• how the system will be maintained in 6 or 12 months.

AI can accelerate execution. But fast execution in the wrong direction remains a problem.

For serious projects, the role of the technical team does not disappear. It moves more towards strategy, verification, integration, and quality control.

Where we see real value for companies

For us, these novelties are especially interesting in three areas.

1. Faster visual content for e-commerce

Online stores constantly need visual materials: banners, posts, explanations, campaigns, images for articles, and ads.

Nano Banana 2 can accelerate this process, especially when there is already an article, a product description, or a clear strategy. You don't start from zero. You transform existing information into visuals that are easier to use.

2. Faster prototyping for applications

In mobile or web projects, Firebase is already a good choice for MVPs, authentication, databases, notifications, and hosting.

If AI agents can work better with Firebase, the first versions of apps can be built faster. But this only makes sense if the MVP is well-defined. Otherwise, you quickly build something that shouldn't have been built at all.

3. Internal automations for teams

Not all AI applications need to be public.

Sometimes, the greatest value is internal:

• a tool that generates content drafts;
• a system that organizes prompts and examples;
• an internal assistant for documentation;
• a report or brief generator;
• a system that helps the team work in a more structured way.

For many companies, this is a healthier starting point than immediately launching an AI feature for customers.

The risks that should not be ignored

Any serious discussion about AI must also include the less spectacular part.

In the case of AI-generated images, the risks are clear:

• images that look good but communicate incorrectly;
• incorrectly generated texts in images;
• visual promises that do not reflect the reality of the product;
• brand consistency problems;
• questions related to copyrights and transparency.

In the case of AI-generated code, the risks are even more serious:

• incomplete security rules;
• poorly exposed data;
• uncontrolled cloud costs;
• architecture that is hard to maintain;
• too much dependence on code the team does not understand.

AI can reduce work time. But it must not reduce the level of responsibility.

How we would test these technologies

Before recommending such features directly in a client project, we would test them on internal projects or controlled prototypes.

A healthy test might look like this:

• we choose an existing blog article;
• we generate 3 infographic variants;
• we check the accuracy of the message;
• we adapt the visual for Romanian and Hungarian;
• we compare the time saved compared to a classic process;
• we decide if the result is good enough for publication.

For the Firebase part, the test could be a small MVP:

• authentication;
• Firestore database;
• simple roles;
• an AI function;
• security rules;
• controlled deployment.

The goal is not to prove that AI can do everything. The goal is to see where it saves time without creating technical debt.

Practical conclusion

Nano Banana 2 and Agent Skills for Firebase show a clear direction: AI is going deeper into the tools used by developers, marketers, and digital teams.

For companies, this can bring speed. But speed without direction is not a strategy.

Real value appears when AI is used in a clear process:

• with a business objective;
• with correct data;
• with human verification;
• with attention to security;
• with measurement of the result.

At Dalbe Digital Agency, we track these changes not to chase after every novelty, but to understand what is worth using in real projects.

AI can help an online store communicate better. It can help an app offer smarter features. It can help a team work faster.

But the human must still decide what makes sense, what is safe, and what brings real value.