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With every new technology platform, the concept of an application shifts. Consider the difference between compiled apps during the PC era, online applications during the Web, and app stores during mobile. Now with AI it's happening again.

Before getting into the impact AI is having on applications, it's worth noting we still have downloadable desktop applications, Web applications, mobile app stores and everything in between. Technology platform shifts don't wipe out the past and they also don't happen overnight. So AI-driven changes, while happening fast, are going to be happening for a long time.

The basic components of an application have also stayed consistent for a long time. An application at its highest level is just running code and a database. The database stores the information an application manipulates and the running code allows you to manipulate it through input and output controls (user interface, auth, etc.).

As AI coding agents have gotten more capable, they've increasingly been able to handle more of the running code aspect of an application. Not only can they generate code, they can review it, fix it, and maintain it. So it's not hard to see how AI agents can be a self-sustaining loop.

As AI coding agents take on more and more of the running code aspect of an application, they increasingly need to create, update, and work with databases. Today's databases, however, were made for people to use, not agents. So we built a database system for AI applications called AgentDB designed for agents, not people.

AgentDB allows agents to manifest new databases by just referencing a unique ID. Instead of filling out a series of forms - like people do when creating a database. It also provides agents with templates that let them start using databases immediately and consistently across use cases. These templates are dynamic so as agents learn new or better ways to use a database, that information is passed on to all subsequent agent use.

With these two changes, the concept of an application is already shifting. But what if the idea of needing "running code" is also changing? By fronting an AgentDB database and template system with a remote Model Context Protocol (MCP) server: all you need is a URL plus an AI model to have an app.

In this video, I demonstrate uploading a CSV file of a credit card statement to AgentDB. The system creates a database and template, encapsulates both with a remote MCP server URL that you can add to any AI application that supports remote MCP like Claude, Cursor, Augment Code, etc. The end result is an instant chat app.

Through natural language instructions, you can read and write data immediately and consistently and ask for any variant of user interface you want. Most credit card websites are painfully limiting but now I can create the specific visualizations, categories, queries, and features I want. No waiting around for the credit card site to implement new code.

You also don't need a CSV file to make an app. Just tell an AI model connected to AgentDB what you want. It can use AgentDB to create a database, populate it, and then ensure anything you add to it includes the right information. Tracking the date, location, and cost of concert tickets? AgentDB will enforce all that info is there and if you add a new bit of data to track, it can update all your records (see video below).

You can try making your own chat app from a database or CSV file at the demo page on AgentDB to get a feel for it. There's definitely some rough edges especially when trying to add a remote MCP server to some AI applications (in fact, this whole step should go away) but it's still pretty compelling.

As I mentioned at the start, we don't fully know how the AI platform shift will transform applications yet. Clearly, though, there's big changes coming.

For AI applications, context is king. So context management, and thereby context engineering, is critical to getting accurate answers to questions, keeping AI agents on task, and more. But context is also hard earned and fragile, which is why we launched templates in AgentDB.

When an AI agent decides it needs to make use of a database, it needs to go through a multi-step process of understanding. It usually takes 3-7 calls before an agent understands enough about a database's structure to accomplish something meaningful with it. That's a lot of time and tokens spent on understanding. Worse still, this discovery tax gets paid repeatedly. Every new agent session starts from zero, relearning the same database semantics that previous agents already figured out.

Templates in AgentDB tackle this by giving AI agents the context they need upfront, rather than forcing them to discover it through trial and error. Templates provide two key pieces of information about a database upfront: a semantic description and structural definition.

The semantic description explains why the database exists and how it should be used. It includes mappings for enumerated values and other domain-specific knowledge. Think of it as the database's user manual written for AI agents. The structural component uses migration schemas to define the database layout. This gives agents immediate understanding of tables, relationships, and data types without needing to query the system architecture.

With AgentDB templates, agents requests like "give me a list of my to-dos" (to-do database) or "create a new opportunity for this customer" (CRM database) work immediately.

Once you've defined a template, it works for any database that follows that pattern. So one template can provide the context an AI agent needs for any number of databases with the same intent. Like a tot-do list database for every user to keep with an earlier example.

But static instructions for AI agents only go so far. These are thinking machines after all. So AgentDB templates can evolve with on use. For example, a template can be dynamically updated with specific queries that worked well. This creates a feedback loop where templates become more effective over time, learning from real-world usage to provide better guidance to future AI interactions.

AgentDB templates are provided to AI agents as an MCP server which also supports raw SQL access. So AI agents can make use of a database effectively right away and still experiment through querying. AgentDB templates are another example of designing software for AI systems rather than humans because they're different "users".