Templates for AI agents: what they are and why they matter

A template for AI agents is a ready-to-use package that combines three things: a data schema (what is stored and how), the tools the agent can use on that data, and expert guidelines that tell it how to reason in that domain. They matter because they turn a generic assistant into one that is useful for something specific from day one.

At Traiker we learned this by building our own “templates” before calling them that: for the gym, for meals, for tracking clients. Every time we repeated the same work, and every time it became clearer that the hard part wasn't the AI, but deciding what to store. Those decisions are what's valuable: which fields matter, what things are called, what questions you'll want to be able to ask in six months.

Why isn't a good prompt enough?

A prompt tells the model how to behave in a conversation. But if there is no structured data behind it, every answer starts from what you tell it at that moment. You can write the best nutrition prompt in the world and the assistant still won't know what you ate yesterday.

The template solves that because it includes the place where the information lives and the way to query it.

What does a template include?

First, the schema. In a gym template it would be exercises, sessions, sets and weights; in a CRM one, contacts, companies, conversations and next steps. Deciding it well is half the work, because the questions you can ask later depend on it.

Second, the tools. With MCP, a server can expose tools that the model invokes, such as “log a session” or “find a contact”, and resources that it reads as context.

Third, the guidelines. This is the knowledge of someone who knows the subject: how to interpret a strength progression, what to ask before a sales meeting. MCP also provides for reusable prompts that fit very well here.

Why do they matter so much?

Because they lower the barrier to entry enormously. Most people don't want to design a database or write a server; they want to ask their assistant how their training is going or what they have pending with a client.

And because they bring order. When data follows a schema, the assistant makes fewer mistakes and answers are comparable over time. We explain this in our gym log: as soon as we fixed the exercise names, the questions started to work. Without a fixed schema, the assistant can treat two names for the same exercise as different things and give meaningless progressions.

Where are we heading?

At Traiker we are building the first templates (gym, meals and CRM) on our own database for each user. The longer-term vision is a marketplace where experts publish theirs: a coach, a nutritionist or someone in sales who packages their way of working so others can use it with their own assistant.

It doesn't exist yet; it's where we want to go. We mention it because we believe the value of agents will lie less in the model and more in the knowledge and data you put in front of them.

Frequently asked questions

Is a template the same as a custom GPT?

Not exactly. A custom GPT focuses on instructions and files within a platform; a template also includes its own data schema and tools to read from and write to it.

Can I create my own template?

With technical knowledge, yes: define the schema and build an MCP server with its tools.

Do templates work with any assistant?

With those that support MCP, which today include Claude, the OpenAI API and the xAI API for Grok.

If you're interested in trying the first templates, sign up for the beta.