Getting Started With AI Agent Development
Understanding Integrations for Agent Development
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Understanding Integrations for Agent Development
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As a beginner trying to understand AI technologies, one of the most confusing distinctions can be between conversational interfaces (like ChatGPT or Claude) and true AI agents capable of taking action in the world. The key difference lies in integrations—the "plumbing" that connects AI models to external systems.
Think about traditional plumbing in a home. A standalone sink with no pipes connected to it isn't very useful—water has nowhere to go. Similarly, a conversational interface without integrations can generate text but can't take meaningful action in the world.
When you ask ChatGPT to "send an email to your team," it can write the email text, but it cannot actually send that email because it lacks the connections to your email service. It exists in isolation from other systems, like a sink with no plumbing.
As Armağan Amcalar explains in the OpenServ DevInsights video:
"...traditionally when people talk about AI agents, they usually mean some sort of large language model backed system that works on your behalf, does things on your behalf is instructed by you, but also tends to work autonomously"
An AI agent has "pipes" connecting it to various services. When an agent generates text suggesting an action should happen, that output can flow through those pipes to trigger actual changes in connected systems.
"...there's the LLM, which generates text, and this other system out there that's a CRM that does certain things. There's no connection between the two. Except in the middle of that, there can be this process, which I would generally say, I would categorize as tool calling...".
This is why conversational interfaces feel "trapped behind glass"—they can discuss taking actions but cannot implement them.
This is because language models themselves:
Can only output text or images - They cannot directly manipulate external systems
Have no persistent memory between sessions without additional architecture
Lack real-time perception of the world and its changes
To transform a basic language model into an agent requires building a "tool-calling" architecture. As explained in the Practical AI podcast:
"If I then ask the LLM to say, hey, I have this customer information, email name, etc., generate the arguments for me to call this function, which takes these specific arguments, then the LLM could generate the necessary arguments to call that function. And if you create a link between the function and the output of the LLM... you could put something on the front end into the LLM, and have the result be a flow of data out of the LLM, into the function, and then into the HubSpot API.
Armağan provides further clarity on this architecture:
"What our platform does is all that orchestration for you. So we figure out how to break down the user requests into smaller tasks that we distribute over an array of agents. And if you happen to have a task assigned to you or to your AI agent, you just get an HTTP request from us with all the details of the task".
MCP allows connecting the existing software world to LLMs. What HTML, browsers, and HTTP enable people to connect to software all over the world, MCP does that, but for LLMs.
Armağan explains MCP integration with OpenServ:
"We have integrated MCP into our core technology, which means it's now easy for you to make use of third-party MCP servers. For example, if you're building an AI agent with our SDK, you can use third-party MCP servers to do the tasks for you" .
As an agent gains access to more systems, a new challenge emerges: how does it know which tool to use when?
Armağan describes how OpenServ addresses this challenge:
"So we break it down into multiple components. Imagine there is a curator that's going to look for items that you can buy online to fit the description, to fit the style, and gathers pictures of those items. Another agent can then take those images and turn them into 3D objects".
Both sources describe multiple approaches to integration, with varying levels of complexity:
With OpenServ you have 4 different approaches:
A, you know nothing about the third party integration, our platform handles it for you.
B, you know a little bit about the third party integration, but you write about your request in text.
C, where you have the full control of the integration and you write your own modules, your own imperative code to talk to the third party API, but it routes over us
MCP
*Read more about our integrations
For beginners looking to build their first agent with integrations, Paloma Oliveira in the DevInsights video suggests starting small:
"So my first agent, I would just worry about the research and I wanna research but they're highly specialized... My second agent, I would like to add my style... My third agent, I need some help to make that a 3D representation... And that is a whole gigantic team of people. But when I break it down, it makes a little sense".
The power of integration transforms AI from an interesting toy into a genuinely useful tool. Without integrations, AI remains confined to generating content. With proper integrations, AI can operate in the real world.
As Armağan puts it:
"OpenServ SDK gives you many controls over the life cycle of your application's needs. And what is left to you as a developer is to bring in the expertise, the creativity, because we have everything else for you already built".
The key insight for beginners is understanding that the language model is just one component in a sophisticated system. The true power of AI agents comes from connecting these models to the digital services that run our world.
As you begin your journey into AI development, focus not just on prompting and model selection, but on understanding how to build these vital connections that transform text generation into real-world agency.
Language models like GPT-4 or Claude are incredibly sophisticated at generating and understanding text, but they are fundamentally constrained in important ways. In the , Daniel Whitenack and Chris Benson discuss this limitation:
Until recently, every developer had to build custom integrations for each service their agent needed to access. The represents a significant advancement by standardizing how these connections work.
"You will not ask LLM, you will not give it a thousand tools. If you as a human look at thousand options and you lose yourself... I expect LLM to have the same sort of 'oops, overwhelmed' effect" ().
Amcalar, A. & Oliveira, P. (2024). OpenServ
Practical AI. (2025, February 14).
Practical AI. (2025, April 14).
