The AI Agent Tech Stack: A Developer’s Most Important Tool

The AI tech stack is vital for successful AI agent development, but truly understanding the tech stack can be a daunting task. With so many tools and layers, developers may get overwhelmed. However, when broken down into separate layers, the ins and outs of the tech stack become clear, allowing developers to use it to its full potential. 

In this blog post, I will explain the AI agent tech stack and why it is a developer’s most important tool.

Analyzing the AI Agent Tech Stack

One of the most important layers of the AI agent tech stack is data collection and integration. This layer forms the foundation of an AI agent, as the agent needs to use data to learn about the context in which it works. Data collection and integration takes real-world data and funnels it into the agent. It does so by providing the infrastructure that allows AI teams to access the public web, gleaning data with scale and with precision. 

There are many other layers to the tech stack, such as agent hosting services, observability, agent frameworks, memory, tool libraries and more. Each layer has its own purpose. For example, tool libraries extend agent capabilities by integrating the agents with external tools and APIs. This layer turns language models into actionable agents. 

Another layer is model serving, which is where the agent makes decisions. In the model serving layer, LLMs are hosted and accessed, which powers each decision that the agent makes. Several platforms help with model serving; for example, many developers use OpenAI, Anthropic or Mistral. 

Storage is another key part of the tech stack. This is where agents keep their history.  Having a place to store past interactions and knowledge is vital for growth and evolution. Without storage, it would be difficult to reproduce past success and learn from past mistakes. When it comes to storage, developers use platforms such as Amazon S3, Google Cloud Storage and Vector DBs. 

Conclusion

Learning the role of each layer of the tech stack, as well as how the layers work together, can make a big difference in the reliability of an AI agent. Developers should study the tech stack and familiarize themselves with the available tools.