Understanding the tech stack–a complex system of tools that help AI agents function–is vital to developers. Without a foundational knowledge of AI tech stacks, it is difficult to get agents to perform reliably.
One of the core layers of the AI agent tech stack is Data Collection and Integration. This layer is made up of a vast network of real-world and real-time data. This data helps an AI agent understand the world in which it works, which is vital for proper functioning.
Other important layers are agent hosting and service, observability, agent frameworks, memory, tool libraries, sandboxes, model serving and storage. Each of these layers is complex and serves a specific purpose. For example, sandboxes are controlled environments where developers can test and build AI agents. Agent frameworks, on the other hand, are libraries for creating and orchestrating agents.
There are many services available for each of the layers. For example, when it comes to observability, many developers are using LangSmith, Weights & Biases and WhyLabs. In relation to the memory layer, developers are using ChromaDB, Qdrant and Weaviate.
Knowing which services are available for each layer of the tech stack, as well as the individual purpose of each layer, is vital when working with AI agents. These layers come together to create a powerful tech stack for AI development, which is a core foundation to master.
Source: Bright Data
Source: Bright Data