It is essential to have a good understanding of AI tech stacks when working with AI agents. Tech stacks are complex, with various layers coming together to develop reliable agents. Learning about these tools, as well as the ways in which they interact, can go a long way when it comes to successful AI development.
The foundational tool in a tech stack is data collection and integration. This layer gathers real-time data, which can then be used to train AI agents and familiarize them with vital information.
After developing a base through data collection and integration, other tools, such as agent hosting and serving, memory and tool libraries come into play. Each is vital in its own way. For example, tool libraries help to extend agent capabilities by bringing external tools and APIs into the mix. Memory, on the other hand, consists of libraries that help to build AI agents.
Luckily, there are many programs that can help developers navigate the tech stack. For instance, within the agent hosting and serving layer, developers can use tools such as Azure Machine Learning, AWS SageMaker and Hugging Face Inference.
When developing AI agents, paying careful attention to each layer of the tech stack will ensure that you are checking all of the boxes when it comes to creating a well-built AI agent.
Source: Bright Data