Data: The Backbone of AI Agents

AI agents are enabling levels of productivity that most employees could have never dreamed of. With a simple command, these agents can perform research by scouring the web in real time or even running entire workflows autonomously. While the user only sees the chat interface, there is actually a lot going on below the surface. These AI agents rely on a complex tech stack with many layers of tools that enable the agent to act, reason, and adapt autonomously. 

Data serves as the foundational layer for tech stacks. AI agents’ outputs are only as good as their connection to the most updated information on the public web. There are some APIs that are designed with the sole purpose of making sure their access to high-quality, real-time information is uninterrupted, such as the Search API and Unlocker API. After accessing the necessary data, agents use hosting platforms like AWS or LangGraph to carry out operations, plan workflows, and communicate with other systems.

Other layers of the tech stack include observability tools and agent frameworks, which serve to provide safe and transparent decision-making. Memory and storage systems help agents retain information and maintain context, which enables the agents to learn and adapt. Other examples of tools include sandboxes, tool libraries, and model serving. 

Source: Bright Data