AI agents have unlocked new levels of productivity for employees. Their abilities range from autonomously scouring the web for research purposes to managing entire workflows. Many workers are already using them as assistants that they can outsource their manual tasks to, while freeing up their time for high-level strategic thinking. Most users are only aware of the chat interface they are communicating with, but behind these agents is a meticulously crafted tech stack, which is a layered system of tools that enable them to reason, act, and adapt according to the needs of their users. Developers must understand this stack to ensure their agents continue to perform, from the tools that are being used to how they interact together. 

The most important layer in any tech stack is the foundational data that the AI agent is learning from. This real-world, real-time, unstructured data, often pulled from the public web, is the key to the AI agent producing outputs that are relevant and accurate. There are many APIs that are available to help make this data collection process seamless. For example, the Search API is able to surface relevant web content in real time, while the Unlocker API is able to help the agent bypass anti-bot protections and access public data resources.