AI agents are quickly becoming a necessity in the workplace. Imagine having the fastest, most knowledgeable assistant immediately available for any type of work at your request. That is exactly what AI agents are designed to be capable of. They are able to autonomously complete a wide variety of tasks, including researching on the Internet and managing entire workflows. However, there is a lot that goes behind the scenes of this advanced technology. The
Unpacking the AI Agent Tech Stack
The most critical layer of the tech stack by far is the data that is fed to the agent in the first place. This foundation determines how the agent understands the world it is operating in and the results that it will produce. The best AI agents are able to tap into the public web, which is where the world’s wealth of information sits. However, it is not a free for all – the agent must be able to do so with precision and in compliance in order to return the most relevant information. There are some specific APIs that enable this: Search API allows the agent to surface relevant web content in real time, and Unlocker API bypasses anti-bot protections to ensure reliable access to public data sources.
Agent hosting services are another important piece of the puzzle. Once the agent has access to data, it needs a digital environment to actually reason, make decisions, and take action. These platforms provide the infrastructure that turn static models into the dynamic, autonomous systems that are so helpful. Observability tools and agent frameworks are then necessary to help the agents become more autonomous and create guidelines for how they are structured. This ensures that developers are not left in the dark with how the AI agent is designed and have full visibility into how they reason, interact with tools, and collaborate with other agents.
There are several other layers to the tech stack that all play important roles in the success of an AI agent. Memory systems allow agents to retain context so users don’t have to constantly update their agents with information they have already fed it before. Tool libraries give agents the ability to interact with external systems APIs, databases, search engines, and basically anything outside of the agent itself. Sandboxes allow agents to safely write and run code in a test environment without permanent results.
Conclusion
However, underpinning all of these tool layers is the importance of quality data. After all, AI agents are only as capable as the information they are built on, and can only produce quality outputs if they have access to the right data at the right time. Without this continuous access, even the most powerful systems will not be helpful. The most valuable source that an AI agent can be trained on is the public web, so it is imperative that their connection to it is secure and based in real time.
