AI agents are quickly shaping the future of work, development, and decision making.
These AI-based software systems are capable of autonomously performing a huge range of tasks with the right prompting from a user, such as conducting research or managing a workflow.
Thanks to their ability to mimic human-like reasoning and observing skills, they can be trained to act as an assistant or thought partner by a human employee, and independently make decisions to achieve the employee’s goals.
AI agents are capable of improving productivity by quite a bit, and therefore expected to play a huge role in the workplaces of the future.
Let’s examine the AI agent tech stack in greater detail below.
Unpacking the AI Tech Stack
While the end user just sees a chat box and a long string of text, behind the scenes there is a carefully constructed tech stack that enables the AI agent to reason, act, and adapt according to the prompts they are fed.
A tech stack is a layered system of tools, and each tool plays a foundational role in making sure the AI agent is able to perform reliably.
Therefore it is essential for developers to understand what is powering their AI agents at each layer, and how they all work together to produce the desired outcomes.
The most critical layer of the tech stack is Data Collection and Integration. Before any action or reasoning can take place, the AI Agent will need to understand the world it is operating in.
The understanding is built off the back of mostly unstructured data. This data is the fuel for multiple use cases, including:
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- training AI models
- powering a retrieval augmented generation (RAG) system
- or enabling an agent to respond to live changes in the market
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There are some specific tools that are geared to make this data collection and integration process more robust.
For example, Search API is able to surface relevant web content in real time.
Unlocker API is able to bypass anti-bot protections to make sure the AI agent can access public data sources as needed.
Once an agent has access to data, the next layer needed is agent hosting services.
This tool creates the digital environment where all the reasoning, decision making, and actions are able to take place.
In other words, these operating systems provide the infrastructure that turns static models consuming data into a dynamic, autonomous system.
These hosting platforms manage everything from orchestration to execution and make sure agents can interact with APIs successfully.
Developers are using a variety of tools – examples include LangGrap, which helps build multi-step agent workflows, and AWS, which offers infrastructure for managing agents at scale.
As agents become more autonomous, they will begin to require observability tools that help developers monitor performance and debug issues as they arise.
AI agents should not be designed as a black box; developers should always have a clear view into what is happening to make sure the agents are operating safely.
Frameworks are another tool used to maintain this visibility into an AI agent’s actions.
They define how agents are structured, how they reason, interact with tools, and collaborate with other agents.
In other words, frameworks give agents their structure and logic, while still relying on real-time data.
Memory is another very important layer in the tech stack.
Memory systems are responsible for allowing agents to retain context, build long term understanding of the problems they are asked to help with, and remember past conversations.
For example, if a worker is using an AI agent, it would be frustrating to have to feed it context about the workstream all over again each time the agent is used.
Memory systems also enable agents to learn and adapt, but requires high quality input in order to achieve this successfully.
AI Agents in Tech Stacks
There are a few other layers in the tech stack, including tool libraries, sandboxes, model serving, and storage.
Each plays their own important role in the success of an AI agent, but the most important layer is obvious: the initial data that it is fed.
AI agents are only able to reason, plan, and act only if they have access to the right data at the right time.
Without it, even the most advanced AI systems will quickly be unable to provide any relevant help.
The most valuable data source of all is the public web, so it’s important that an AI agent can access it at any time.
Source: Bright Data