What is an AI Agent and How to build one?

AI agents are the next generation of internet bots, but with a major difference. Whereas current agents typically perform simple and structurally repetitive tasks, such as indexing web pages for search engines, AI agents can learn, adapt, and make decisions based on the data they’ve processed. You could say that an AI agents have evolved decision-making capabilities.

What is an AI agent?

An artificial intelligence agent is an autonomous system capable of interacting with and manipulating software apps, websites, and online tools. It represents a shift from traditional agents, by focusing on dynamic interaction with digital environments and executing complex, goal-oriented tasks autonomously.

Current agents, often referred to as web agents, internet bots or web robots, are software applications that typically perform simple and structurally repetitive tasks, ones well-suited for automation. AI agents on the other hand are designed to perceive their environment, make decisions and then iterate those decisions based on the feedback they receive.

What Does an AI Agent Do?

AI agents operate beyond merely generating new text and images. They use APIs to perform tasks like scheduling meetings or playing video games like Minecraft. They can analyze data by understanding and adapting to new patterns and information.

They present a major advance from indexing web pages (think Google's web crawlers), predefined customer service agents (scripted chatbots), or monitoring websites for updates (change detection bots). Instead, they’re able to continuously learn and adapt, improving their performance and decision-making over time.

What is an Example of an AI Agent?

One of the most well known examples of an AI agent in BioTech/BioInformatics is DeepMind's AlphaFold, which predicts the 3D structures of proteins with remarkable accuracy. AlphaFold is used to accelerate scientific discovery and drug development.

Its genesis was predicated on processing vast amounts of biological data and applying advanced deep learning techniques, necessitating significant computational processing power. These developments have allowed AI agents to perform highly specialized and intricate scientific analysis that was previously unattainable.

Build AI Agents

Creating an AI agent capable of interacting with various tools involves building a versatile general problem-solver. This type of AI agent is built upon three primary components:

1. Planner: This module comprehends the assigned task, breaks it down into manageable sub-tasks, and devises a sequence for task execution.
2. Memory: It functions as the agent’s knowledge reservoir, maintaining a log of past interactions and tracking the progress of solved and pending tasks. This memory also stores essential domain knowledge that assists the planner in its function.
3. Execution Tools: These are application-specific utilities that follow the planner's instructions to carry out tasks using relevant tools. For example, they might schedule an appointment using Google Calendar.

With the advent of tools like AutoGPT and BabyAGI, which utilize the OpenAI GPT-4 model, tasks can now be understood and a step-by-step plan can be generated more effectively. The academic community is also contributing significant research on how to use or fine-tune large language models (LLMs) for efficient planning.

To create an effective planner, the process should begin by clearly outlining the goals and limitations. One can then prompt an LLM like GPT-4 to formulate a plan consisting of a sequence of actions. This sequence might resemble a tree structure, where actions branch out depending on different conditions or outcomes. Managing the sequence requires careful orchestration to address dependencies and to pass inputs from one step to the next. A memory component is crucial for tracking the history of actions and decisions made. For memory storage, vector stores and databases are effective tools. For execution, one can utilize simple APIs that leverage code-generation capabilities of LLMs, combined with knowledge bases, to carry out tasks.

A rigorous testing framework is necessary to ensure the plan is realistic and free from errors, such as "hallucinations" (incorrect or irrelevant information generated by the model) or repetitive, cyclical steps. Finally, establishing a robust feedback loop is critical. This allows for continuous improvement of all components, ensuring that the AI agent evolves and adapts over time.