AI Agents Are Here. What Now?

Introduction

The sudden, rapid advancement of LLM capabilities – such as writing fluent sentences and achieving increasingly high scores on benchmarks – has led AI developers and businesses alike to look towards what comes next: What game-changing technology is just on the horizon? One technology very recently taking off is “AI agents”, systems that can take actions in the digital world aligned with a deployer’s goals. Most of today’s AI agents are built by incorporating large language models (LLMs) into larger systems that can perform multiple functions. A fundamental idea underlying this new wave of technology is that computer programs no longer need to function as human-controlled tools, confined to specialized tasks: They can now combine multiple tasks without human input.

This transition marks a fundamental shift to systems capable of creating context-specific plans in non-deterministic environments. Many modern AI agents do not merely perform pre-defined actions, but are designed to analyze novel situations, develop relevant goals, and take previously undefined actions to achieve objectives.

In this piece, we briefly overview what AI agents are and detail the ethical values at play, documenting tradeoffs in AI agent benefits and risks. We then suggest paths forward to bring about a future where AI agents are as beneficial as possible for society. For an introduction to the technical aspects of agents, please see our recent developer blogpost. For an introduction to agents written before modern generative AI (that is largely still applicable today) please see Wooldridge and Jennings, 1995.

What is an AI agent?

Overview

Table 1. One example of how systems using machine-learned models, such as LLMs, can be more or less agentic. Systems can also be combined in "multiagent systems," where one agent workflow triggers another, or multiple agents work collectively toward a goal.
Adapted from smolagent blog post, with changes tailored for this blog post.