AI Agents Are Here. What Now?

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 .

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.

Our analysis reveals that risks to people increase with a system’s level of autonomy: The more control a user cedes, the more risks arise from the system. Particularly concerning are risks for the safety of individuals that arise from the same benefits that motivate AI agent development, such as freeing developers from having to predict all actions a system may take. Further compounding the issue, some safety harms open the door for other types of harm – such as harms of privacy and security – and inappropriate trust in unsafe systems enables a snowball effect of yet further harms. As such, we recommend that fully autonomous AI agents are not developed. For example, AI agents that can write and execute their own code, beyond constrained code options controlled by the developer, will be endowed with the ability to override all human control. In contrast, semi-autonomous AI agents may have benefits that outweigh risks, depending on the level of autonomy, the tasks available to the system, and the nature of individuals’ control over it. We now turn to these topics in-depth.

There is no clear consensus on what an “AI agent” is, but a commonality across recently introduced AI agents is that they are “agentic”, that is, they act with some level of autonomy : given the specification of a goal, they can decompose it into subtasks and execute each without direct human intervention. For example, an ideal AI agent could respond to a high-level request such as “help me write better blogposts” by independently breaking this task down into retrieving writing on the web that is similar to your previous blog topics; creating documents with outlines for new blog posts; and providing initial writing within each. Recent work on AI agents has made possible software with a broader range of functionality and more flexibility in how it can be used than in the past, with recent systems deployed for everything from organizing meetings ( example1 , example2 , example3 , example4 ) to creating personalized social media posts ( example ), without explicit instructions on how to do so.

All recently introduced AI agents we’ve surveyed for this newsletter are built on machine learning models, and most specifically use large language models (LLMs) to drive their actions, which is a new, novel approach for computer software. Aside from being built on machine learning, today’s AI agents share similarities with those in the past, and in some cases realize previous theoretical ideas of what agents might be like : acting with autonomy, demonstrating (perceived) social ability, and appropriately balancing reactive and proactive actions.