A Short Introduction to AI Agents

A short introduction to AI agents

Despite the buzz surrounding AI agents, the technology is still in its infancy. We'll discuss what AI agents are, how they work, their current capabilities, and the challenges they face. While AI agents show promise for automating complex tasks across various industries, significant improvements are needed before they can operate autonomously at scale.

What are AI Agents?

AI agents are different from chatbots in many ways. The term ‘AI agent’ refers to a type of AI system that is able to plan, make decisions, and execute tasks without human intervention. In theory, this capability allows them to handle complex workflows and execute more complex tasks, setting them apart from chatbots that are designed to answer questions or help you change your address.

Since the introduction of LLMs, AI agents have become increasingly feasible to build. At their core, AI agents leverage large language models (LLMs) combined with the ability to use tools. The LLMs are enabling the agents to reason, plan, and prioritize tasks. Meanwhile, the integration with external tools allows them to perform increasingly complex tasks – that’s the idea at least.

Current limitations

Applications and tools like AutoGPT, Devin, and Cosine can be seen as early attempts at creating AI agents platforms. Despite the excitement surrounding AI agents, the reality is that fully autonomous agents are still in their nascent stages. They face challenges in complex reasoning, planning, and accurately interacting with external tools or websites.

Common problems include getting stuck in endless loops of consideration, losing context, and struggling to manage large amounts of information. These limitations make AI agents currently unreliable for tasks that require sustained focus and complex decision-making.

Conclusion

As we look to the future of AI agents, it’s clear that while the potential is there, significant engineering work remains to be done.

Beyond that, improvements in reasoning capabilities of LLMs, context management and/or memory, are necessary building blocks for AI agents to truly operate autonomously at scale. It remains to be seen if the pace of development will continue and/or when progress will stall. For now, it’s important to recognize both the potential and the current limitations. One thing is for certain: as the technology continues to evolve, we can expect AI agents to play a growing role in re-shaping the future of work.