“AI Agents” have become the latest buzzword in AI world and have always confused me about what does it mean. It does not help that there are a lot of products claiming to be “Agents” without having any of its core capabilities.
So, in order to dig deeper, I explored some research papers & courses that talk specifically about characteristics of LLM agents to build my mental model around the topic.
In this blog post, I aim to present LLM Agents & their core capabilities and also differentiating them standalone LLM applications.
Data Efficiency. Reasoning Capability (debatable!). Generalisation.**
Traditional ML/DL AI systems required large amounts of labeled data for specific tasks. Furthermore, they had limited generalisable qualities and had to be re-trained for other tasks.
Whereas LLMs demonstrate remarkable generalisation across wide variety of tasks while being data efficient as they just need exposure to a few examples. This is because LLMs exhibit language based reasoning, generating intermediate steps to reach conclusions similar to humans! ML/DL systems learned from statistical pattern matching from massive datasets.
Therefore, LLMs excel in variety of use-cases such as text summarisation, translation, code generation ,etc
Standalone-LLM based applications integrate intelligence of LLMs into specific workflows, automating parts of it and thereby, enhancing productivity.
Use-cases : Summarisation, Code generation, Language translation, Content creation, etc.
Key capabilities : Enhanced User Experience & Productivity with few parts of workflow automated.
Key shortcomings :