Explaining AI Agents

Explaining AI Agents

Introduction The term "AI agent" has been popping up everywhere lately, but underneath the marketing side of things, theoretical programs transformed into practical applications. 2025 was the year that AI went from just talking and started taking action. The rise of new protocols have bridged the gap between the power of LLMs and external software, giving AI the agency to perform tasks with semi-autonomous control. However, with every single company pushing out their own rendition of agentic AI, it begs the question: what exactly are AI agents?

What are AI Agents? Agency is the ability to make decisions or act independently. This is the core concept that drives AI agents. Previously, LLMs were limited to processing and answering questions with the information they had access to. AI agents are tools that are powered by LLMs with the ability to independently access external APIs and software to plan, reason, and execute tasks.

How are they different from typical chatbots? The two main differences between AI agents and chatbots is their capacity for action and memory. Though both are powered by LLMs, each was designed for a different purpose. Chatbots were built for dialogue and simple information retrieval, but agents were made to handle intricate workflows and independent operations.

What makes an AI agent different from the typical chatbot is its ability to engage with software beyond the chat window. Chatbots are limited to the information they have available and the data it was trained on, but AI agents go beyond that with their ability to access other tools. Your expected response with a chatbot is retrieval of information, but with agents you can expect real-time completion of tasks with the right tools.

The other defining aspect of AI agents is their long-term memory. Chatbots are typically restricted to the information in a single conversation or from the files uploaded in it. AI agents contain a persistent, long-term memory that allow them to recall and learn from past interactions to provide personalized responses to the context provided. The memory capabilities that AI agents possess will make them feel like a work partner that knows exactly what you need, rather than a mere information-grabbing tool.

How does DeepTutor utilize AI Agents? At DeepTutor, we have released our own rendition of an AI agent with a new chatting option called Agent Mode. In Agent Mode, we give our users the ability to chat to their entire Zotero library at once. This means that users do not have to upload the specific documents they need to extract information. All users have to do is prompt what they need and our AI agents can find what they need with a simple keystroke.

A major benefit that our users gain is that they do not have to waste time sorting through their vast amount of files to utilize our service. Agent Mode supports semantic search, meaning that users can ask us what type of documents we are looking for and we find it for them. This means conducting deep research across thousands of files and quickly identifying similar methods and insights across papers with no time added. We give our users the time they need to focus and synthesize their findings, with no extra strain on their energy.