Introduction
Large language model (LLM) agents are advanced AI systems that use LLMs as their central computational engine. They have the ability to perform specific actions, make decisions, and interact with external tools or systems autonomously. This allows them to handle complex tasks that require complex reasoning, unlike standard LLMs, which primarily focus on text-generation-based inputs. With the increasing interest in the use cases of LLM agents across various industries, there are several questions regarding them that need to be answered. In this blog, I will cover the frequently asked LLM agent questions. This includes questions ranging from basics to components to practical applications and many more. So, let’s head towards these questions.
Overview
- Understand what LLM agents are and how they are different from LLMs, RL agents, and RAG.
- Explore some interesting use cases and examples of LLM agents.
- Learn about the components of LLM agents and some of the related tools and popular frameworks.
- Know the limitations and ethical concerns regarding LLM agents and how to handle them.
15 Most Frequently Asked Questions
Q1) What are agents in LLMs?
The term “agent” in the context of “LLM agent” refers to autonomous AI systems that leverage LLMs’ abilities beyond text generation. The agent is responsible for performing specific tasks by understanding the task, making decisions, and interacting with the external environment. Some of them are:
- Task executions: They are based on the given instructions, such as scheduling a meeting or booking a flight ticket.
- Decision-making: Decision-making involves analyzing data to determine the best course of action based on the available information.
- Task Management: Agents remember previous actions, ensuring they follow all the multi-step instructions without losing track.
- Interaction with external Systems: Agents can link with external tools and functions to update the records, retrieve required information, perform calculations, and execute code.
- Adaptability: Agents can adapt to changes or new information by adjusting their behavior in real-time.
Also Read: The Rise of LLM Agents: Revolutionizing AI with Iterative Workflows
Q2) What is an example of an LLM agent?
Consider John, who is planning a vacation. To do so, he seeks help from a chatbot.
John to the chatbot: “What is the best time to visit Egypt?”
The chatbot is equipped with a general-purpose LLM to provide a wide range of information. It can share the location, history, and general attractions of Egypt.
However, this question about the best time to visit Egypt requires specific information about weather patterns, peak seasons, and other factors influencing the tourist experience. Hence, to answer such questions accurately, the chatbot needs specialized information. This is where an advanced LLM agent comes into play.
An LLM agent can think, understand, and remember past conversations and use different tools to modify answers based on situations. So, when John asks the same question to a virtual travel chatbot designed based on an LLM agent, here’s how it goes.
John to chatbot: “ I want to plan a seven-day trip to Egypt. Please help me choose the best time to visit and find me flights, accommodation, and an itinerary for those seven days.”
The agent embedded in the LLM chatbot initially processes and understands the user’s inputs. In this case, the user wants to plan his trip to Egypt, including the best time to visit, flight tickets, accommodation, and itinerary.
In the next step, the agent bifurcates the tasks into
- Finding the best time to visit Egypt
- Booking flight tickets
- Booking accommodation
- Itinerary planning
While performing these actions, the agent searches the travel database for suitable travel timings and the perfect seven-day itinerary. However, for flight and hotel bookings, the agent connects to booking APIs (such as Skyscanner or ClearTrip for flight bookings and Booking.com or Trivago for hotel bookings).
Hence, LLM agents combine this information to provide the entire trip plan. The agent will also book the flight and finalize accommodation, if the user confirms any options. Moreover, if the plan changes last minute, the agent dynamically adjusts its search and provides new suggestions.
Q3) What is the difference between LLM and Agent?
The differences between LLMs and Agents are:
| S.NO | Large Language Model (LLM) | Agent |
| 1 | LLM is an advanced AI model trained on massive datasets. | Agent is a software entity that can autonomously perform specific tasks given by users. |
| 2 | Process text input as prompt and produce human-like text as output using Natural Language Processing (NLP). | Autonomously understands inputs, makes decisions, and performs final actions based on interaction with external systems like APIs or databases. |
| 3 | External environments or systems are not directly involved. | External systems, tools, databases, and APIs are directly involved. |
| 4 | Example: summary generation through GPT-4 | Example: A virtual assistant agent can book flights for the users, send follow-up emails, etc. |
Q4) Why do we need LLM agents?
LLM agent combines NLP with autonomous decision-making and final execution. When the project demands understanding, sequential reasoning, planning, and memory, LLM agents can be very helpful, as they involve multi-step tasks to handle complex text. They can analyze massive datasets to draw insights and help make autonomous decisions. LLM agent interacts with external systems to access or fetch real-time information. This enhances and creates personalized actions across various applications from healthcare to education and beyond.
Q5) What are some real-world use cases of LLM agents?
In the fast-moving world, there are various real-world use cases in different fields. Some of them are listed below:
- Alibaba uses LLM agents to enhance its customer service.LLM agents help the customer support system directly process requests instead of instructing. This streamlines the entire process and increases client satisfaction.
- AI-based legal and compliance organization, Brytr has developed an AI agent named “Email Agent”. This AI agent is capable of preparing drafts and replying to emails from commercial teams directly in MS Outlook or Gmail.
- Indeed, a job-seeking platform uses LLM agents to get a comprehensive list of job descriptions and opportunities that suit the job seeker data based on their experience and education.
- Oracle, a tech company, uses LLM agents for legal search, revenue intelligence, job recruitment, and call center optimization. This would save time in retrieving and analyzing information from complex databases.
- An E-Learning platform, Duolingo also uses LLM agents to enhance their learners’ learning experience.
- Automobile company Tesla is implementing LLM agents in its self-driving car. These agents contribute to the research and development of new organizational technologies.
Also Read: 10 Business Applications of LLM Agents
Q6) What are some popular frameworks and tools for building LLM agents?
Developers use an LLM agent framework as a set of tools, libraries, and guidelines to create, deploy, and manage AI agents through a large language model (LLM). Some popular frameworks are:
- LangGraph
We know that a “graph” is a pictorial representation of data in a structured manner. The LangGraph framework integrates LLMs with structured graph-based representations. This helps the model understand, analyze, and generate relevant output logically. This framework reduces human efforts to construct the flow of information for developing complex agentic architectures. - CrewAI
The term “Crew” means a group of people who work together. The CrewAI framework specializes in collaborating LLM agents with multiple other LLM agents, each with its own unique features. All of these agents work collectively towards a common goal. - Autogen
“Autogen” is related to the word “automatic.” Autogen facilitates smooth conversations among various agents. It makes it very easy to create conversible agents and has a variety of convenient Agent classes to develop agentic frameworks.
Learn More: Top 5 Frameworks for Building AI Agents in 2024
Q7) What are the components of an LLM agent?
A simple LLM agent consists of 8 components as shown in the figure below:
- User Prompt: The instructions given to an LLM in the form of text to generate responses. The output produced depends on the quality of the prompt.
- Large Language Models: LLM is the core computational engine of an LLM agent, which is trained on a massive dataset. These models process and understand language based on the data they are trained on.
- Planning: A flow of thoughts is necessary to solve complex tasks. The model must choose between existing and external sources to generate output.
- LLM’s Existing Knowledge: This refers to the massive datasets like web sources, blogs, research papers, Wikipedia, etc. on which LLMs are trained on.
- Tools: These are systems designed to interact with external systems or environments to perform tasks like database calling, API calling, etc.
- Call Tool(s): The act of using the required tool for extracting relevant information.
- External Information: Data accessed through external environments such as web pages, databases, APIs, etc.
- Output: The final response generated based on the existing knowledge or combination of both existing and external knowledge.
Q8) What is the difference between an RL agent and an LLM agent?
Differences between reinforcement learning (RL) agent and LLM agent are:
| S.NO | RL Agent | LLM Agent |
| 1 | RL agents interact with the external environment by continuously receiving immediate feedback in the form of rewards or penalties to learn from past outcomes. Over time,this feedback loop boosts decision-making. | LLM agents interact with the external environment through text-based prompts instead of feedback. |
| 2 | Deep Q-Networks (DQNs) or Double Deep Q-Networks (DRRNs) calculate Q-value to identify the appropriate actions. | LLM agent selects the most optimal action through training data and prompts. |
| 3 | RL agents are used in decision-making tasks such as robotics, simulations etc.. | LLM agents are used to understand and generate human-like text for virtual assistance, customer support, etc. |
Q9) What is the difference between RAG and LLM agents?
Differences between RAG and LLM agents are
| S.NO | Retrieval Augmented Generation (RAG) | LLM Agent |
| 1 | RAG generally involves two two-step process.Step 1: Retrieve relevant information from external sources.Step 2: Generate a response using an LLM. | LLM Agent counts on prompt-based input and reasoning to determine the optimal action, which may involve several steps |
| 2 | Do not preserve long-term memory. Each query is processed independently. | LLM agent maintains both long and short-term memory. |
| 3 | Do not perform any action beyond text generation. | Has an ability to act based on outputs such as sending emails, booking flight tickets, etc. |
Q10) How do LLM agents handle ambiguous or unclear inputs?
LLM Agents rely on prompts as input, and the final output depends on the quality of the prompt. In case of ambiguous or unclear input, the LLM agent needs clarity. An LLM agent can generate a few specific follow-up questions to improve clarity.
Example: If the user prompts the agent to “send an email,” the agent responds with questions like “Could you please mention the email ID?”
Q11) Can LLM agents be customized for specific industries or tasks?
Yes, LLM Agents can be customized as per industries or tasks. There are different methods to create a customized LLM Agent, such as:
- Fine-tuning on specific domain data
- Incorporating domain specific APIs and databases
- Customizing prompts
Q12) What are the ethical concerns surrounding LLM agents?
There are many ethical concerns while training and using LLM agents. Some of them are:
- LLM agents are trained on massive datasets that may include biased content. As a result, they may sometimes give out discriminatory outputs. They may also produce offensive content.
- LLM agents may generate both accurate and hallucinated responses. All LLM-generated responses must therefore be cross-checked.
- AI models are vulnerable to jailbreaking and prompt injection. They can be used for harmful or illegal purposes bypassing security measures.
- LLM agents provide different answers to the same query, at different times or to different users. This raises interpretability concerns.
However, the National Institute of Standards and Technology (NIST) has addressed these concerns and has come up with standard guidelines that AI developers should incorporate when deploying any new model.
Learn More: How to Build Responsible AI in the Era of Generative AI?
Q13) What are the limitations of current LLM agents?
LLM Agents are highly useful but still face a few challenges. Some of them are:
- Limited long-term memory: LLM Agents struggle to remember every detail from past conversations. It can keep track of limited information at a time. This might lose some crucial pieces of information. VectorStore techniques are useful for storing more information, but the issue is still not solved completely.
- Input is prompt-dependent: The LLM Agent relies on prompts for input. A small mistake in the prompt can lead to a completely different output, so a refined, structured, and clear prompt is required.
- Prone to changes in external tools: The LLM agent depends on external tools and sources, and changes in them may disrupt the final output.
- Produces inconsistent output: They may produce different outputs even when there is a small change in a prompt. This sometimes leads to unreliable outputs, which would be an error in the task performed.
- Cost and efficiency: LLM agents can be very resource-intensive, calling an LLM multiple times to come out with the final solution.
Q14) How do LLM agents handle continuous learning and updating?
Change is permanent. Agents can be set up in a way that they adapt to these changes regularly using finetuning, incorporating human feedback, and tracking performance for self-reflection.
Q15) How do LLM agents ensure data privacy and security?
AI-generated content may contain crucial or sensitive information. Ensuring privacy and security is a crucial step of LLM agent models. Hence, many models are trained to detect privacy violation norms in real-time, such as sharing Personally Identifiable Information (PII) like address, phone numbers, etc.
Conclusion
In this article, we covered some of the most frequently asked questions about LLM Agents. LLM Agents are effective tools for handling complex tasks. They use LLM as their brain and have seven other major components: user prompt, planning, LLM’s existing knowledge, tools, call tools, and output. Finally, integrating all these components boosts the ability of agents to tackle real-world problems. However, there are still a few limitations, such as limited long-term memory and real-time adaptation. Addressing these limitations would unlock the full potential of LLM agent models.
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