LLM agents explained

What are LLM agents?

LLM agents are software applications powered by large language models (LLMs), a type of generative AI, that perform tasks, make complex decisions, and interact with users and the environment with autonomy and adaptability.

Unlike a standalone LLM that simply generates text, an LLM agent provides more contextual and adaptive responses by using sequential reasoning, planning ahead, and keeping track of past conversations.

How do LLM agents work?

LLM agents process user queries step by step. First, they take in the user’s request and analyze it to recognize the intent and key details, like what action is needed or what kind of information is being asked for.

Next, they figure out how to tackle the task. This might mean splitting it into smaller steps or deciding if they need extra data. If outside information is needed — like pulling details from a database or an API — the agent integrates or “calls” these external tools to retrieve the necessary information before moving forward.

Once all the pieces are in place, the agent decides on the best way to respond. It uses its language generation capabilities to produce a clear, relevant answer based on what it has processed. If the response needs improvement, the agent may repeat this process to refine its output based on additional input or clarification from the user.

From a user perspective, it’s simple — you type in a prompt and hit enter, and the agent gets to work. It figures out what you’re asking, plans how to respond, pulls in extra data, and then gives you an answer. If some information is missing or if you give it feedback, the agent might go through the process again to attempt to improve its response.

What are the key elements of LLM agent architecture?

Developers build LLM agents with multiple interconnected components that enable them to process input, plan actions, and interact with external systems. The key elements of LLM agent architecture include:

1. 1.Core LLM is the foundational AI model that processes user input, generates text-based responses, and performs reasoning based on its training and knowledge retrieval mechanisms. This is the agent’s “brain,” responsible for processing language and producing responses.
2. 2.Prompt handling and input processing is the system that infers meaning from user input, identifies intent, and reformats input so that the LLM receives clear, well-formatted instructions. If the user input is unclear or incomplete, the prompt handling system cleans, structures, and refines it before passing it to the core LLM.
3. 3.The memory module helps the agent remember past interactions and keep track of context. Short-term memory keeps track of conversations within a session, while long-term memory allows the agent to remember past exchanges, user preferences, or historical data.
4. 4.The planning module helps the agent divide complex tasks into steps, solve problems, and decide what to do next. Some advanced systems use methods like the tree of thoughts framework to explore different reasoning paths and self-reflective agents to assess and refine their own decisions. Planning is crucial for handling multi-step tasks, solving problems in an organized way, and making decisions in the right order.
5. 5.Tool use and API integration expand the agent’s abilities beyond text generation (like retrieving live data, executing scripts, and interfacing with applications) by allowing it to interact with other tools, databases, and APIs. This helps the agent pull real-time data, run commands, and complete tasks like booking tickets or retrieving financial information.
6. 6.A knowledge retrieval system is a mechanism that lets the agent access and incorporate external information beyond what it was originally trained on. It can search databases, retrieve documents, or use retrieval-augmented generation (RAG) to improve accuracy.
7. 7.The action execution module executes the planned actions by running commands, calling APIs, or interacting with other systems. This allows it to take action instead of just generating text.
8. 8.Feedback and adaptation enables the agent to refine its approach based on user feedback, errors, and task results. The LLM agent may refine its responses through learning mechanisms, self-correction, or direct user input.

While key elements focus on how an LLM agent’s functionality is structured and implemented, core components represent the essential building blocks that power those capabilities. Let’s take a closer look at the core components to better understand how LLM agents function.

What are the core components of LLM agents?

The core components or features of an LLM agent include user requests, tools, memory, planning, and knowledge. They are the building blocks that enable the agent to process inputs, retrieve relevant information, make decisions, and generate coherent responses. Let’s examine each of them in more detail.

User request

The user request, or prompt, is the question or instruction you give the LLM agent. It tells the agent what you need, like asking for a summary, a translation, or a recommendation.

Via prompts, the agent receives input and instructions from users or systems.

Tools

Tools are the extra features the agent can use to go beyond its built-in knowledge. Think of them like apps on a phone — some fetch real-time data, while others run calculations or generate images. These are the external tools, resources, and APIs the agent can interact with to perform tasks. They are integrated into the agent’s system but operate separately from the core LLM model.

Memory

Memory allows the agent to retain information from past conversations. The agent’s ability to retain context and previous interactions, either short-term or long-term, helps it respond more naturally and keep conversations flowing. Without memory, every response would feel like a brand-new interaction.

Planning

Planning is the process of strategizing, reasoning, and structuring actions to solve tasks or achieve goals. It allows the agent to break down complex tasks into smaller, logical steps. If you ask for a trip itinerary or a coding project, the planning module organizes the steps in a structured sequence.

Knowledge

Knowledge refers to the information the agent uses to generate responses. It includes both pre-trained data (from the model’s original training set) and external resources (such as databases, APIs, or RAG systems). While the model can generate responses based on its pre-existing knowledge, external retrieval mechanisms help provide more accurate, up-to-date information.

What are the types of LLM agents?

LLM agents come in different types, each designed for specific tasks and use cases. Some focus on answering questions and assisting users in conversations, while others specialize in retrieving real-time information, reasoning through complex problems, or working autonomously. Knowing these types may help you to choose the right agent for each of your tasks.

Conversational agents

Conversational agents engage in dialogue-based interactions with users, mimicking human-like conversation. They rely on large language models, often fine-tuned with reinforcement learning, to generate coherent and contextually relevant responses. Customer service chatbots and virtual assistants are common examples.

Task-oriented agents

Task-oriented, or task-specific, agents focus on achieving specific objectives like booking appointments, processing orders, or automating workflows. They’re designed with a clear goal in mind. Unlike conversational agents, they follow structured decision-making processes to interpret user needs and execute predefined actions.

Creative agents

Capable of generating content such as artwork, music, or code snippets, creative agents use LLMs to infer human preferences and artistic styles. Content generation tools that draft articles or create digital art fall under the category of creative agents.

Autonomous agents

As the name implies, autonomous agents are designed to operate with minimal human intervention and perform actions independently. Their autonomy levels vary — some function under predefined rules, while others dynamically adapt to new inputs. Businesses often use them for robotic process automation (RPA) and large-scale data processing.

Collaborative agents

Collaborative agents work alongside humans or other AI agents to accomplish shared goals or tasks. They may help with communication and coordination between team members or between humans and machines. For example, project management bots help teams coordinate tasks and deadlines.

Retrieval-augmented generation (RAG) agents

RAG agents improve their responses by accessing external data sources, which provide up-to-date and contextually relevant information. They integrate real-time retrieval mechanisms with language generation capabilities to deliver current outputs. For example, an AI-powered research assistant gathers and incorporates the latest studies and publications into its responses when you ask it about recent scientific developments.

Reasoning agents

Reasoning agents break down complex problems into manageable steps and apply logical analysis to find solutions. They systematically explore different ideas and provide conclusions. For example, a legal advisory bot analyzes the specifics of a case, interprets relevant laws, and offers a structured legal summary based on its analysis.

Multimodal agents

Multimodal agents process and generate content across various formats, such as audio, images, and video. This way, they can handle a wider range of tasks and provide more interactive and varied experiences than text-only agents. For instance, virtual assistants can interpret visual data, like images or videos, and generate images based on textual descriptions.

Multi-agent systems

Multi-agent systems involve multiple agents working together to tackle complex tasks by using the strengths of different specialized agents. They communicate and coordinate to achieve a common objective. For example, one agent gathers data, another analyzes it, and a third generates reports based on the analysis.

What are the use cases of LLM agents?

LLM agents may be applied across different industries and functions, including:

Healthcare

By analyzing medical records and the latest research, LLM agents may assist doctors in making more accurate diagnoses. For example, they can quickly recommend treatment options for a patient based on their medical history, which may help doctors make decisions faster.

Finance

Financial institutions can use LLM agents to monitor transactions and identify potential fraud by recognizing patterns that indicate suspicious activities. This real-time analysis may improve security and reduce fraud.

LLM agents can also analyze vast amounts of financial data to uncover market trends and offer insights. They generate detailed reports, forecasts, and risk assessments, providing businesses and individuals with information that, if used with caution, may help to optimize investment strategies.

Education

Educational platforms use LLM agents to create personalized learning experiences and adapt content to suit individual students’ needs and learning styles. These agents may act as AI tutors, simulate classroom interactions, and give personalized feedback to students.

Certain LLM agents can help educators create course materials, quizzes, and interactive content, which may be useful when simplifying the curriculum development process. These agents may save teachers some time by adapting content based on student performance and keeping it relevant.

Legal

Law firms can use LLM agents to analyze legal documents, contracts, and case law, which may speed up the review process. These agents are able to sift through large volumes of information, identify critical details, and reduce the time spent on routine tasks. LLM agents can also assist legal professionals in research by retrieving and summarizing relevant legal precedents, statutes, and case studies.

Customer service

Companies use LLM-powered chatbots to handle customer inquiries and provide instant responses. Some agents can also analyze customer feedback to assess sentiment and identify areas that should be improved. By recognizing patterns in reviews or complaints with the help of an LLM agent, businesses can easily see which concerns are more pressing and need attention to maintain a positive relationship with customers.

Automation

Whether it’s scheduling meetings, sending email reminders, or generating reports, automation-oriented LLM agents handle repetitive manual tasks.

LLM agent frameworks

An LLM agent framework is a set of tools and guidelines that help developers create, manage, and run AI agents powered by LLMs. Here are some key open-source solutions, each focused on a different part of building, orchestrating, and scaling LLM-powered agents.

CrewAI

As the name suggests, CrewAI is a framework for managing AI agents that work together as a team. Instead of a single AI handling everything, CrewAI lets you assign different roles to agents for a more structured workflow, so it’s useful for tasks that require a coordinated effort.

CrewAI’s key features include multi-agent collaboration, which allows agents to share information and work collectively. It also offers flexible memory systems to maintain context across different agents. With role-based orchestration, each agent can take on a specific function — like planning, executing, or verifying — to keep the process organized and efficient.

LangChain

LangChain is a framework that makes it easier to build applications using LLMs. It’s designed with a modular approach, so you connect different components (prompts, external APIs, and data sources) to create dynamic and flexible AI-driven workflows.

Key features include prompt templates for reusing and adapting prompts across tasks, tool integration with databases and APIs, and chain-of-thought logic for handling multi-step reasoning. This setup allows for more structured and complex interactions while keeping development straightforward.

Microsoft AutoGen

Microsoft AutoGen is a tool that helps developers build AI agents that can communicate, share knowledge, and collaborate. Instead of relying on a single AI model, Microsoft AutoGen enables multiple agents to work together.

Its key features include agent-to-agent conversations that allow AI units to brainstorm and verify each other’s outputs. The interactive architecture lets developers add or remove agents as needed to adapt to task complexity.

Phidata

Phidata is a solution for quickly developing AI assistants, with pre-packaged tools for easy deployment. It provides prebuilt modules that reduce development time, scalable deployment options that work in both local and cloud environments, and an extensible design that lets users add or remove functionality through plug-ins as business needs change.

LangGraph

LangGraph is a library that connects large language models with graph-based structures to improve reasoning and organize knowledge. It uses graphs to map relationships between concepts, making it easier for AI to follow complex ideas. It also allows quick retrieval of stored information by querying nodes and edges. With better context handling, it keeps track of how ideas link together and preserves relevant information throughout interactions.

SmolAgents

SmolAgents is a lightweight library from Hugging Face that helps developers create AI agents with minimal code. It keeps things simple by using about 1,000 lines of core logic, avoids unnecessary layers, and focuses on raw code. Agents define their actions as Python code snippets that are flexible and easy to combine. The library works with different large language models, including those from OpenAI, Anthropic, and the Hugging Face Hub. It also makes it easy to share and load tools directly from the Hugging Face Hub.

AutoGPT

AutoGPT is an open-source application that runs tasks on its own based on the goals that the user defines. It breaks down big objectives into smaller steps and completes them independently. AutoGPT can access the internet to gather real-time information, track past actions to stay consistent, and adapt using custom plugins.

What are the benefits of LLM agents?

LLM agents deliver several advantages, especially for organizations looking to enhance productivity:

• Scalability. LLM agents can handle numerous user interactions simultaneously without performance bottlenecks.
• Consistency. Agents provide uniform responses, which may reduce errors and information discrepancies.
• Efficiency. LLM agents automate routine tasks and free human professionals to focus on more complex responsibilities.
• Adaptability. Agents can be updated or fine-tuned as new data becomes available, which allows them to produce current, relevant solutions and responses.
• Cost savings. By automating support and administrative processes with agents, businesses can reduce operational expenses.
• IT modernization. Building and orchestrating multiple gen AI agents is estimated to propel IT modernization. Data, accumulated by December 2024, shows that using generative AI cuts manual work, speeds up tech modernization by 40–50%, reduces tech debt costs by 40%, and improves output quality.

What are the limitations of LLM agents?

Despite their capabilities, LLM agents also present some challenges and limitations:

• Lack of true understanding. They rely on pattern recognition rather than genuine comprehension, which can lead to superficial or incorrect answers.
• Sensitivity to input phrasing. Slight rewording of a question can yield different responses, which impacts reliability.
• Potential for bias. Training data may contain biases, and the agent’s outputs can inadvertently reflect those biases.
• Resource intensive. Large-scale training and real-time inference can be expensive and require substantial computational power.
• Data privacy concerns. Handling sensitive data requires strict security and compliance measures.

If you’ve decided that LLM agents’ benefits outweigh their drawbacks, you might as well start implementing them in your business.

How can you implement LLM agents?

Implementing LLM agents involves a systematic approach that includes the following steps:

1. 1.Defining objectives. First, you must identify specific tasks and goals you want the agent to accomplish, for example, customer support, data retrieval, creative content.
2. 2.Selecting appropriate models. Choose an LLM that suits your requirements — consider factors like size, latency, and domain specialization.
3. 3.Integrating tools and resources. Connect external APIs, databases, or plug-ins to extend the agent’s capabilities beyond language understanding.
4. 4.Building a feedback loop. Collect user feedback and iterate on the system. Continual refinement helps maintain accuracy and relevance.
5. 5.Ensuring security and compliance. Protect sensitive data through encryption, role-based access controls, and compliance with relevant data protection laws.
6. 6.Monitoring and optimization. Regularly measure metrics like response time, accuracy, and user satisfaction to pinpoint areas of improvement.

Once you’ve implemented one or more LLM agents, you’ll need to monitor their performance and evaluate their results. This may require further optimization and fine-tuning. But first, you need to understand the evaluation criteria and how to measure their effectiveness.

How can you evaluate LLM agents?

To make sure an LLM agent meets your business needs, you have to keep an eye on it — test its performance, reliability, and adaptability. Here are key ways to evaluate an agent:

1. 1.Performance metrics. Track key indicators like response accuracy, latency, and user satisfaction scores. These metrics help measure how well the agent performs under different conditions.
2. 2.Scenario testing. Simulate real-world use cases to see how the agent adapts to different contexts and unexpected inputs. This helps assess its flexibility and problem-solving ability.
3. 3.Accuracy and response quality. Check if the agent generates relevant, factually correct, and well-structured responses. Benchmark tests with sample queries can reveal precision and consistency — or lack of it.
4. 4.Task execution and autonomy. Evaluate how well the agent handles workflows, integrates with tools, and completes tasks with minimal human intervention. A well-functioning agent should follow logical steps and avoid errors.
5. 5.Latency and scalability. Measure response speed and system performance under increasing workloads. A scalable agent should remain effective even as the number of requests grows.
6. 6.Continuous monitoring and feedback. Observe the agent’s real-world behavior and collect user feedback to identify performance gaps.
7. 7.Security and compliance. Make sure the agent follows data privacy regulations, encryption standards, and access controls to protect sensitive information.

Using an LLM management platform like nexos.ai can streamline this process so you can manage, track, and optimize your LLM agents from a central dashboard.

What are the future prospects of LLM agents?

In the future, LLM agents are envisioned to become better at handling long conversations and complex queries and retaining deeper context. Beyond text, future models may support multiple formats, a single agent may be able to interpret and generate images, audio, and video. LLM agents should also integrate better with technologies like augmented reality (AR), the Internet of Things (IoT), and advanced robotics. Collaboration may also improve, with multiple AI agents working together to tackle large-scale data analysis and scientific research.

If you’re considering using LLM agents, start by defining clear objectives and choosing the right framework. With the right approach, these AI systems can become a game-changing tool for your business. And with the right LLM management platform, you can get the best out of a variety of LLM agents.