LLM Agents Explained: Why Your Enterprise Can't Afford to Ignore Them

Rapid advances in large language models (LLMs) have redefined the AI market. Well-known LLMs such as GPT-4, Llama, Claude and Cohere have demonstrated remarkable capabilities: they can understand, generate, and apply natural language akin to humans. In fact, Gemini Ultra was the first LLM to achieve human-level performance on the Massive Multitask Language Understanding (MMLU) benchmark, further cementing progress in this area.

These strides have paved the way for the emergence of LLM agents—systems that can perform specific tasks autonomously. These agents have already demonstrated their capabilities in creating marketing copy, writing code, deriving insights from census data, and even assisting healthcare providers.

What are large language model agents, and how can they help your business? Read on to find out.

What Is an LLM Agent and What Can It Do?

LLM agents are advanced AI systems that rely on large language models like GPT-4 to perform various tasks. They are versatile, able to answer customer queries and assist in decision-making based on large datasets.

Contrary to popular belief, large language model agents are not the same as conversational LLMs like ChatGPT. Although both can understand natural language and mimic human communication patterns, LLMs go further because of their capabilities to:

• Perform complex tasks. To properly process user requests, LLM agents can break down tasks into manageable chunks and use external data sources and third-party tools.

• Act independently. An LLM agent can work independently without being constantly guided by a user. It can make decisions and take action based on what it has learned and the instructions it receives. For example, if you ask it to summarize a document, it knows that it should read the document first and then create a summary.

The most popular LLM agents include AutoGPT (a command-based agent designed for developers and other technical proficient users), GPT-Engineer (specialized in software engineering), and AgentGPT (a browser-based with self-prompting capabilities). 

Large language model agents are synonymous with innovation. That’s why it's hard to believe their journey began over six decades ago.

The evolution of LLM technology and the introduction of LLM agents 

It has taken more than half a century for LLM agents to develop into what they are today. It all began in the early 1960s with ELIZA, a rule-based system that could carry on a human-like conversation. It did not understand language but used scripts to give the impression that it did. Although ELIZA was not a true LLM by today’s standards, it laid an important foundation for future developments in natural language processing (NLP) and AI.

The following milestones in the development of LLM agents included:

• The introduction of neural networks in the 1980s-1990s that made it possible for machines to learn from data. This gave rise to recurrent neural networks (RNNs), which can process sequential data—a type of data where the order of elements is important.

• The introduction of transformer architecture in 2017 that revolutionized natural language processing, significantly enhancing the efficiency and scalability of large language models. Unlike previous models, transformers can process words in relation to all other words in a sentence, focus on important words in a sentence, and process multiple words simultaneously. 

• The release of GPT models. While GPT-2 (2019) demonstrated the potential of transformers to generate coherent text, GPT-3 (2020) extended the capabilities of the previous version with 175 billion parameters, making it one of the most powerful large language models at the time.

• Bidirectional Encoder Representations from Transformers (BERT), a new approach to understanding context in language that was introduced in 2018. In contrast to previous models, which generally only processed the text in one direction, BERT-based LLMs can understand the context of a word based on the preceding and following words in a sentence.

• The ChatGPT launch in November 2022 that made generative AI accessible to a broader audience. ChatGPT attained over 100 million users within just two months of its release, making it one of the fastest-growing consumer applications in history. The success of ChatGPT sparked the integration of AI tools into everyday practices.

The initial success of conversational agents like ChatGPT encouraged research into how large language models could be used in more complex, goal-oriented applications beyond human-like conversations. This is how LLM agents came about.

In early 2023, research on LLM agents emphasized their potential for autonomous functionality and task management. The OpenAI Developer Conference in November 2023 introduced customized versions of ChatGPT, solidifying the role of LLM agents as a mainstream product in AI technology.

The Key Components Behind the Facade of a Knowledgeable Assistant

From an architectural point of view, an LLM agent essentially consists of a series of interconnected modules that enable it to perform complex tasks. 

Let’s look at these components in detail.

Perception

This component is responsible for gathering information, which might involve diverse forms of input, such as text, speech, or images. The quality and breadth of the data collected directly impact the agent's ability to understand user intent and respond effectively.

Agent

The basis of the agent component is a large language model. It plays a crucial role in processing user input, understanding the context, and generating relevant responses. Trained on extensive data sets, this component can capture the nuances of language and perform tasks with precision.

The agent component is activated by a user prompt, which can include any query that matches the agent's area of expertise. In response to this prompt, the agent populates a prompt template that lists the next steps it should take and the tools available for use.

Planning module

The planning module plays a critical role in the agent's decision-making process. This module helps the agent break down tasks into smaller, manageable steps, allowing the agent to master complicated tasks step by step.

The planning module can operate in two modes:

• Planning with feedback, where the agent refines its actions based on real-time feedback from previous interactions. This helps the agent to adapt its strategies and improve its performance over time.

• Planning without feedback, where the agent executes tasks based on predefined plans without real-time adjustments. This is useful for simple tasks where the path to completion is clear and approval of steps is not required.

Memory module

The memory module is crucial for the agent's contextual understanding and interaction continuity. It stores the agent’s past reasoning, actions, and observations.

Memory in the LLM context can be divided into two types:

• Short-term memory allows an agent to retain information from recent interactions. It helps maintain context during ongoing conversations or tasks.
• Long-term memory stores information over longer periods. It enables a deep understanding of user preferences and stores historical data that can inform future interactions.

The memory module complements the planning one. Both allow the agent to draw on past experience and anticipate future needs to deliver effective and customized solutions.

Tools

LLM agents use external tools like the Wikipedia Search API and the Code Interpreter to extend their capabilities beyond language processing. These tools are tailored to specific tasks and help LLM agents tackle various challenges.

The tools used by LLM agents can be categorized into four groups:

• Data analysis tools designed to perform complex calculations or analyze large data sets.
• Web browsers, which allow agents to search the internet, access websites, and gather real-time data.
• Text-to-speech and speech-to-text tools intended to convert text to audio and vice versa, enabling voice interactions.
• APIs, which provide access to external databases or services. They extend the agent's ability to retrieve and process information from a larger number of sources.

These components help agents seamlessly interact with different systems and data sources to provide more comprehensive solutions. Let’s see them in action.

How Does an LLM Agent Function?

As illustrated above, a sophisticated, autonomous "expert" that interacts meaningfully with users relies on the interplay of several components working together.

This is how these components work when a user makes a request:

1. A user submits a query or request. 
2. The perception component collects and processes the input data.
3. The LLM analyzes the request and determines its intent.
4. The planning module breaks down the query into smaller, manageable steps. The memory module also kicks in to draw on information from recent interactions and historical data to help the agent develop the most effective solution.
5. Based on the developed strategy, the agent selects the appropriate tools to accomplish the task.
6. The agent creates a response based on the processed information, contextual understanding, and insights from the tools.
7. The agent sends contextually relevant responses back to the user, completing the interaction. 

The agent can also store this interaction in its memory so that it can provide even better-personalized responses in subsequent interactions. 

Let’s move from theory to practice and explore how LLM agents perform in real-world scenarios.

LLM Agents in Action: Industry Use Cases and Real-World Implementations 

Equipped with LLM agents' capabilities in reasoning, planning, and applying external sources and tools, organizations can increase efficiency, improve the user experience, and innovate faster.

McKinsey reports that GenAI (and LLM agents in particular) have the potential to generate $2.6 trillion to $4.4 trillion in global business value in 63 use cases. Here are some of the most promising applications.

Customer support

LLM agents can respond immediately to queries, resolve issues, and guide users through processes. These capabilities have led 82% of 600 surveyed CX leaders to redefine customer care.

Zendesk has integrated LLM agents into its customer support platform, allowing businesses to automate responses to common customer inquiries. The agents can handle multiple queries simultaneously, reducing response times and improving customer satisfaction. 

Committed to making the best technologies accessible to its customers, Zendesk continuously improves the support agent experience. In April 2024, it announced a collaboration with Anthropic and AWS and, a month later, with OpenAI, making GPT-4o accessible to Zendesk users.

Content creation

LLM agents can help create high-quality written content, including articles, marketing copy, and social media posts. So, it’s no wonder that 43% of over 1,000 marketers surveyed by HubSpot use AI tools for content creation.

One such solution is Copy.ai, which has effectively integrated LLM technology to help marketers and editors produce creative content quickly. The agent significantly speeds up the content creation process and helps eliminate writer's block. Users simply type in a few keywords or phrases, and the LLM agent creates engaging text.

Programming assistance

LLM agents can support developers in many ways: by suggesting code, assisting with bug fixing, and even generating complete code snippets based on user input.

GitHub Copilot, an AI tool based on the OpenAI codex, acts as a virtual programming partner. As developers write code, Copilot provides real-time code completion suggestions and snippets. Research shows that developers using GitHub Copilot can write code up to 55% faster than with traditional methods.

Data analysis

LLM agents can analyze extensive data sets, derive valuable insights, and create visualizations. In other words, they are invaluable tools for making informed decisions.

NVIDIA, for example, offers a framework for developing LLM agents. These agents can interact with structured databases via SQL or APIs such as Quandl, extract necessary information from financial reports (10-K and 10-Q), and perform complex data analysis tasks.

Healthcare support

LLM agents can support healthcare professionals by providing quick access to medical information, medical history, and treatment recommendations. Google's Med-PaLM 2 scored 85% on medical exams, matching the level of a human expert, while GPT-4 earned 86% on the United States Medical Licensing Examination (USMLE), demonstrating its potential to support clinical decision-making and education.

The future of large language models in healthcare is bright and will lead to more efficient, personalized, and accessible medical services.

Conclusion

With their exceptional reasoning and interaction capabilities, LLM agents are poised to revolutionize business processes in various sectors. These advanced systems are already prevalent, finding applications in healthcare, customer service, data analysis, and content creation. And one thing is certain: their presence will persist, leading to the development of even more intelligent and responsive systems.

If you want to make LLM agent capabilities serve your needs best, a custom assistant is the ideal solution. You can quickly create, train, and deploy a tailored LLM agent in your infrastructure and with your data with minimal investment through a user-friendly interface. Dynamiq can make it possible for you, so feel free to book a demo.