What are LLM Agents?

LLM agents can be used to: 

1. Answer questions, with greater relevance and accuracy.

2. Summarize texts, preserving only essential information.

3. Translate texts, with context and nuance.

4. Analyze sentiment, for social media monitoring, and more.

5. Create content, where unique and engaging material is required.

6. Extract data, like names, dates, events, or locations.

7. Generate code, debug, or even write entire programs. 

To do this, they rely on 2 core technologies: 

• Natural Language Understanding (NLU) enables them to comprehend human language and also deduce context, sentiment, intent, and nuance.

• Natural Language Generation (NLG) empowers them to create coherent and contextually relevant text. 

The power of LLM agents lies in their ability to generalize information from a huge amount of training data. This capability allows them to perform a wide range of tasks with high accuracy and relevance. And they can be customized and fine-tuned for specific use cases, from customer support to financial and healthcare services.

The architecture of Large Language Model (LLM) agents is typically based on neural networks, especially deep learning models designed to handle language tasks.

The key elements of LLM agent architecture include: 

• Transformer architecture 

  Transformers use self-attention, to prioritize the importance of different words in a sentence, and multi-head attention, to allow the model to focus on different parts of a sentence at the same time. Positional encodings are added to input embeddings to enable the transformers to understand the order of words. 

• Encoder-decoder structure 

  The encoder processes the input text, while the decoder generates the output. 

  While some models use only the encoder (like BERT) or only the decoder (like GPT), others (like T5) use both the encoder and the decoder.  

• Large-scale pre-training 

  Models are pre-trained on vast datasets containing diverse text from books, websites, and other sources. Pre-training helps the model understand language patterns, facts, and general knowledge. 

• Fine-tuning 

  After pre-training, models often go through fine-tuning on domain-specific data to enhance their performance in tasks like customer service, for example. 

LLM agents can be divided into 4 components: 

1. Brain 

The brain of the agent is your large language model itself, trained to understand human language based on the vast volume of data it's been fed. 

2. Memory 

Memory allows the agent to handle complex tasks by reviewing past events and analyzing what was done in each case.  

Short-term memory is like the agent’s notebook, where it jots down key points during a conversation. By keeping track of the ongoing discussion, it helps your model respond with context. The problem with short-term memories is that they’re forgetten once the task is done.

Long-term memory is comparable to the agent’s diary, where it stores insights from past interactions. It’s used to study patterns, learn from previous actions, and recall this information to make better decisions when faced with simalr sets of circumstances in the future.

By combining both types of memory, the model can keep up with current conversations and also be able to draw on a rich history of interactions. An agent uses this combined memory to enable your LLM to respond with a high level of AI personalization for a superior user experience. 

3. Planning 

LLM agents can employ chain-of-thought prompting to subdivide larger tasks into smaller, more manageable parts, and formulate specific plans for each subtask. As tasks evolve, agents can also reflect on particular plans to ensure relevance to real-world scenarios – which is critical to successful task completion.

During the plan formulation stage, agents break down a large task into smaller sub-tasks. With chain-of-thought reasoning, agents can address sub-tasks one by one, allowing for greater flexibility.

In the plan reflection stage, agents review and assess the plan’s effectiveness. While LLM agents can draw upon internal and environmental feedback mechanisms to refine their strategies, they can also have a human in the loop to adjust their plans based on professional experience.  

4. Auxilliary tools  

Auxilliary tools are third-party resources that let LLM agents connect with external environments to write code, respond to queries, retrieve data from data lakes, or any other task the agent needs to perform to get the job done.  

Here are some examples of auxilliary tools developed at Cornell University:  

• API-Bank is a benchmark that tests how well LLMs can use APIs to handle health data management, scheduling, or smart home control. 

• HuggingGPT is a GPT-based framework that manages tasks by choosing the best HuggingFace model to handle a specific request and summarizing the results. 

• MRKL (Modular Reasoning, Knowledge, and Language) has your LLM route queries to expert modules, ranging from simple calculators and weather APIs to neural networks. 

• ‍Toolformer  and  TALM (Tool Augmented Language Models) connect your LLM to external APIs, such as a financial API that analyzes stock market trends or forecasts currency fluctuations. 

An LLM agent framework makes use of functions, which can be defined as executable units of programming logic designed to achieve specific goals. Functions can be intrinsic, embedded in your LLM, external, called upon when needed, or hybrid, a combination of the two.  

Intrinsic functions are built-in to your LLM 

Intrinsic functionality consists of: 

• Text processing 

  Text processing includes transforming LLM text to SQL, tagging of parts of speech, tokenization, and Named Entity Recognition (NER) which detects and classifies entities like names, dates, events, etc. 

• Natural Language Understanding (NLU) 

  NLU includes intent recognition, which attempts to understand the purpose of the query, sentiment analysis, which determines the emotional tone of voice of the text, and semantic parsing, which converts natural language into structured data or commands. 

• Natural Language Generation (NLG) 

  NLG includes text generation, designed to create human-like text based on prompt engineering techniques like paraphrasing, which conveys the same meaning in different words, and summarizing, which condenses longer texts into shorter versions while retaining the most essential information. 

External functions interact with other systems 

External functionality consists of: 

• Database queries 

  Database queries include SQL functions that write and execute SQL queries to retrieve or manipulate data from databases. 

• API integration 

  API integration includes web requests, that send HTTP requests to external APIs to make sure the relevant data is available, and service integrations, that interact with various external services (e.g., stock information, weather data, etc.). 

• Custom logic 

  Custom logic includes rules-based systems, which apply pre-defined rules to making decisions or taking actions, and the execution of specialized algorithms for tasks like recommendation systems, sorting, etc. 

Hybrid apporaches combine instrinsic and external functions 

Examples of hybrid functionality include: 

• Workflow automation, in which, for example, an agent extracts data from text intrinsically and then uses that same data to update a database externally. 

• Dialog management, which controls conversations by integrating NLU, NLG, and external functions. 

LLM agent function considerations 

Below are 3 key considerations for LLM function calling: 

• Security 

  When interacting with external systems or databases, ensure that secure methods and protocols are used to protect sensitive data according to pre-defined LLM guardrails. 

• Efficiency 

  Optimize function calls to minimize latency and computational overheads. 

• Scalability 

  Design functions to handle varying loads and scalable interactions, especially for applications with high user engagement. 

By leveraging different types of LLM function calling, LLM agents can effectively perform a wide variety of tasks, making them highly versatile and powerful tools in numerous GenAI applications. 

An LLM agent arhcitecture can solve complex problems, learn from mistakes, employ various tools to enhance their effectiveness, and even collaborate with other agents to improve their performance. Their key capabilities include: 

• Problem solving 

  To solve complex problems, LLM agents can generate project plans, write code, monitor benchmarks, and deliver summaries. 

• Self-evaluation 

  To evaluate their outputs, LLM agents can run unit tests on their code or search the web to verify the accuracy of the information they provide. 

• Performance improvement 

  To improve their performance, LLM agents can identify errors and correct them on the fly, and even work together to critique individual responses.  

  
   

While LLM agents can be incredibly useful, they also face several challenges, including: 

1. Risks of accessing live systems 
   Direct access to operational systems can quickly result in unmanageable spaghetti code, load issues on operational data sources, and security concerns of limiting user access to data, with each function having to handle access controls on its own. LLM function calling can be a great asset but must be controlled.
   
   
   
   
2. Poor at context 
   LLM agents can only keep track of a relatively small amount of information at any given time, meaning that they might not remember important details from earlier dialog or miss important instructions. An LLM vector database can help by providing access to more information, but that doesn’t really solve the problem. 
3. Limited ability to plan 
   LLM agents can’t plan for the long term because they don’t easily adapt to unexpected scenarios. This lack of flexibility often requires having a human in the loop. 
4. Inconsistent outputs 
   LLM agents rely exclusively on natural language to interact with other tools and databases, so they sometimes produce unreliable outputs. They might make formatting mistakes or not follow instructions correctly, which can lead to errors in the tasks they perform. 
5. Dependence on good prompts  
   LLM agents are activated via AI prompt engineering, but the resultant prompts must be very precise. Even slight variations can lead to massive mistakes, so creating and refining prompts is a serious business. 
6. Difficulty adapting to different roles 
   LLM agents must match their roles to different tasks, but fine-tuning them – to assume unusual roles or empathize with human feelings – is an impossible task. 
7. Lack of data readiness 
   Data readiness can make or break your GenAI projects. Keeping your LLM data AI-ready – protected, complete, and accessible in real time –  isn’t trivial. LLM agents are tasked with providing the data needed to make informed decisions. But irrelevant information can lead to incorrect conclusions. 
8. Cost and efficiency 
   Running LLM agents can be resource-heavy. When lots of data needs to be processed quickly, costs go up and performance goes down if not managed properly. 

Addressing these challenges while comparing the effectiveness of prompt engineering vs fine-tuning, is crucial for improving the effectiveness and reliability of LLM agents in various applications. 

K2view is rethinking enterprise data and how we organize it for generative AI. Instead of "going macro" – trawling for data in a big data lake and then hardcoding hundreds of functions to an ever-growing amount of questions –we’re "going micro" by organizing all the data for a single entity (say, specific customer) in its own dedicated Micro-Database™. 

The Micro-Database can be queried in an instant to field any question.

GenAI Data Fusion, a revolutionary RAG tool by K2view, features a no-code LLM data agent builder enabling: 

• Chain-of-thought and RAG prompt engineering 

• Automated Text-to-SQL, data retrieval, and data summary 

• 200+ prebuilt data processing functions 

• LLM abstraction capabilities 

• Multi-agent system design 

• Built-in interactive visual debugger 

K2view closes the generative AI data gap by enabling you to use your enterprise data to personalize LLMs to your business. LLMs are then always ready to handle any GenAI question, by anyone, while never sacrificing data privacy and security through the enforcement of LLM guardrails.

Discover GenAI Data Fusion by K2view, the market-leading suite of RAG tools that puts LLM agents and functions to best use.