LLM Agents: A Complete Guide

Large language models (LLMs) are the engines powering AI, allowing people to ask simple questions and receive simple answers. But what if you need to do more than that? That’s where LLM agents excel. There are a few types of LLM agents, but they all handle more complex queries that require memory, sequential reasoning and the use of multiple tools.

LLMs can now handle more than a trillion parameters . And demand for agent-driven digital labour will continue to grow as companies look to grow their use of generative AI.

We'll break down how different types of LLM agents work, what they can do, the components they require, challenges they bring and how businesses are using these tools now and in the future.

What we'll cover:

• What are LLM agents?
• What can LLM agents do?
• Types of LLM agents and what they can do
• Components of an LLM agent
• How businesses are using different types of LLM agents
• Potential challenges of LLM agents
• The future of LLM agents
• Leading with LLMs

LLMs are artificial intelligence (AI) systems that use a combination of memory, planning and sequential reasoning to generate in-depth responses to user questions in a way that is similar to how a human would respond. Here's an example:

User 1 asks their company's internal chatbot, trained using an LLM, to pull up payroll statistics for the last year. The chatbot follows a preset process to search the relevant databases and return the specific dataset.

User 2, however, has a more in-depth question. They want to know how new federal and state laws may affect policies based on last year's payroll data. In this case, the chatbot falls short. While it can return data about payroll and information about new laws, it can't combine them into a meaningful answer — but LLM agents can.

Using a combination of machine learning (ML) and natural language processing (NLP), LLM agents can understand and respond to complex queries. These characteristics separate agents from traditional retrieval augmented generation (RAG) models, which pull data from internal sources to answer simple questions.

LLM agents can apply reason and logic to answer questions. Instead of simply taking a question at face value, agents can break queries into smaller parts to find answers. They then use their memory of the original question to combine answers and produce an accurate result. This allows AI agents to answer in-depth queries based on multiple datasets, create summaries from text, write code or generate plans.

LLM agents can improve their output over time by analysing and learning from previous interactions. In effect, agents can self-reflect on their behaviour, determine the success of this behaviour and make changes that improve outputs.

To improve with each task, LLM agents use tools such as web searches or code testers to verify accuracy and reduce response times. By continually evaluating answers against new and historical data, agents can identify and correct these errors.

It's also possible for agents to work in tandem. For example, one agent might take on the task of retrieving information and generating answers while another evaluates the output for accuracy. A third can assess the performance of both and suggest improvements. These agents then combine their data to produce a single, relevant result.

You can configure LLM agents to fulfil multiple roles, but the different types of agents aren't mutually exclusive. One agent can perform several functions simultaneously or in sequence.

Common types of LLM agents include:

• Task-specific LLM agents: These agents are designed to execute narrowly defined operations such as checking on a customer's order status or updating their delivery information. Typically, task-specific agents pull from a smaller pool of data, such as those offered by small language models (SLMs).
• Conversational LLM agents: Conversational AI agents can interact with end-users such as staff or customers and provide answers to common questions. The difference between these AI agents versus chatbot counterparts is that these agents learn over time, allowing them to produce more natural responses to user questions.
• Decision support LLM agents: Decision support agents surface insights from datasets or provide large-scale data summaries to improve decision-making. For example, a company's chief security information officer might use a decision support LLM to evaluate and summarise the impact of all corporate cybersecurity attacks over a given period.
• Workflow automation LLM agents: Workflow automation LLM agents automate multistep workflows. Autonomous agents are provided with the necessary system data, functional specifications and historical workflow performance to create an operational baseline. From there, agents carry out the assigned task and improve over time.
• Information retrieval LLM agents: RAG methods are used by these types of LLM agents to find and retrieve data from any system. Here, data retrieval is relatively straightforward. Agents excel in their ability to identify and capture relevant data and ignore extraneous information.
• Collaborative LLM agents: Collaborative agents operate alongside or in tandem with other agents. These agents effectively operate in parallel, which makes it possible to reduce response times without losing accuracy.
• Adaptive learning LLM agents: Adaptive agents use historical data to improve future performance. Agents evaluate past outputs and compare them to ideal results to develop new strategies.

Building an AI agent with LLM capabilities requires a large language model. This LLM generates and interprets natural language text and additional components like prompt engineering, memory modules or retrieval systems to enhance its contextual understanding and functionality. For all types of LLM agents, the three high-level components are brain, memory and planning.

The brain of an agent is a language model that can understand and respond to user questions. Agents use prompts — questions or statements made by users — to guide their decision-making and answer processes. Using solutions such as Agentforce, these brains may be customised with frameworks designed for specific situations, such as handling finance, HR or cybersecurity tasks.

Memory helps agents recall their previous actions to improve their next output. This can further be broken down into three types:

• Short-term: Short-term memory in agents applies to the current conversation or task. It's a record of user prompts and any actions taken so far by the agent.
• Long-term: Long-term memory is a repository of data from past conversations or prompts that may span weeks or months. This memory provides context to current tasks and can be reviewed by agents to identify possible errors and areas for improvement.
• Hybrid: Hybrid memory combines short- and long-term memory to improve agent responses in the moment by maintaining situational awareness and drawing on relevant information dynamically.

Planning modules improve responses by breaking complex tasks down into smaller parts:

• Plan formulation (with and without feedback): Plan formulation is the first step. Agents break down tasks into subtasks and then complete them. Plans may be created and executed with or without feedback. Without feedback, plans are developed and run by agents with no human oversight. With feedback, humans in the loop evaluate plans and make recommendations before plans are executed.
• Plan reflection: Plan reflection occurs after plans are carried out. Using a combination of internal assessment tools and external human feedback, plan reflection helps identify areas for improvement.

In practice, these components work together like a simplified human brain. Agent brains ingest and interpret user queries. Short-term memory is used to generate an understanding of the current task while long-term memory provides context. Planning splits complex tasks into subtasks, which are then completed to solve the problem and provide an answer.

Plan reflection helps reduce the risk of future errors by enabling them to critically evaluate their outputs, identify potential mistakes and improve the accuracy and coherence of their plans.

There are multiple ways to use these types of LLM agents, including:

• Sales: With sales AI, agents may be used to keep in touch to potential customers and provide product information, set up meetings with a sales rep or offer special pricing and promotions.
• Service: Service-based AI agents can answer customer questions, offer technical troubleshooting advice or escalate questions to human representatives.
• Commerce: By using AI for commerce, agents enable on-demand order status and tracking, order modifications and help with returns or refunds.
• Marketing: With their ability to handle complex prompts, LLM agents can use marketing AI to identify target audiences and create marketing campaigns that are personalised for different segments, based on past campaign performance.

While the advantages of AI and LLMs are significant, you could still face some challenges with various types of LLM agents. Common issues include:

If LLM agents aren't trained on enough data or the data lacks variety, it may create limited context. This reduces the agent's ability to produce relevant, actionable answers.

Agents excel at short-term planning but may struggle to handle requests for longer-term plans that stretch over months or years because of a lack of persistent memory, context window limitations and tool (and resource) integration gaps.

Inaccurate source data or unclear instructions can lead to inconsistent outputs. If the same query returns multiple results, it undermines the usefulness of LLM agents.

Agents can be customised to fill roles. The success of these roles, however, depends on the AI framework used. This is because the framework determines how effectively the agent can be trained, deployed and integrated with other tools and systems.

While prompts form the basis of agent answers, LLM agents should also use memory and self-reflection to inform responses. If these components are lacking or absent, it may limit the scope and accuracy of answers.

One example is prompt dependence. This occurs when LLMs “depend” on prompts to provide contextual clues about the desired output. In the best-case scenario, this leads to slightly biased outputs. In the worst case, outputs are inaccurate.

The sheer volume of knowledge handled and stored by LLM agents can lead to management challenges. These challenges may manifest as reduced performance or inaccurate responses.

Usually, LLM agents improve operational efficiency, which can mean increased ROI from using agents and savings across the business. But if agents don't integrate with existing systems or are built on resource-intensive frameworks, there can be increased costs and reduced efficiency.

As ML algorithms become more complex and chipsets more powerful, expect these types of LLM agents and AI agents to get smarter, faster and more capable of learning as they go. In practice, this creates an opportunity for these AI-powered chatbots to to work with their human counterparts rather than operating as an afterthought.

Consider B2B sales. Traditionally, employees might use LLMs to improve marketing or product copy and generate potential leads. With more advanced tools, staff can use agents to design and deliver in-depth email campaigns and field questions from customers. This offers the dual benefits of improved personalisation for customers and more time for staff to focus on building long-term sales strategies.

With enterprise AI seeing exponential growth, businesses are benefiting from various types of LLM agents. This technology offers a way for companies to improve customer service, enhance decision-making and handle complex, multistep problems.

Agentforce is helping companies take the lead with LLMs. By building and customising autonomous AI agents, businesses can combine the experience of human employees with the growing expertise of AI to support customers and employees 24/7. Try Agentforce for yourself to see how it can better help you to connect with customers and make your employees more efficient.