Complete Guide to LLM Agents (2025)

'LLM agents' on a blue and black background.

Diagram that reads 'LLM + retrieval, reasoning, memory, tool use = LLM agent

LLM agents are a subset of AI agents that use large language models to complete language-based tasks.

While the broad category of AI agents include non-linguistic applications (content recommendation systems, image recognition, robotical control, etc.), LLM agents are typically conversational AI software.

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What are LLM agents?

LLM agents are AI-powered tools that use large language models to interpret language, have conversations, and perform tasks.

These agents are built on complex algorithms trained on vast amounts of text data, enabling them to comprehend and produce language in a way that mimics human-like communication.

LLM agents can be integrated into AI agents, AI chatbots, virtual assistants, content generation software, and other applied tools.

How do LLM agents work?

LLM agents combine the power of an LLM with retrieval, reasoning, memory, and tool use to autonomously complete tasks. Let’s break down what each of these components does.

Retrieval	Most agents use retrieval-augmented generation (RAG) to access real-time data, like regulatory changes for legal agents or product inventory for e-commerce agents.
Reasoning	LLM agent builders can use advanced prompting techniques that guide the agent on how to make decisions.
Memory	LLM agents have short-term memory (recent conversation history), long-term memory (storage of structured information), and task-specific recall, allowing them to adapt and personalize decisions.
Tool Use	Beyond conversations, LLM agents can interact with nearly any external system to get data or perform actions. They can run Python code to debug an error or check a user’s order history in a CRM.

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In combination, these capabilities allow LLM agents to carry out complex, multi-step workflows with complete autonomy.

For example:

A B2B sales agent retrieves CRM data on a prospect, analyzes the deal progression, remembers past interactions with the prospect so it can personalize the follow-up, and uses email and calendar APIs to send and schedule.
An IT agent retrieves system logs to diagnose an error, analyzes troubleshooting steps for the best strategy, remembers what worked in previous user issues, and executes scripts to restart services or create a ticket.

Features of LLM Agents

There are four key features of an LLM agent:

Language Model

The language model is often considered the "brain" of an LLM agent. Its quality and scale directly influence the performance of the LLM agent.

It’s a sophisticated algorithm trained on enormous datasets of text, which allows it to understand context, recognize patterns, and produce coherent and contextually relevant responses.

Identify and learn language patterns
Gain a degree of contextual awareness (thanks to its vast training data)
Adapt across different domains and handle a wide range of topics

The language model determines the depth, accuracy, and relevance of responses, which forms the foundation of the agent’s language capabilities.

Memory

Memory refers to the capability to retain information from past interactions, like facts, user preferences or topics across sessions.

This enhances the agent's contextual understanding and makes conversations more continuous and relevant.

In some setups, memory allows the agent to retain information over time. This supports long-term interaction where the agent "learns" from repeated user behavior or preferences – though this is often regulated for privacy and relevance.

Tool Use

Its tool use takes an LLM agent from conversation to action.

An LLM agent can integrate with external applications, databases, or APIs to perform specific functions.

This means they can fetch real-time information, execute external actions, or access specialized databases, giving it the ability to provide real-time information. This includes:

Calling APIs
Pulling in live data, like weather updates or stock prices
Scheduling meetings or appointments
Querying databases, like product catalogs or HR policy documents

Tool use allows the LLM agent to move from a passive, knowledge-based system to an active participant capable of interfacing with other systems.

Planning

Planning is the ability of an LLM agent to break down complex tasks into a series of manageable steps.

An LLM agent can plan with or without feedback. The difference?

Planning without feedback means the LLM agent will create a plan based on its initial understanding. It’s faster and simpler, but lacks adaptability.
Planning with feedback means an LLM agent can continuously refine its plan, taking input from its environment. It’s more complex, but makes it far more flexible and improves performance over time.

By planning, an LLM agent can create logical flows that move progressively toward a solution, making it more effective in handling complex requests.

Types of LLM Agents

Conversational Agents

These kinds of agents engage in natural dialogue with users – they often provide information, answer questions, and assist with various tasks.

These agents rely on LLMs to understand and generate human-like responses.

Examples: Customer support agents and healthcare chatbots

Task-Oriented Agents

Focused on performing specific tasks or achieving predefined objectives, these agents interact with users to understand their needs and then execute actions to fulfill those needs.

Examples: AI assistants and HR bots

Creative Agents

Capable of generating original and creative content such as artwork, music, or writing, these agents use LLMs to understand human preferences and artistic styles, enabling them to produce content that resonates with audiences.

Examples: Content generation tools and image generation tools (like Dall-E)

Collaborative Agents

These agents work alongside humans to accomplish shared goals or tasks, facilitating communication, coordination, and cooperation between team members or between humans and machines.

LLMs may support collaborative agents by assisting in decision-making, generating reports, or providing insights.

Examples: Most enterprise AI agents and project management chatbots

Enterprise Use Cases

Enterprises benefit from LLM agents in areas that involve processing and responding to natural language, like answering questions, providing guidance, automating workflows, and analyzing text.

Enterprises often use LLM agents for marketing, data analysis, compliance, legal assistance, healthcare support, financial tasks, and education.

Here are 3 of the most popular use cases of LLM agents:

Customer Support

LLM agents are widely used in customer support to handle FAQs, troubleshoot issues, and provide 24/7 assistance.

These agents can engage with customers in real time, offering immediate help or escalating complex inquiries to human agents.

Sales and Lead Generation

In sales, LLM agents are used for AI lead generation — they can even qualify leads afterwards by engaging potential customers in conversations, assessing needs, and gathering valuable information.

They can also automate follow-up interactions, sending personalized recommendations or product information based on the customer’s interests.

Internal Support: HR and IT

For internal support, LLM agents streamline HR and IT processes by handling common inquiries from employees. In HR, they answer questions on topics like benefits, leave policies, and payroll, while in IT, they provide troubleshooting for basic technical issues or automate routine tasks like account setup.

This allows HR and IT teams to focus on more complex responsibilities, instead of repetitive busywork.

How to Build an LLM Agent

Define Objectives

The first step in building an AI agent or chatbot is to nail down what exactly it is that you want it to accomplish.

Clarify what you want the LLM agent to achieve, whether it’s assisting with customer inquiries, generating content, or handling specific tasks.

Identifying clear goals will shape the agent’s setup and configuration.

Choose an AI Platform

The best AI platforms will depend entirely on your goals and needs.

Select a platform that aligns with your requirements, considering factors like customization options, integration capabilities, ease of use, and support.

The platform should:

Support your desired use case
Offer your preferred LLMs
Offer integration capabilities

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Configure the LLM

Based on the platform’s options, either choose a pre-built LLM or fine-tune a model for specialized tasks if necessary.

Many platforms offer built-in language models that are pre-trained and ready to use.

If you’re interested in customizing your LLM usage, read our article on picking a custom LLM option for your AI project from our growth engineer, Patrick Hamelin.

Integrate Tools

Most platforms provide integration options for external tools. Connect any APIs, databases, or resources your agent will need to access, such as CRM data or real-time information.

Test and Refine

Test the agent thoroughly using the platform’s built-in testing tools. Adjust parameters, prompt phrasing, and workflows based on testing outcomes to ensure the agent performs well in real scenarios.

Deploy and Monitor

Use the platform’s monitoring tools to track the agent’s interactions and performance after deployment.

Gather insights and refine the setup as needed, taking advantage of any feedback mechanisms provided by the platform.

Deploy a custom LLM agent

LLM agents are reaching mass adoption rates amongst enterprises – in customer service, internal operations, and e-commerce. The companies that are slow to adopt will feel the consequences of missing the AI wave.

Botpress is an endlessly extensible AI agent platform built for enterprises. Our stack allows developers to build LLM agents with any capabilities you could need.

Our enhanced security suite ensures that customer data is always protected, and fully controlled by your development team.

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