AI tools are evolving, and models that simply respond to commands are being replaced with complex systems that can reason, plan and act. Large language model (LLM) agents bridge the gap between a basic factual response from an AI tool and one that’s clear, contextual and informed by reasoning.
In this guide, we’ll uncover what LLM agents are, how they work and some of the key challenges they present.
LLM agents are built around a large language model that processes and generates text based on a set of data it’s been trained on. An LLM on its own generates responses purely based on the prompt it’s given. LLM agents go a step further, providing a more human-like response from generative AI tools. For example, when you ask a question in ChatGPT, the response often goes beyond facts – it anticipates needs, adjusts to your tone and references earlier parts of the conversation. With the ability to think ahead, remember past conversations and use tools to adjust their responses based on the context, LLM agents are complex AI systems that can create text with sequential reasoning.
Every LLM agent starts with a user request, or prompt. When you give an AI tool instructions, you provide it with a goal that helps guide the agent on how to respond, what tools to use and whether to retrieve memory. Think of it like setting the destination for a GPS – the clearer your instructions, the better the route it can plan.
The agent then assesses the prompt and determines what needs to be done, deciding how to break down the task, in what order and with what resources. Some frameworks use hardcoded rules for this, whereas others use additional models to generate action plans dynamically. These plans may include selecting which tools to use, retrieving relevant memory or even prompting another model to handle a subtask. At this stage, the agent transforms a simple user request into a structured strategy for execution.
Once the goal is understood, the agent creates a plan. This involves breaking down tasks into subtasks, deciding the sequence of tools and setting up a loop to revise outputs based on intermediate results. Planning is critical for tasks that require multiple steps or decisions, such as writing code, conducting research or automating workflows. Some advanced agents also use feedback loops, where they evaluate their own outputs and refine them before returning a final answer, improving quality and reducing error rates over time.
Memory allows LLM Agents to retain context across steps and sessions, creating a more informed response. This includes tracking what’s been said or done in the current session as well as retaining information over time, such as user preferences and prior tasks. It also retrieves vector databases, used to store and retrieve embeddings of previous interactions. With memory, LLM agents can build on past tasks and maintain coherent, ongoing conversations.
Using tools, LLM Agents can go beyond what they were trained on, interacting with live or external data. This can include web searches and gathering information from the internet, API calls for weather, stock data or news, code execution or file environments like reading or writing documents. The ability to look further than the tool itself makes LLM agents more dynamic and practical in real-world applications.
The effectiveness of an LLM agent often hinges on the quality of the prompt it receives. Crafting clear, specific prompts is a skill in itself, and poor prompting can lead to irrelevant, incomplete or misleading results. To get the most from LLM agents, users should focus on being explicit about the task and desired format. Providing examples or using structured prompts like step-by-step breakdowns can significantly improve the quality of the response.
The truth is, AI tools and LLM agents are not a replacement for human output. Despite advances in large language models, LLM agents are still prone to generating incorrect or misleading information. Often, they produce outputs that sound plausible but, in reality, are factually inaccurate. Human oversight is essential, and AI tools are best used to augment human workflows, rather than replace them.
Giving agents access to tools, APIs or private data introduces a layer of risk. Without proper sandboxing or access controls, agents may expose sensitive information, make unintended changes or fall victim to security attacks. Strong security practices such as input validation and permission restrictions are key to safe deployment of AI.
As LLM Agents begin to automate more cognitive tasks, from content creation to decision-making, ethical considerations are becoming increasingly important – and it’s important that decision-makers implement it responsibly. Some of the ethical questions arising are around:
These are all open questions that require ongoing attention from developers, regulators, and users alike.
As AI tools progress, we’ll likely see more reliable, secure and efficient LLM agent systems deployed across industries, such as healthcare, finance and the energy sector. Their ability to reason, plan, remember and act is changing how we operate, and understanding their architecture and limitations is the first step to using them effectively and responsibly.
Speak to the team to discover how LLM agents could enhance your business or product, and turn your idea into a digital reality.
