LLM agents: The ultimate guide

When you face a problem with no simple answer, you often need to follow several steps, think carefully, and remember what you’ve already tried. LLM agents are designed for exactly these kinds of situations in language model applications. They combine thorough data analysis, strategic planning, data retrieval, and the ability to learn from past actions to solve complex issues.

In this article, we'll explore what LLM agents are, their benefits, abilities, practical examples, and the challenges they face.

What are LLM agents?

LLM agents are advanced AI systems designed for creating complex text that needs sequential reasoning. They can think ahead, remember past conversations, and use different tools to adjust their responses based on the situation and style needed.

Consider a question in the legal field that sounds like this:

"What are the potential legal outcomes of a specific type of contract breach in California?"

A basic LLM with a retrieval augmented generation (RAG) system can easily fetch the needed information from legal databases.

Now, consider a more detailed scenario:

"In light of new data privacy laws, what are the common legal challenges companies face, and how have courts addressed these issues?"

This question digs deeper than just looking up facts. It's about understanding new rules, how they affect different companies, and finding out what courts have said about it all. A simple RAG system can pull up relevant laws and cases, but it lacks the ability to connect these laws to actual business situations or analyze court decisions in depth.

In such situations, when the project demands sequential reasoning, planning, and memory, LLM agents come into play.

For this question, the agent can break down its tasks into subtasks like so. The first subtask may be accessing legal databases to retrieve the latest laws and regulations. Secondly, it can establish a historical baseline of how similar issues were previously handled. Another subtask can be summarizing legal documents and forecasting future trends based on observed patterns.

To complete these subtasks, the LLM agent requires a structured plan, a reliable memory to track progress, and access to necessary tools. These components form the backbone of an LLM agent’s workflow.

LLM agent components

LLM agents generally consist of four components:

• Agent/brain
• Planning
• Memory
• Tool use

Let’s discuss each of them.

Agent/brain

At the core of an LLM agent is a language model (or an action model) that processes and understands language based on a vast amount of data it's been trained on.

When you use an LLM agent, you start by giving it a specific prompt. This prompt is crucial—it guides the agent on how to respond, what tools to use, and the goals it should aim to achieve during the interaction. It's like giving directions to a navigator before a journey.

Additionally, you can customize the agent with a specific persona. This means setting up the agent with certain characteristics and expertise that make it better suited for particular tasks or interactions. It's about tuning the agent to perform tasks in a way that feels right for the situation.

Essentially, the core of an LLM agent combines advanced processing abilities with customizable features to effectively handle and adapt to various tasks and interactions.

Memory

The memory of LLM agents helps them handle complex LLM tasks with a record of what’s been done before. There are two main memory types:

Short-term memory: This is like the agent’s notepad, where it quickly writes down important details during a conversation. It keeps track of the ongoing discussion, helping the model respond relevantly to the immediate context. However, this memory is temporary, clearing out once the task at hand is completed.

Long-term memory: Think of this as the agent’s diary, storing insights and information from past interactions over weeks or even months. This isn't just about holding data; it's about understanding patterns, learning from previous tasks, and recalling this information to make better decisions in future interactions.

By blending these two types of memory, the model can keep up with current conversations and tap into a rich history of interactions. This means it can offer more tailored responses and remember user preferences over time, making each conversation feel more connected and relevant. In essence, the agent is building an understanding that helps it serve you better in each interaction.

Planning

Through planning, LLM agents can reason, break down complicated tasks into smaller, more manageable parts, and develop specific plans for each part. As tasks evolve, agents can also reflect on and adjust their plans, making sure they stay relevant to real-world situations. This adaptability is key to successfully completing tasks.
Planning typically involves two main stages: plan formulation and plan reflection.

Plan formulation

During this stage, agents break down a large task into smaller sub-tasks. Some task decomposition approaches suggest creating a detailed plan all at once and then following it step by step. Others, like the chain of thought (CoT) method, recommend a more adaptive strategy where agents tackle sub-tasks one by one, allowing for greater flexibility. Tree of thought (ToT) is another approach that takes the CoT technique further by exploring different paths to solve a problem. It breaks the problem into several steps, generating multiple ideas at each step and arranging them like branches on a tree.

There are also methods that use a hierarchical approach or structure plans like a decision tree, considering all possible options before finalizing a plan. While LLM-based agents are generally knowledgeable, they sometimes struggle with tasks that require specialized knowledge. Integrating these agents with domain-specific planners has proven to improve their performance.

Plan reflection

After creating a plan, it’s important for agents to review and assess its effectiveness. LLM-based agents use internal feedback mechanisms, drawing on existing models to refine their strategies. They also interact with humans to adjust their plans based on human feedback and preferences. Agents can also gather insights from their environments, both real and virtual, using outcomes and observations to refine their plans further.

Two effective methods for incorporating feedback in planning are ReAct and Reflexion.

ReAct, for instance, helps an LLM solve complex tasks by cycling through a sequence of thought, action, and observation, repeating these steps as needed. It takes in feedback from the environment, which can include observations as well as input from humans or other models. This method allows the LLM to adjust its approach based on real-time feedback, enhancing its ability to answer questions more effectively.

Tools use

Tools in this term are various resources that help LLM agents connect with external environments to perform certain tasks. These tasks might include extracting information from databases, querying, coding, and anything else the agent needs to function. When an LLM agent uses these tools, it follows specific workflows to carry out tasks, gather observations, or collect the information needed to complete subtasks and fulfill user requests.

Here are some examples of how different systems integrate these tools:

• MRKL (Modular reasoning, knowledge, and language): This system uses a collection of expert modules, ranging from neural networks to simple tools like calculators or weather APIs. The main LLM acts as a router, directing queries to the appropriate expert module based on the task.

In one test, an LLM was trained to use a calculator for arithmetic problems. The study found that while the LLM could handle direct math queries, it struggled with word problems that required extracting numbers and operations from text. This highlights the importance of knowing when and how to use external tools effectively.

Here’s an example where GPT 4 is asked to tell the answer to 4.1 * 7.9, and it fails:

• ‍Toolformer and TALM (Tool Augmented Language Models): These models are specifically fine-tuned to interact with external APIs effectively. For instance, the model could be trained to use a financial API to analyze stock market trends or predict currency fluctuations, allowing it to provide real-time financial insights directly to users.

• HuggingGPT: This framework uses ChatGPT to manage tasks by selecting the best models available on the HuggingFace platform to handle specific requests and then summarizing the outcomes.
• API-Bank: A benchmark that tests how well LLMs can use 53 commonly used APIs to handle tasks like scheduling, health data management, or smart home control.

How SuperAnnotate helps improve LLM agents

SuperAnnotate works with several leading companies that develop LLM Agent systems, helping them build better models more quickly.

We help enterprises with:

Fine-tuning: Fine-tuning can enhance an agent's performance on specific tasks or teach it new capabilities. It involves training your model on large datasets of input-output pairs that clearly demonstrate what you need the model to achieve.

Evaluation: Figuring out how the agent will perform on data it hasn't seen before can be tough. Most LLM evaluation datasets out there are geared towards academic domains and may not fit your specific needs. Also, keep in mind that some base models may have been trained on the benchmarking datasets, which could affect their ability to perform accurate tests.

SuperAnnotate offers a user-friendly platform to manage LLM dataset creation, with everything you need for full-scale Gen AI data projects. Whether you’re working with your own teams or data creation partners, SuperAnnotate can help you enhance quality and ramp up productivity.

Thanks to our robust tools for quality, project, and people management, we can help you expand or streamline your data creation processes. Our fully managed service is trusted by leading foundational model companies to set up best practices and quickly assemble expert teams, ensuring you create top-quality datasets efficiently.

What can LLM agents do?

LLM agents can solve advanced problems, learn from their mistakes, use specialized tools to improve their work, and even collaborate with other agents to improve their performance. Here’s a closer look at some of the standout capabilities that make LLM agents so valuable:

1. Advanced problem solving: LLM agents can handle and execute complex tasks efficiently. They can generate project plans, write code, run benchmarks, create summaries, etc. These tasks show their ability to plan and execute tasks that require a high level of cognitive engagement.
2. Self-reflection and improvement: LLM agents are able to analyze their own output, identify any issues, and make necessary improvements. This self-reflection ability allows them to engage in a cycle of criticism and rewriting, continuously enhancing their performance across a variety of tasks such as coding, writing text, and answering complex questions.
3. Tool use: LLM agents can evaluate their own output, ensuring the accuracy and correctness of their work. For instance, they might run unit tests on their code or use web searches to verify the accuracy of the information in their text. This critical evaluation helps them recognize errors and suggest necessary corrections.
4. Multi-agent framework: In a multi-agent LLM framework, one agent can generate outputs, and another can critique and provide feedback, resulting in advanced performance.

LLM agent frameworks

Let’s take a look at some notable LLM agents and frameworks:

• Langchain - A framework for developing LLM-powered applications that simplifies the LLM application lifecycle.
  - CSV Agent
  - JSON Agent
  - OpenAPI Agent
  - Pandas Dataframe Agent
  - Python Agent
  - SQL Database Agent
  - Vectorstore Agent
• Llama Index: A data framework that simplifies the creation of LLM applications with data connectors and structuring, advanced retrieval interfac and integration capabilities..
  - Llama Hub - Community-driven library for data loaders, readers, and tools.‍
• Haystack - An end-to-end NLP framework that enables you to build NLP applications.
  - Haystack Agent
  - SearchEngine
  - TopPSampler
• Embedchain - A framework to create ChatGPT-like bots for your dataset.
  - JS Repo
• MindSearch: A new AI search engine framework that works similarly to Perplexity.ai Pro. You can set it up as your own search engine using either proprietary LLMs like GPT and Claude or open-source models like InternLM2.5-7b-chat. It's built to browse hundreds of web pages to answer any question, providing detailed responses and showing how it found those answers.
• AgentQ: Helps create autonomous web agents that can plan, adapt, and self-correct. It integrates guided Monte Carlo tree search (MCTS), AI self-critique, and RLHF using the direct preference optimization (DPO) algorithm.
• Nvidia NIM agent blueprints: An agent for enterprise developers who need to build and deploy customized GenAI applications.
• Bee agent framework: An open-source framework by IBM for building, deploying, and serving large agentic workflows at scale. IBM’s goal with Bee is to empower developers to adopt the latest open-source and proprietary models with minimal changes to their current agent implementation.

LLM agent challenges

While LLM agents are incredibly useful, they do face several challenges that we need to consider:

1. Limited context: LLM agents can only keep track of a limited amount of information at a time. This means they might not remember important details from earlier in a conversation or miss crucial instructions. Although techniques like vector stores help by providing access to more information, they can't completely solve this issue.
2. Difficulty with long-term planning: It's tough for LLM agents to make plans that span over long periods. They often struggle to adapt when unexpected problems pop up, which can make them less flexible compared to how humans approach problem-solving.
3. Inconsistent outputs: Since LLM agents rely on natural language to interact with other tools and databases, 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.
4. Adapting to specific roles: LLM agents need to be able to handle different roles depending on the task at hand. However, fine-tuning them to understand and perform uncommon roles or align with diverse human values is a complex challenge.
5. Prompt dependence: LLM agents operate based on prompts, but these prompts need to be very precise. Even small changes can lead to big mistakes, so creating and refining these prompts can be a delicate process.
6. Managing knowledge: Keeping an LLM agent's knowledge accurate and unbiased is tricky. They must have the right information to make informed decisions, but too much irrelevant information can lead them to draw incorrect conclusions or act on outdated facts.
7. Cost and efficiency: Running LLM agents can be resource-intensive. They often need to process a lot of data quickly, which can be costly and may slow down their performance if not managed well.

Addressing these challenges is crucial for improving the effectiveness and reliability of LLM agents in various applications.

Final thoughts

In conclusion, LLM agents are powerful tools for tackling complex LLM tasks. They can plan, find information, remember past interactions, and learn from them, making them indispensable when answers aren't just black and white. However, they have limitations, such as a short memory span and a need for precise directions. By working to overcome these challenges, we can enhance their abilities and make them even more effective and adept at complex LLM problems.