As large language models (LLMs) become more powerful, a new breed of software known as “agents” has arisen to augment and enhance the capabilities of LLMs. This article introduces the key concepts of agents and how they complement LLMs.
Since the initial release of ChatGPT, which was based on GPT 3.5, large language models have evolved and matured. Some of the recent releases — like GPT-4o, Gemini Pro, and the Claude Opus models — have even demonstrated advanced reasoning abilities. The open language model landscape has also been rapidly evolving in recent times. Several variants of these LLMs have been released for use in private environments. In terms of reasoning and answering complex questions, some open language models — like Mistral and Llama 3 — are on par with commercial models. This has all been a driver of the AI agents trend.
What Is an AI Agent?
An agent is an autonomous software entity that leverages the language processing capabilities of LLMs to perform a wide range of tasks beyond simple text generation and comprehension. These agents extend the functionality of LLMs by incorporating mechanisms to interact with digital environments, make decisions and execute actions based on the language understanding derived from the LLM.
In the context of operating systems, consider LLMs to be the kernel and agents to be the programs.
Agents rely heavily on the LLM to perform reasoning while augmenting an LLM’s functionality by adding new capabilities.
LLMs have several limitations that agents attempt to overcome. Let’s take a look at some of these limitations.
Limitations of LLMs
LLMs Don’t Have Memory
Similar to a REST API call, invoking an LLM is entirely stateless. Each interaction with an LLM is independent, meaning the model does not inherently remember prior exchanges or build upon previous conversations. This limitation affects the continuity and coherence of long-term interactions, as the model cannot leverage historical context to inform future responses. The stateless nature of LLMs necessitates that each input must be fully self-contained, leading to repetitive or disjointed interactions in extended use cases.
LLM Invocations Are Synchronous
LLMs operate in a synchronous manner, meaning that they process and respond to each input sequentially, one at a time. This synchronous operation implies that the model must complete its response to a given input before it can process the next one. This sequential processing can be a limitation in scenarios requiring real-time interaction or simultaneous handling of multiple queries, as it cannot inherently parallelize the processing of different inputs.
LLMs Might Hallucinate
LLMs might produce hallucinations, which are instances where the model generates information that is factually incorrect or nonsensical. This phenomenon occurs because LLMs are trained on vast datasets comprising text from the internet, where they learn patterns and correlations rather than factual accuracy. As a result, they can fabricate details or present false information confidently, creating the illusion of knowledge.
LLMs Cannot Access the Internet
LLMs cannot browse the web or invoke a web service, so they are limited to the data they were trained on and do not have the capability to retrieve or verify information from live web sources in real time. This constraint means that their responses are based solely on the pre-existing knowledge embedded within them, which might not be up-to-date or contextually relevant for real-time inquiries. Consequently, LLMs are unable to provide current news updates, access the latest research or pull data from dynamic online databases — making their use less effective for tasks requiring the most recent information.
LLMs Are Bad at Math
LLMs are often poor at handling mathematical tasks, particularly those that require precise calculations or complex problem-solving. This limitation arises because LLMs are primarily designed to understand and generate natural language based on patterns learned from vast textual datasets. While they can perform simple arithmetic and follow basic mathematical rules, their ability to solve more complex mathematical problems or ensure accuracy in multi-step calculations is limited. They often lack the structured logical reasoning needed to perform advanced mathematical operations reliably.
LLMs Have Non-Deterministic Output
LLMs exhibit non-deterministic output in terms of data format and structure, meaning that identical inputs can produce varying outputs each time they are processed. This variability stems from the probabilistic nature of the algorithms that underpin LLMs, which select from a range of possible responses based on learned patterns rather than deterministic rules. As a result, the format and structure of the output can differ, making it challenging to achieve consistent results, particularly for applications requiring uniformity in response formatting — such as automated report generation, form filling or data extraction.
How Do Agents Augment LLMs?
Agents bridge the gap that exists between traditional software development tools and LLMs, which helps solve or alleviate some of the above limitations.
For example, by integrating tools such as web browsing and code execution environments, agents can combine real-world data with complex calculations before having an LLM analyze and generate a detailed response.
In the context of operating systems, consider LLMs to be the kernel and agents to be the programs. The shell consists of the tools and support services needed by agents to execute. Agents enhance an LLM’s functionality by connecting it with the tools and external services needed to complete a task.
Let’s understand the role of agents in augmenting the capabilities of LLM.
Memory and Context Retention
Unlike LLMs, which are stateless and do not retain a memory of previous interactions, agents can incorporate memory mechanisms to remember past interactions and build upon them. This allows agents to maintain continuity and coherence in long-term engagements, leveraging historical context to inform future responses. This capability enhances the user experience by creating more personalized and contextually relevant interactions.
Asynchronous and Parallel Processing
While LLMs process inputs synchronously and sequentially, agents can manage multiple tasks simultaneously and operate asynchronously. This ability to parallelize processes enables agents to handle real-time interactions more effectively, improving efficiency and responsiveness in scenarios that require simultaneous handling of multiple queries or tasks.
Fact-Checking and Real-Time Information Access
Agents can mitigate the issue of hallucinations in LLMs by incorporating real-time data verification and access to external information sources. By connecting to the internet or specific databases, agents can validate the information generated by LLMs, ensuring accuracy and reducing the incidence of false or misleading outputs. This makes agents particularly valuable in applications where up-to-date and precise information is crucial.
Enhanced Mathematical Capabilities
Agents can integrate specialized mathematical engines or software to handle complex calculations and problem-solving tasks, compensating for the mathematical weaknesses of LLMs. This integration allows agents to perform precise and reliable mathematical operations, expanding their utility in technical and scientific domains.
Consistent Output Formatting
To address the non-deterministic nature of LLM outputs, agents can implement post-processing steps to standardize the format and structure of responses. For example, they can enforce that the output from an LLM is always formatted in JSON or XML. By ensuring consistency in data presentation, agents can enhance the reliability of outputs in applications requiring uniformity, such as report generation and data extraction.
Persona-Driven Interactions
Agents enhance persona-driven interactions with LLMs by leveraging memory and personalization capabilities to create more tailored and engaging user experiences. By maintaining context over multiple interactions, agents can adapt responses to align with the user’s preferences, history and conversational style — effectively simulating a consistent persona. This personalized approach not only improves user satisfaction but also allows the agent to provide more relevant and context-aware assistance. Agents can dynamically adjust their behavior based on user feedback and past interactions, making the conversation feel more natural and human-like.
Summary
LLMs have evolved significantly, exemplified by models like GPT-4o and Gemini 1.5. However, they remain stateless, process inputs sequentially, can hallucinate, lack real-time data access, struggle with complex math and produce non-deterministic outputs.
AI agents augment LLMs by incorporating memory mechanisms for context retention, managing tasks asynchronously and validating information in real-time, thereby enhancing accuracy and coherence. They also integrate specialized mathematical engines and standardize output formats, making them more reliable and efficient for diverse applications.
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An AI agent is an autonomous software entity that is often used to augment a large language model. Here's what developers need to know.