LLM Powered Autonomous Agents
An Introduction to AI Agents, their Components, Capabilities and Future.
JUNE 2024
BY MCKENZIE LLOYD-SMITH
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Summary: Large Language Models are powerful in their own right, but agents take them a step further by providing a way to integrate their reasoning capabilities with specific knowledge, long-term memory, planning and tool use, allowing agents to take autonomous actions to tackle use cases that wouldn't have been possible earlier.
Introduction to LLM Agents
If you've ever engaged with a Large Language Model (LLMs) such as OpenAI's GPT-4, Anthropic's Claude, or Google's Gemini, you're likely aware of their strengths and also their limitations. For instance, it was not until 2023 that ChatGPT gained the capability to conduct internet searches, and only recently has OpenAI included a basic "memory" function. These enhancements underscore a fundamental issue: out of the box LLMs are extremely limited in their functionality. This limitation directly connects to the emergence of what we now recognize as autonomous "agents."
This new phase in LLM development marks a transition from standalone models to interactive AI agents that are capable of navigating a series of actions with a degree of autonomy. This degree of autonomy is currently limited, as we'll explore further. But AI agents represent an important shift in how we interact with technology – by interpreting natural language, agents are able to actively engage with and manipulate their environments to achieve specified goals. They are designed to integrate the general knowledge and reasoning capacities of LLMs with enhanced, goal-oriented functionalities, taking actions on our behalf, and pushing the boundaries of what AI systems can accomplish.
But what is an AI Agent?
These agents go by various names – AI agent, interactive agent, autonomous agent, LLM agent – but the easiest way of thinking about an agent is to think of it as a large language model with access to a bunch of digital tools. As the user, you provide some instructions, the AI agent interprets your instructions, creates a plan, and carries out the plan by using its tools – taking actions on your behalf to complete a goal, based on your instructions.
Let's dig deeper.
AI agents are structured around several core components that enable its functionality. At it's core is the LLM itself. This functions as an AI agent’s "brain," supported by several key components:
Simplified Components of an AI Agent
Planning
To manage complex tasks efficiently, an AI agent in able to decompose a task, splitting it into smaller, more manageable parts, often known as subgoals. This breakdown simplifies the approach, making it easier for an AI agent to tackle large and intricate projects by addressing each segment one at a time.
Sophisticated AI agents are able to reflect and refine, via a process of self-evaluation regarding its previous actions. By analyzing past mistakes and learning from them, an AI agent can refine its strategies for future actions, thereby enhancing the quality of its outcomes.
Memory
It's important to note that even the simplest AI agents can operate without a memory component, but nearly all practical applications involve some level of memory. In AI agents, memory is typically split into short- and long-term:
Short-term Memory: This aspect can be likened to an AI agent’s ability to learn and adapt within a given context, much like a human using memory to recall recent events. It allows an AI agent to temporarily hold and manipulate information relevant to the task at hand.
Long-term Memory: This feature equips an AI agent with the ability to store and retrieve an extensive amount of information over prolonged periods. This is often achieved through the use of an external storage system, which acts like a library of information that an agent can access whenever necessary.
Tool Use
An AI agent has the ability to utilize external tools via APIs (Application Programming Interfaces), which are gateways to accessing additional information not included in the initial training of the model. These tools can provide up-to-date information, execute code, or access specialized databases. This capability is crucial, and is what differentiates an AI agent from a LLM.
Action
Action in AI agent systems is closely related to tool use. When an AI agent utilizes a tool, it essentially makes a decision to perform an action. This action can either be internal, limited to an agent's own "thought" process, or external, resulting in a tangible output from an agent. For example, a conversational agent primarily operates with internal actions, focusing on generating and refining its responses, while a task-oriented agent not only processes information internally but also performs actions in the external world, such as sending emails or managing data.
Note that in some instances, an AI agent might choose not to take any action (opting for inaction). This decision typically occurs when an AI agent determines that performing an action would not add significant value to its reasoning process.
Capabilities of AI Agents
AI agents excel precisely where traditional LLMs show limitations. These agents are instrumental in executing tasks that require not just generating text but also planning, decision-making, and action-taking within interactive contexts.
The advantage of AI agents, beyond automating tasks, is their ability to conduct tasks without explicit step-by-step instructions. Unlike traditional automation, which requires programmatic input — defining exactly how a task will be conducted, and what to do (if anything) if the task fails — an AI agent is able to understand natural language, devise a plan, and execute it. This means an AI agent allow you to conduct and automate tasks–
faster than done manually
without the ability to write code
without the need to design specific workflows
even if you don't know how to do a task
We're seeing the deployment of AI agents span industries and functions. In customer service, AI agents are able to handle inquiries and provide support with increasing autonomy, reducing the need for human intervention by not only responding to a customer's question, but also finding information, raising a ticket, or sending an email when required.
In marketing, AI agents are are able to adapt to real-time data, generating content based on current trends and conversations, and using social-monitoring and feedback loops to learn when best to post content and engage with others, to maximise key metrics. In sales, AI agents are able to examine existing accounts, identify opportunities, research leads, and develop bespoke outreach strategies.
Outside of organizations, personal AI agents are increasingly being adopted by individuals to minimize repetitive "grunt work" and maximize productivity.
The capacity of AI agents to integrate and interact with external tools also opens up new avenues for automation and efficiency in various processes, enabling them to perform both as independent solutions and in conjunction with human operators.
Table: Five levels of AI Agents. Note: not all AI agents found within this table are powered by LLMs. For example, AlphaGo and AlphaFold use neural networks and reinforcement learning.
The Future of AI Agents
It's fair to say we're at an interesting moment.
Looking forward, the trajectory for AI agents is set towards greater autonomy and more profound integrative capabilities within enterprise frameworks. The ongoing advancements in AI are expected to enhance their decision-making and planning capabilities, tools use and, most importantly, the ability to engage in autonomous learning and collaborative behaviors, making them even more reliable and versatile.
Based on generality and performance of AI agent capabilities, a matrix can be used to classify different levels of AI agents, and the steps between them. Within the table, five levels of AI agent are defined, starting at Emerging AI and concluding with Superhuman AI.
Performance estimates how the AI agent compares to human-level performance for a given task.
Tools / Techniques reflects the various external tools that support richer action capabilities for AI agents, including APIs, knowledge bases, visual encoding models and language models, enabling the agent to adapt to environmental changes, provide interaction and feedback, and even influence the environment.
Generality (narrow / broad) measures the range of tasks for which an AI gets to a goal performance threshold. Examples are given for each, where they have been achieved.
While we currently sit around level 3, Expert AI for narrow tasks (with the odd exception of extremely powerful models like AlphaFold and AlphaZero), we're only just starting to see Level 1: Emerging AI for general tasks. There's a long way to go.
Although the rate of progression between levels of performance and/or generality are nonlinear, as we continue to develop these AI agents, they are expected to play pivotal roles in driving the transition from traditional computational models to more sophisticated, self-regulating systems—bringing in a new era of agentic software, where AI works seamlessly with human inputs to create smarter, more responsive technological environments.
The evolution from basic LLMs to sophisticated autonomous agents marks a significant milestone in AI development. These AI agents are not only redefining the possibilities of generative AI, but are also setting the stage for future innovations that will further integrate this tech into everyday business operations and personal activities. As we advance, the blend of human creativity with the computational power of AI agents promises to unlock new potentials across all sectors of society.
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The evolution from LLMs to autonomous agents is another major shift in how we conceive of and interact with AI systems. At MindPort, we believe that the future of AI lies in its ability to seamlessly integrate into the human experience, enhancing our capabilities and enriching our interactions. Our ongoing research and development efforts aim to ensure that as autonomous agents become a reality, they do so in a way that prioritizes human values and needs, setting a standard for responsible and innovative AI development.
If you're exploring AI agents, want to build your own agent, need help implementing an agent into your existing workflows, or just want to learn more, get in touch.
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