What are AI Agents?

The discussion highlights the evolution of AI agents in 2024, emphasizing a shift from monolithic models to compound AI systems. These systems integrate models with programmatic components, enhancing adaptability and efficiency. The conversation explores how AI agents utilize reasoning, action, and memory to solve complex tasks, employing a modular approach that allows for dynamic problem-solving. The concept of AI autonomy is introduced, with a focus on balancing programmatic and agentic approaches depending on task complexity. The potential of AI agents to handle intricate queries independently marks a significant advancement in AI technology.

• 2024 is predicted to be a significant year for AI agents, marking a shift in the field of generative AI.
• The transition from monolithic models to compound AI systems is a key development.
• Monolithic models are limited by their training data, affecting their knowledge and task-solving capabilities.
• Adapting monolithic models requires substantial investment in data and resources.

"2024 will be the year of AI agents. So what are AI agents? And to start explaining that, we have to look at the various shifts that we're seeing in the field of generative AI."

• Introduction to the importance of AI agents in 2024 and the shifts in generative AI.

"Models on their own are limited by the data they've been trained on. So that impacts what they know about the world and what sort of tasks they can solve."

• Highlights the limitations of monolithic models due to their dependency on training data.

• Compound AI systems integrate models into existing processes, enhancing their utility.
• These systems involve multiple components, making them modular.
• An example is planning a vacation, where a compound system accesses a database to provide accurate information.
• Compound systems can include tuned models, large language models, image generation models, and programmatic components.

"The magic gets unlocked when I start building systems around the model and actually take the model and integrate them into the existing processes I have."

• Emphasizes the enhanced functionality of models when integrated into compound systems.

"This is an example of a compound AI system, and it recognizes that certain problems are better solved when you apply the principles of system design."

• Describes the advantage of solving complex problems using compound AI systems with system design principles.

• System design in AI involves multiple, modular components.
• These components can include output verifiers and programs that refine queries for accuracy.
• Integration with databases and other resources is crucial for compound AI systems.

"By the term 'system', you can understand there's multiple components. So systems are inherently modular."

• Explains the modular nature of systems in compound AI, allowing flexibility and customization.

"I can have programs that can take a query and then break it down to increase the chances of the answer being correct."

• Discusses the role of programmatic components in enhancing the accuracy of responses in compound systems.

• System approaches involve breaking down a desired program's functions and selecting the appropriate components to achieve those functions efficiently.
• This method is generally faster and more adaptable compared to model tuning.
• Compound AI systems, such as Retrieval Augmented Generation (RAG), are prevalent and utilize a structured path or control logic for processing queries.

"When we talking about a system approaches, I can break down what I desire my program to do and pick the right components to be able to solve that."

• The speaker highlights the importance of defining program goals and selecting suitable components for efficient solutions.

"So the example I use below, is an example of a compound AI system. You also might be popular with retrieval augmented generation (RAG), which is one of the most popular and commonly used compound AI systems out there."

• Retrieval Augmented Generation (RAG) is identified as a widely used compound AI system.

• Control logic dictates the path a program follows to answer queries, which the human programmer defines.
• A limitation of rigid control logic is its inability to handle queries outside its predefined path, such as irrelevant questions about the weather when the system is designed to search a vacation policy database.

"If I bring a very different query, let's ask about the weather in this example here. It's going to fail because this the path that this program has to follow is to always search my vacation policy database."

• The speaker illustrates the limitation of strict control logic using a weather query example.

"So when we say the path to answer a query, we are talking about something called the control logic of a program."

• Control logic is the predetermined path a program follows to process and respond to queries.

• Agentic approaches involve using LLMs to control the logic of a compound AI system, leveraging their improved reasoning capabilities.
• LLMs can be prompted to deconstruct complex problems, create plans, and adjust those plans as needed, contrasting with rigid, fast-thinking systems.

"One other way of controlling the logic of a compound AI system is to put a large language model in charge, and this is only possible because we're seeing tremendous improvements in the capabilities of reasoning of large language models."

• Placing LLMs in charge of logic is feasible due to their enhanced reasoning abilities.

"Another way to think about it is, on one end of the spectrum, I'm telling my system to think fast, act as programmed, and don't deviate from the instructions I've given you. And on the other end of the spectrum, you're designing your system to think slow."

• The spectrum of AI system design ranges from fast, programmed responses to slow, plan-oriented reasoning.

• LLM agents possess the capability to reason, forming the core of problem-solving processes.
• They are prompted to generate plans and reason through each step, adapting as necessary.
• The ability to act is another key capability of LLM agents, allowing them to execute plans effectively.

"So let's break down the components of LLM agents. The first capability is the ability to reason, which we talked about."

• Reasoning is a fundamental capability of LLM agents, essential for solving complex problems.

"The model will be prompted to come up with a plan and to reason about each step of the process along the way."

• LLM agents are tasked with creating and reasoning through plans to address problems.

• Tools are external programs integrated into AI systems to enhance their functionality.
• These tools can perform various tasks such as web searches, calculations, database manipulations, and translations.
• Tools are often implemented as APIs, allowing models to access external functionalities.

"And this is done by external programs that are known in the industry as tools. So tools are external pieces of the program, and the model can define when to call them and how to call them in order to best execute the solution to the question they've been asked."

• Tools serve as external resources that AI models can utilize to improve the accuracy and efficiency of their responses.

"So an example of a tool can be search, searching the web, searching a database at their disposal. Another example can be a calculator to do some math."

• Examples of tools include web search engines, calculators, and database access, which provide additional capabilities to AI models.

• Memory in AI systems refers to the capability to store and retrieve information, enhancing personalization and problem-solving.
• Memory can involve storing internal dialogues or remembering past interactions with users.
• This functionality allows for more personalized user experiences and improved contextual understanding.

"Third capability, that is the ability to access memory. And the term 'memory' can mean a couple of things."

• Memory in AI can refer to both internal thought processes and the history of user interactions, aiding in personalized experiences.

"So those inner logs can be stored and can be useful to retrieve at different points in time. But also this could be the history of conversations that you as a human had when interacting with the agent."

• Memory involves the storage of thought processes and user interaction histories, which can be retrieved for future use to enhance personalization.

• ReACT is a method for configuring AI agents, combining reasoning and action components.
• The process involves feeding user queries into a model, prompting it to think and plan before acting.
• Agents can use external tools to assist in formulating solutions, iterating on plans if necessary.

"One of the more most popular ways of going about it is through something called ReACT, which, as you can tell by the name, combines the reasoning and act components of LLM agents."

• ReACT is a popular method that integrates reasoning and action in AI agents, enhancing their decision-making process.

"So the instructions that's given is don't give me the first answer that pops to you. Think slow planning your work. And then try to execute something. Tried to act."

• ReACT encourages AI agents to deliberate and plan before acting, improving the quality of their responses.

"You can define whether. If you want to use external tools to help you come up with the solution. Once you get you call a tool and you get an answer. Maybe it gave you the wrong answer or it came up with an error."

• ReACT allows AI agents to utilize external tools for solutions, with the flexibility to iterate and refine their approach if initial attempts are unsuccessful.

• Determining the number of sunscreen bottles involves multiple considerations such as vacation days, sun exposure hours, and recommended sunscreen dosage.
• The process requires retrieving personal vacation plans, checking weather forecasts for sun exposure, and consulting health guidelines for sunscreen use.
• The complexity of the task highlights the modularity of AI systems in solving multifaceted problems.

"So I want to know what is the number of two-ounce sunscreen bottles that I should bring with me? And this is a complex problem."

• This quote introduces the complexity of determining the number of sunscreen bottles needed, setting the stage for a detailed planning process.

"There's a number of things to plan. One is how many vacation days am I planning to take?"

• The speaker emphasizes the importance of knowing the duration of the vacation as a key factor in calculating sunscreen needs.

"Two is how many hours do I plan to be in the sun?"

• The speaker identifies sun exposure hours as critical in determining sunscreen requirements, suggesting consultation of weather forecasts.

"Three is trying maybe going to a public health website to understand what is the recommended dosage of sunscreen per hour in the sun."

• The speaker highlights the need to consult health guidelines to understand appropriate sunscreen dosage, adding another layer of complexity to the planning.

• Compound AI systems are designed to handle complex tasks by exploring multiple solution paths, making them modular and adaptable.
• These systems are evolving towards agent technology, allowing for a sliding scale of AI autonomy based on task complexity.
• Narrow, well-defined problems benefit from a programmatic approach, while complex tasks require agent-based systems for efficiency.

"Compound AI systems are here to stay. What we're going to observe this year is that they're going to become more agent tech."

• The speaker predicts the evolution of AI systems towards agent technology, emphasizing their permanence and adaptability.

"You have a sliding scale of AI autonomy. And you would the person defining the system would examine what trade-offs they want in terms of autonomy in the system for certain problems."

• The speaker describes the flexibility in AI system design, allowing for varying levels of autonomy based on specific problem requirements.

"So a narrow problem set. You can define a narrow system like this one. It's more efficient to go the programmatic route because every single query will be answered the same way."

• The speaker explains the efficiency of using a programmatic approach for narrow problem sets, ensuring consistent query responses.

"But if I expect to have a system, accomplish very complex tasks like, say, trying to solve GitHub issues independently, and handle a variety of queries, a spectrum of queries. This is where an agent de Groot can be helpful."

• The speaker highlights the usefulness of agent-based systems in managing complex tasks and diverse queries, showcasing their adaptability and efficiency.

• Agent systems are in their early stages but are showing rapid progress, combining system design with genetic behavior.
• Human involvement remains crucial in these systems to ensure accuracy as they continue to improve.
• The combination of AI system design and genetic behavior is leading to advancements in agent systems.

"And we're still in the early days of agent systems. We're seeing rapid progress when you combine the effects of system design with a genetic behavior."

• The speaker acknowledges the nascent stage of agent systems while highlighting the rapid advancements through the integration of system design and genetic behavior.

"Of course, you will have a human in the loop in most cases as the accuracy is improving."

• The speaker notes the importance of human oversight in agent systems to maintain accuracy during their development and improvement.