What are AI Agents?

What Are AI Agents and Their Role in Generative AI?

The Shift from Monolithic Models to Compound AI Systems

• 2024 is anticipated to be the year of AI agents, marking a significant shift in generative AI towards compound systems rather than relying solely on monolithic models.

• Monolithic models are limited by their training data, affecting their knowledge and adaptability. Tuning these models requires substantial investment in data and resources.

Practical Example: Vacation Planning

• A practical example illustrates the limitations of standalone models when planning a vacation; they lack access to personal data such as available vacation days.

• Integrating a model into existing processes enhances its utility. For instance, connecting it to a database allows for accurate responses based on real-time information.

Understanding Compound AI Systems

• Compound AI systems leverage system design principles, combining multiple components like tuned models and programmatic elements for better problem-solving capabilities.

• These systems are modular, allowing for various components (e.g., output verifiers, search tools) that can be tailored to specific tasks instead of just tuning one model.

Control Logic in Compound Systems

• Control logic dictates how queries are processed within compound systems. It ensures that the right path is followed based on the nature of the query.

• An example highlights that if a system is programmed to search only vacation policies, it will fail at unrelated queries like weather inquiries.

The Emergence of Agents in AI Systems

• Agents enhance control logic by utilizing large language models (LLMs), which have improved reasoning capabilities. This allows them to tackle complex problems more effectively.

• There exists a spectrum between fast execution with strict adherence to instructions and slower, more thoughtful planning where LLM agents break down problems into manageable parts.

Key Capabilities of LLM Agents

• LLM agents possess reasoning abilities at their core, enabling them to devise plans and assess each step during problem-solving processes.

Capabilities of AI Agents

Accessing External Programs and Memory

• AI models can access external programs, enhancing their capabilities beyond standard functions.

• The concept of "memory" includes both internal logs (like thinking out loud) and historical interactions with users, which can be retrieved for personalized experiences.

Configuring Agents with ReACT

• ReACT is a popular method for configuring agents, combining reasoning and action components in LLM agents.

• When configuring a REACT agent, the user query is processed through a model that encourages slow thinking and planning before acting.

Problem-Solving Process Example

• An example scenario involves planning a vacation to Florida while considering sunscreen needs; this illustrates the complexity of problem-solving.

• Key factors include retrieving past vacation days from memory, assessing expected sun exposure based on weather forecasts, and understanding recommended sunscreen dosages.

Modular Approach to Complex Problems

• The modular nature of AI systems allows exploration of various paths to solve complex problems effectively.

• Compound AI systems are becoming more prevalent, emphasizing the need for tailored configurations based on problem complexity.

Trade-offs in AI Autonomy

• A sliding scale of AI autonomy exists; system designers must consider trade-offs between efficiency and flexibility when defining narrow or broad problem sets.