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Whitepaper Companion Podcast - Agents | 5-Day Gen AI Intensive Course with Google
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Whitepaper Companion Podcast - Agents | 5-Day Gen AI Intensive Course with Google

Generative AI Agents: Understanding Their Architecture

Introduction to Generative AI Agents

  • The discussion begins with an overview of generative AI agents, highlighting the rapid advancements in AI technology and the focus on understanding these agents.
  • The aim is to distill complex concepts from a paper titled "Agents" (February 2025), focusing on defining what an agent is, its components, and potential applications.

Defining Generative AI Agents

  • A generative AI agent is described as a program that extends beyond traditional generative models by incorporating reasoning, logic, and external information access.
  • Unlike standard models that only generate text or images, these agents pursue specific goals through observation and action based on their environment.

Cognitive Architecture of Agents

  • The cognitive architecture consists of three main parts: the model (intelligence), tools (interaction capabilities), and orchestration layer (control center).

1. The Model

  • The model refers to the language model at the core of the agent's intelligence; it can be a single model or multiple models working together.
  • These models utilize instruction-based reasoning frameworks like REACT or Chain of Thought to strategize problem-solving.

2. Tools

  • Tools empower agents to interact with both physical and digital environments, enabling actions such as data retrieval or modification.
  • They allow agents to connect internal reasoning with external systems, crucial for tasks like retrieval augmented generation.

3. Orchestration Layer

Understanding Agents and Their Architecture

Components of an Agent

  • The orchestration of tools within an agent can range from simple to complex, involving various calculations and machine learning algorithms.
  • An agent consists of a model, tools, and an orchestration layer, which differentiates it from standalone models.

Key Differences Between Agents and Models

  • Knowledge: Standalone models are limited to their training data, while agents can access real-time information through tools.
  • Information Handling: Unlike models that make single predictions without memory, agents retain the history of interactions for multi-turn conversations.
  • Tool Integration: Tools are integral to an agent's architecture; models lack built-in support for such tools.
  • Cognitive Architecture: Agents possess a dedicated logic layer that allows them to use reasoning frameworks effectively.

Cognitive Architectures in Action

  • The paper uses the analogy of a chef in a busy kitchen to illustrate how agents operate—planning, acting, observing, and refining actions based on feedback.
  • The orchestration layer is crucial as it tracks task states and manages reasoning processes.

Role of Prompt Engineering

  • Effective prompt engineering is essential for guiding the thought process of language models within agents.

Frameworks for Reasoning and Planning

React Framework

  • React (Reason and Act) encourages step-by-step reasoning followed by interaction with the environment based on user queries.
  • This transparency in thought processes leads to more reliable answers.

Chain of Thought (CoT)

  • CoT involves structured problem-solving where the model explains its reasoning step-by-step rather than providing direct answers. Variations include self-consistency and multimodal CoT.

Tree of Thoughts (ToT)

  • ToT expands upon CoT by exploring multiple possibilities strategically—similar to anticipating moves in chess rather than considering one move at a time.

Practical Example Using React Framework

Flight Booking Process with AI Agents

Interaction with Flight APIs

  • The agent utilizes a flight API to gather necessary information, initiating a back-and-forth conversation with the user about their travel preferences.
  • This iterative process involves the agent asking clarifying questions and refining its understanding until it has sufficient details to book the flight.

Importance of Tools in AI Models

  • The effectiveness of an agent's final answer, such as booking a flight, relies on its reasoning capabilities and tool utilization.
  • Language models excel at processing text but require tools to interact with the real world, bridging gaps between digital and physical actions.

Categories of Tools for Google Models

  • The paper categorizes tools into three main types: extensions, functions, and data stores.

Extensions

  • Extensions provide a standardized method for agents to connect to APIs without needing extensive custom code.
  • They simplify API usage by allowing agents to learn from provided examples rather than requiring detailed programming knowledge.

Functions

  • Functions are self-contained code segments that perform specific tasks; they differ from extensions as they do not directly call APIs but instead determine which function to use based on specifications.
  • Unlike extensions that run on the agent side, functions execute client-side, giving developers more control over API interactions.

Practical Applications of Functions

  • In scenarios like travel planning, functions can suggest cities without direct interaction with external APIs; this allows for flexibility in how data is processed before being sent back to the agent.

Understanding Tools for Language Models

Function Definitions and Usage

  • Snippets six and seven illustrate how to define a function in Python and utilize it with the Gemini 2.0 FL 1 model, showcasing the power of building tools based on functions.
  • The discussion transitions to data stores, comparing a language model's training data to a static library that doesn't update, highlighting the limitations of outdated knowledge.

Dynamic Data Access

  • Data stores provide agents with access to dynamic, up-to-date information from external sources like databases or APIs, enhancing their relevance in real-time tasks.
  • Agents can query data stores without needing retraining; these systems convert documents into formats understandable by the agent.

Vector Databases and Retrieval Augmented Generation (RAG)

  • Vector databases are emphasized for their efficiency in storing and searching information, allowing agents to find relevant content easily.
  • RAG is introduced as a technique enabling language models to access extensive external knowledge bases instead of relying solely on internal memory.

Workflow of RAG Systems

  • Figure 12 illustrates an agent connected to multiple data stores simultaneously, while Figure 13 outlines the typical workflow: user queries are converted into vector embeddings for document retrieval.
  • The agent combines its internal knowledge with retrieved documents to generate comprehensive responses.

Tool Types Overview

  • A recap table summarizes key features of each tool type: extensions and data stores operate within the agent's infrastructure, while function calling occurs client-side.
  • Extensions are ideal for direct API interactions; function calling is preferred when security or authentication concerns arise.

Enhancing Model Performance

  • The paper discusses challenges in ensuring models select appropriate tools as complexity increases; targeted learning strategies are proposed using cooking analogies.
  • Three main approaches for enhancing model effectiveness are outlined: in-context learning, retrieval-based in-context learning, and fine-tuning based learning.

Learning Approaches Explained

  • In-context learning involves providing specific prompts (recipes), necessary tools (ingredients), and examples (finished dishes).
  • Retrieval-based in-context learning allows models access to broader resources (pantry/cookbooks), augmenting their knowledge base similar to RAG functionality.

Understanding Generative AI Agents

Overview of Task-Specific Models

  • Teaching models to specialize in certain tasks or tools can enhance their effectiveness, but each approach has trade-offs. Context learning is quick but may struggle with complex tasks, while fine-tuning is powerful yet resource-intensive.

Building Agents with LangChain

  • The paper includes a section on quickly starting agent development using LangChain, an open-source library for building functional agents.

Example Use Case: Texas Longhorns Game Inquiry

  • An example in the paper demonstrates how to use the Gemini 2.0 model with tools like Google search and Google Places API to answer multi-step questions about sports events.

Agent Functionality and Code Snippets

  • Code snippet 8 illustrates setting up API keys, defining tools, initializing the model, and creating a reactive agent capable of answering complex queries.

Production-Level Applications with Vertex AI

  • For real-world applications, Vertex AI offers a managed platform that provides user interfaces and evaluation systems necessary for sophisticated agent development.

Sample Architecture Overview

  • Figure 15 in the paper presents an architecture overview built on Vertex AI, showcasing how various components integrate seamlessly for effective agent functionality.

Key Takeaways on Generative AI Agents

Advancements Beyond Language Models

  • Generative AI agents represent significant progress from standalone language models by enabling real-time information access and interaction capabilities for executing complex tasks.

Importance of Orchestration Layer

  • The orchestration layer is crucial as it employs cognitive architectures and reasoning techniques (like React and Chain of Thought) to structure decision-making processes within agents.

Essential Tools for Agent Development

  • Tools such as extensions (for API calls), functions (for controlled execution), and data stores (for external data access) are vital for enhancing agent capabilities.

Future Prospects of Generative AI Agents

Iterative Development Process

  • Building these agents involves an iterative process where experimentation leads to refinement; there’s no one-size-fits-all solution but foundational concepts provide a solid starting point.

Encouragement for Innovation

Playlists: 5-Day Gen AI Course 2025 - Companion Podcast Episodes
Video description

Read the whitepaper here: https://www.kaggle.com/whitepaper-agents Learn more about the 5-Day Generative AI Intensive: https://rsvp.withgoogle.com/events/google-generative-ai-intensive_2025q1 Introduction: Humans are fantastic at messy pattern recognition tasks. However, they often rely on tools - like books, Google Search, or a calculator - to supplement their prior knowledge before arriving at a conclusion. Just like humans, Generative AI models can be trained to use tools to access real-time information or suggest a real-world action. For example, a model can leverage a database retrieval tool to access specific information, like a customer's purchase history, so it can generate tailored shopping recommendations. Alternatively, based on a user's query, a model can make various API calls to send an email response to a colleague or complete a financial transaction on your behalf. To do so, the model must not only have access to a set of external tools, it needs the ability to plan and execute any task in a self-directed fashion. This combination of reasoning, logic, and access to external information that are all connected to a Generative AI model invokes the concept of an agent, or a program that extends beyond the standalone capabilities of a Generative AI model. This whitepaper dives into all these and associated aspects in more detail.