Key Notes and Summary of the Google Whitepaper on Agents

Key Notes and Summary of the Google Whitepaper on Agents
Key Notes and Summary of the Google Whitepaper on Agents

In the rapidly evolving world of artificial intelligence, Generative AI Agents are emerging as a transformative technology. These agents extend the capabilities of traditional language models (LMs) by enabling them to interact with the external world, access real-time information, and perform complex tasks autonomously. In this blog post, we’ll dive deep into the concept of Generative AI Agents, their architecture, tools, and how they can revolutionize industries. We’ll also explore practical examples, tabular data, and visual representations to help you understand how these agents work.


What is a Generative AI Agent?

Generative AI Agent is an application that achieves specific goals by observing the world, reasoning, and acting upon it using a set of tools. Unlike traditional language models, which are limited to their training data, agents can interact with external systems, access real-time information, and perform tasks autonomously.

Key Characteristics of Agents:

  • Autonomy: Agents can act independently without human intervention.
  • Proactivity: They can reason and plan actions to achieve their goals.
  • Tool Integration: Agents use tools like APIs, databases, and external services to interact with the world.

Core Components of an Agent

Agents are built on a cognitive architecture that consists of three key components:

ComponentDescription
ModelThe language model (LM) used for decision-making. Can be general-purpose or fine-tuned.
ToolsEnable agents to interact with external systems (e.g., APIs, databases).
Orchestration LayerGoverns the agent’s reasoning, planning, and decision-making processes.

1. The Model

The model is the brain of the agent. It can be a single or multiple language models capable of following reasoning frameworks like ReActChain-of-Thought (CoT), or Tree-of-Thoughts (ToT). The model is responsible for generating responses, making decisions, and selecting the appropriate tools for a given task.


2. The Tools

Tools bridge the gap between the agent and the external world. They enable agents to perform tasks like fetching real-time data, updating databases, or sending emails. There are three primary types of tools:

Tool TypeDescription
ExtensionsBridge agents and APIs. Agents use examples to dynamically select extensions.
FunctionsAllow agents to generate function parameters, executed client-side.
Data StoresProvide access to structured/unstructured data (e.g., PDFs, spreadsheets).

3. The Orchestration Layer

The orchestration layer is the decision-making engine of the agent. It governs how the agent processes information, reasons, and takes actions. This layer uses frameworks like ReAct to guide the agent’s reasoning and planning.


Agents vs. Models: What’s the Difference?

AspectModelsAgents
KnowledgeLimited to training data.Extended through tools and external systems.
Session HistoryNo native session history management.Managed session history for multi-turn interactions.
ToolsNo native tool implementation.Tools are natively implemented.
Logic LayerNo native logic layer.Uses reasoning frameworks like ReAct, Chain-of-Thought, etc.

How Agents Operate: Cognitive Architectures

Agents operate using cognitive architectures that mimic human reasoning. For example, imagine a chef in a kitchen:

  1. Gather Information: The chef collects ingredients and customer orders.
  2. Reason: The chef decides what dishes to prepare based on available ingredients.
  3. Act: The chef cooks the dish and adjusts based on feedback.

Similarly, agents:

  1. Gather Information: Collect data from the environment or user queries.
  2. Reason: Use reasoning frameworks like ReAct or Chain-of-Thought to decide the next action.
  3. Act: Execute tasks using tools like APIs or databases.

Tools: Connecting Agents to the External World

Agents rely on tools to interact with the external world. Let’s explore the three primary tool types in detail:

1. Extensions

Extensions bridge agents and APIs. They allow agents to execute API calls dynamically based on user queries. For example, an agent can use the Google Flights API to fetch flight information.

Example: Google Flights Extension

python

from vertexai.preview.extensions import Extension

extension_flights = Extension.from_hub("flights")
response = extension_flights.execute(
    operation_id="get_flights",
    operation_params={"origin": "Austin", "destination": "Zurich"}
)

2. Functions

Functions allow agents to generate structured outputs (e.g., JSON) that can be executed client-side. For example, an agent can generate a list of cities for a travel recommendation.

Example: Function for Displaying Cities

python

def display_cities(cities: list[str], preferences: Optional[str] = None):
    return cities

# Output: {"cities": ["Aspen", "Vail", "Park City"], "preferences": "skiing"}

3. Data Stores

Data Stores provide agents with access to dynamic and up-to-date information. They are typically implemented as vector databases that store data in the form of embeddings.

Example: Retrieval Augmented Generation (RAG)

  1. A user query is converted into embeddings.
  2. The embeddings are matched against a vector database.
  3. The agent retrieves relevant information and generates a response.

Enhancing Model Performance with Targeted Learning

Agents can improve their performance through targeted learning techniques:

Learning ApproachDescription
In-Context LearningProvides the model with prompts and examples at inference time.
Retrieval-Based LearningDynamically retrieves relevant information from external memory.
Fine-TuningTrains the model on specific datasets before inference.

Practical Example: Building an Agent with LangChain

Let’s build a simple agent using LangChain and Google Places API to answer a multi-stage query.

python

from langgraph.prebuilt import create_react_agent
from langchain_core.tools import tool
from langchain_community.utilities import SerpAPIMrapper
from langchain_community.tools import GooglePlacesTool

@tool
def search(query: str):
    return SerpAPIMrapper().run(query)

@tool
def places(query: str):
    return GooglePlacesTool().run(query)

model = ChatVertexAI(model="gemini-1.5-flash-001")
tools = [search, places]

query = "Who did the Texas Longhorns play in football last week? What is the address of the other team's stadium?"
agent = create_react_agent(model, tools)
input = {"messages": [("human", query)]}

for s in agent.stream(input, stream_mode="values"):
    print(s["messages"][-1])

Production Applications with Vertex AI

Google’s Vertex AI platform simplifies the development of production-grade agents. It offers tools like Vertex Agent BuilderVertex Extensions, and Vertex Example Store to help developers build, test, and deploy agents at scale.


Conclusion

Generative AI Agents are revolutionizing the way we interact with AI systems. By leveraging tools like ExtensionsFunctions, and Data Stores, agents can perform complex tasks, access real-time information, and deliver actionable insights. Platforms like Vertex AI and libraries like LangChain make it easier than ever to build and deploy these agents.

As the field of Generative AI continues to evolve, agents will become even more powerful, enabling businesses to solve increasingly complex problems and drive real-world value.


References


By understanding and leveraging the power of Generative AI Agents, businesses can unlock new opportunities and stay ahead in the competitive landscape. Whether you’re building a travel concierge or a customer support agent, the possibilities are endless!

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