wow-agent-day01 What is an Agent?

A complete Agent mainly consists of three core components:
1. Model:

Role: Acts as the "brain" of the Agent, responsible for understanding user input, reasoning and planning, and selecting appropriate tools for execution.
Types: Common models include ReAct, Chain-of-Thought, Tree-of-Thought, etc., which provide different reasoning frameworks to assist the Agent in multi-turn interactions and decision-making.
Importance: The model is the core of the Agent, and its reasoning ability determines the efficiency and accuracy of the Agent's actions.
2. Tools:
Role: Serves as the "bridge" for the Agent to interact with the outside world, allowing the Agent to access external data and services to perform various tasks.
Types: Tools can be various APIs, such as database queries, search engines, code executors, email senders, etc.
Importance: Tools extend the capabilities of the Agent, enabling it to perform more complex tasks.
3. Orchestration Layer:
Role: Responsible for managing the internal state of the Agent, coordinating the use of models and tools, and guiding the Agent's actions based on objectives.
Types: The orchestration layer can use various reasoning frameworks, such as ReAct, Chain-of-Thought, etc., to assist the Agent in planning and decision-making.
Importance: The orchestration layer is the "command center" of the Agent, responsible for coordinating various components to ensure the Agent's actions align with its goals.

The operation process of an Agent can be summarized in the following steps:

Receive Input: The Agent receives user instructions or questions.
Understand Input: The model understands the user's intent and extracts key information.
Reasoning and Planning: The model reasons and plans based on user input and current state to determine the next action.
Select Tools: The model selects appropriate tools based on objectives.
Execute Actions: The Agent uses tools to execute actions, such as querying a database or sending emails.
Obtain Results: The Agent obtains the results of the tool's execution.
Output Results: The Agent outputs the results to the user or proceeds to the next action.

The application range of Agents is very broad, for example:

Intelligent Customer Service: Agents can automatically answer user questions, process orders, resolve customer issues, and improve customer satisfaction.
Personalized Recommendations: Agents can recommend products, content, services, etc., based on user interests and behaviors, enhancing user experience.
Virtual Assistants: Agents can help users manage schedules, book trips, send emails, etc., improving work efficiency.
Code Generation: Agents can automatically generate code based on user needs, enhancing development efficiency.
Intelligent Creation: Agents can create poetry, novels, scripts, etc., based on user needs, inspiring creative ideas.
Knowledge Graph Construction: Agents can extract knowledge from text to build knowledge graphs for knowledge management and reasoning.

To facilitate the development of Agents, Google provides various tools and platforms, such as:

LangChain: An open-source library that helps developers build and deploy Agents. LangChain provides a set of APIs that make it easy for developers to combine LLMs with tools and orchestration layers to build powerful Agents.
LangGraph: An open-source library that helps developers build and visualize Agents. LangGraph provides a graphical interface that makes it easy for developers to design and test Agents.
Vertex AI: A cloud platform that offers various AI tools and services, such as Vertex Agent Builder, Vertex Extensions, Vertex Function Calling, etc., to help developers quickly build and deploy Agents. Vertex AI provides robust infrastructure and tools to facilitate Agent development, testing, deployment, and management.