Google on AI Agents

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Google recently published a white paper on AI agents.

The paper explains how agents utilize reasoning frameworks like ReAct and Chain-of-Thought.

Here is a quick read from the paper.

Let’s dive in.

Table of Contents

• What Are AI Agents?

• The Anatomy of AI Agents

• How AI Agents Reason

• Building Smarter Agents

• How to build AI Agents?

What Are AI Agents?

AI agents are advanced programs that can think, act, and achieve goals.

They are not just programs trained on static datasets.

They can reason and use tools to interact dynamically with their environment.

These agents can autonomously determine their next steps without requiring constant human input.

This independence distinguishes agents from traditional AI models, which are limited to the data they were trained on and lack memory or the ability to interact with external systems.

AI agents, on the other hand, can

• access new information

• remember past interactions

• utilize built-in tools to execute tasks effectively

See the whole document here: Google’s white paper - Agents

The Anatomy of AI Agents

Every AI agent consists of three essential components:

1. Model
   The model acts as the brain, using advanced language models to process information and make decisions

2. Tools
   Tools allow agents to interact with the outside world which includes -

   • Extensions to connect with APIs for real-time data

   • Functions that enable developers to control processes on the client side

   • Data stores powered by vector databases provide up-to-date and relevant information

3. Orchestration Layer
   This layer manages the agent’s reasoning and planning, ensuring it makes the best decisions based on its environment and goals

How AI Agents Reason

AI agents are equipped with frameworks that guide their decision-making -

• ReAct 
  (Reason + Act) ensures thoughtful and deliberate actions

• Chain-of-Thought 
  facilitates step-by-step reasoning for complex problems

• Tree-of-Thoughts 
  explores multiple paths to find the best solution

These reasoning techniques allow agents to handle complex tasks, adapt to new challenges, and deliver precise results.

Building Smarter Agents

Developers can improve AI agents through various learning methods-

• In-context learning provides examples during runtime.

• Retrieval-based learning taps into external data stores for optimal solutions.

• Fine-tuning trains agents on specific datasets for better performance.

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How to build AI Agents?

For now, you can check these platforms:

• n8n

• Dify

• Vertex AI Agent Builder

• Relevance AI

• Make AI

I am hosting a webinar on AI Agents this weekend and will share details with you by email, or for a quick update join the WhatsApp community.

You can ask your questions by replying to this email.