Generative AI Leader (Module 14): Agent Architecture, Reasoning Loops, ReAct, CoT & Tooling

Welcome to Module 14: Generative AI Agent Architecture and Engineering — one of the most technical and high-value modules in the Generative AI Leader journey.

In this module, we go deep into how modern AI agents are actually built, engineered, and tuned in production environments. You’ll move beyond “LLMs that talk” and understand agents as full systems composed of models, reasoning loops, and tools that can observe, decide, and act.

What you’ll learn in Module 14
🧠 Evolution of AI Agents

Deterministic (rule-based) agents vs Generative agents

Why traditional decision trees fail at edge cases

How RAG (Retrieval Augmented Generation) transformed agents

Hybrid agents: combining deterministic logic + LLM reasoning

🧩 Core Architecture of a Generative AI Agent

Every modern agent is built on three pillars:

Foundational Model (LLM like Gemini)

Reasoning Loop (decision-making engine)

Tools (APIs, functions, data stores, plugins)

You’ll see how these components work together to achieve real goals.

🔁 Engineering the Reasoning Loop

Learn how agents “think” and decide what to do next:

Iterative reasoning and goal evaluation

Tool selection and orchestration

Internal decision-making vs external actions

Prompt Engineering for Agent Logic

Role prompting

Few-shot prompting

Metaprompting (using AI to generate better prompts)

🧠 Advanced Reasoning Frameworks

Understand and compare the two most important agent frameworks:

Chain-of-Thought (CoT)

Step-by-step internal reasoning

Better accuracy and explainability

Ideal for complex logical problems

ReAct (Reason + Act)

Thought → Action → Observation loop

Dynamic interaction with tools and real-world data

Reduced hallucinations and higher trust

You’ll also see how CoT and ReAct can be combined for powerful hybrid agents.

🔧 Agent Tooling: From Text to Action

Agents become truly useful when they can act.

Tool categories covered:

Extensions (APIs)

Functions

Data Stores

Plugins

You’ll learn how tools fit into the agent’s operational cycle with a clear, real-world example (appointment scheduling).

☁️ Google Cloud Services for Agent Tooling

Build production-ready agents using Google Cloud:

Cloud Storage

Cloud SQL, Firestore, Spanner

Cloud Functions & Cloud Run

Vertex AI as an agent-to-agent tool

Pre-built AI APIs as Agent Tools

Speech-to-Text / Text-to-Speech

Translation & Document Translation

Document AI

Vision & Video Intelligence

Natural Language API

📍 Practical example: Meeting location planner using Document AI + Maps + Cloud Functions.

🎛️ Controlling Model Behavior

Master sampling parameters to tune agent behavior:

Token count

Temperature

Top-P / Top-K

Output length

Safety settings

Understand when to use low creativity vs high creativity depending on the task.

🧪 Experimentation Platforms

Compare:

Google AI Studio → fast prototyping, learning, experimentation

Vertex AI Studio → enterprise-grade, scalable, production systems

You’ll know exactly where to start and when to upgrade.

📌 Free Study Guide & Exam Resources

Access structured notes, diagrams, and exam-aligned explanations here:
👉 https://www.cloud-edify.com/google/ge...

🔥 Discounts & Course Deals

Verified coupons and bundles available at:
👉 https://www.cloud-edify.com/sale

🎯 By the end of this module, you’ll understand how AI agents are engineered end-to-end — from reasoning loops and prompt frameworks to tools, cloud services, and model tuning.

👍 Like, subscribe, and comment:
Which agent framework do you find more powerful — CoT, ReAct, or a hybrid approach?



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