Generative Agent

As I was preparing slides for a recent workshop on Generative AI Agents, I realized something: the way people think about AI today feels a lot like how we thought about the “cloud” a decade ago. Back then, most people assumed cloud was just about storage.

Now, it’s the backbone of modern computing.AI agents are going through the same journey. Many see them as “just chatbots” but in reality, they’re digital teammates capable of planning, reasoning, and acting to accomplish real-world tasks. In this article, I’ll walk you through what agents are, how they work, where they’re already being used, and how you can start building one yourself on platforms like Google Cloud. We as humans sometimes act irrational based on emotional decision or lack of data also sometimes we lack of taking actions. AI agents sometimes also hallucinate and can act based on bias depends on data that has been fed into the model. Hence agents can aid

human and can not replace them if we think of them as tools to improve the execution of tasks or navigating uncertainty.

What Are AI Agents?

Instead of going with the formal definition which can be found in many articles or texst

books I will define an AI agent simply as an application that can:

1. Observe the world (through APIs, sensors, or data). Example of google APIs are
   flight API and weather API.


2. Decide what to do (using reasoning frameworks). Each framework has
   limitations and capabilities common framework are Re-Act, COT and TOT.


3. Act on the world (through tools or integrations). Reinforcement learning rewards
   good actions and punishes wrong actions allowing agents to adapt.
   Unlike traditional apps, agents are proactive. They don’t just follow a script they learn,
   adapt, and often anticipate the next step.
   Example: A self-driving car. You don’t tell it “turn left, then right, then brake.” You tell
   it the destination, and the car decides and acts.

Google Agent Development KIT (ADK):

Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows.

Types of Agents

Just as the cloud has IaaS, PaaS, and SaaS, agents also come in different forms:  Copilot Agents – Productivity boosters (think GitHub Copilot, Docs AI).

A. Workflow Automation Agents – Orchestrating repetitive tasks.  Virtual Workers – AI assistants that can handle customer service or sales end-toend.

B. Domain-Specific Agents – Medicine, law, finance, or education.

How Do Agents Think?

Agents use a reasoning loop — like a feedback cycle:

1. Plan – Break the task down.


2. Decide – Pick the next action.


3. Act – Do the task.


4. Reflect – Evaluate results and try again.
   This loop is powered by frameworks such as:
   A. ReAct (Reason + Act) – Reduces hallucinations, improves trust.
   B. Chain-of-Thought (CoT) – Step-by-step reasoning.
   C. Tree-of-Thought (ToT) – Exploring multiple possible solutions.
   Core Building Blocks
   Just like designing a cloud system, an AI agent is built from a few key components:
   A. Model: The brain (LLMs like GPT).
   B. Memory: Short-term (context window) + long-term (vector databases).
   C. Tools: APIs, extensions, and functions that let it interact with the outside world.
   D. Persona: The “character” of the agent (tone, style, role).
   AI Agent Workflow
   Example: A Call Center Agent (Google Agent Code
   Assist)
   Imagine you’re running a support center. Instead of relying only on human reps:
   A. The AI agent listens in.
   B. It suggests smart replies.
   C. Summarizes the customer’s problem.
   D. Detects if the customer is frustrated.
   E. Fetches relevant docs instantly.

The human stays in control but productivity and customer experience both improve dramatically.

Building Your First Agent on GCP

Google Cloud provides several building blocks for experimenting with agents:

A. Vertex AI – Run and fine-tune foundation models.

B. LangChain + Vertex Extensions – Orchestrate reasoning and tool use.  Memory with Vector Databases (like Pinecone or AlloyDB with pgvector) – Store long-term context.

C. Cloud Functions & APIs – Extend your agent to interact with external services.

A simple workflow might look like this:

1. Define the problem (e.g., “Help students summarize study material”).


2. Pick your model (Gemini via Vertex AI).


3. Add tools (Docs API, email API).


4. Add memory (vector search).


5. Deploy (Cloud Run, Workstations, or App Engine).
   Key Considerations (Just Like Cloud)
   When building agents, pay attention to:
   Trust & Safety
   A. Guardrails to prevent harmful or biased outputs.  Human-in-the-loop for sensitive domains (finance, healthcare).
   Cost Optimization
   B. Smaller, task-specific models can be cheaper than always using large LLMs.
   C. Cache responses where possible.
   D. Use scheduling (Cloud Workflows / Cloud Scheduler) to “pause” agents when not
   in use.
   Scalability
   A. Containerize your agent on Cloud Run for autoscaling.
   B. Use Pub/Sub for event-driven triggers.
   Real-World Use Cases
   Agents are already at work in multiple fields:
   A. Drug discovery (AI suggesting molecule interactions).
   B. Finance (trading bots).
   C. Legal research (patent analysis).
   D Education (student study assistants).

Summary

AI agents are to today what cloud was 10 years ago: a paradigm shift. They’re not just

chatbots — they’re the foundation of intelligent, proactive digital teammates.

With Google Cloud tools like Vertex AI, LangChain integrations, and scalable

infrastructure, anyone can begin experimenting with agents today.

The real question is: what task in your daily life or work would you trust your first

AI agent to handle?

Resources to Learn Generative Agent and ADK

A, https://www.cloudskillsboost.google/paths/1951

B. https://www.cloudskillsboost.google/course_templates/1382

C. https://www.cloudskillsboost.google/course_templates/1275