DEV Community: Ahmed Alolaywi

Generative Agent

Ahmed Alolaywi — Mon, 22 Sep 2025 18:28:43 +0000

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

Observe the world (through APIs, sensors, or data). Example of google APIs are flight API and weather API.
Decide what to do (using reasoning frameworks). Each framework has limitations and capabilities common framework are Re-Act, COT and TOT.
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:
A. Copilot Agents – Productivity boosters (think GitHub Copilot, Docs AI).
B. Workflow Automation Agents – Orchestrating repetitive tasks.
C. Virtual Workers – AI assistants that can handle customer service or sales end-toned.
D. Domain-Specific Agents – Medicine, law, finance, or education.

How Do Agents Think?

Agents use a reasoning loop — like a feedback cycle:

Plan – Break the task down.
Decide – Pick the next action.
Act – Do the task.
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.
C. Memory with Vector Databases (like Pinecone or AlloyDB with pgvector) – Store long-term context.
D. Cloud Functions & APIs – Extend your agent to interact with external services.

A simple workflow might look like this:

Define the problem (e.g., “Help students summarize study material”).
Pick your model (Gemini via Vertex AI).
Add tools (Docs API, email API).
Add memory (vector search).
Deploy (Cloud Run, Workstations, or App Engine).

Key Considerations (Just Like Cloud)
When building agents, pay attention to:
A.Trust & Safety
B. Guardrails to prevent harmful or biased outputs.
C. Human-in-the-loop for sensitive domains (finance, healthcare).
Cost Optimization
D. Smaller, task-specific models can be cheaper than always using large LLMs.
E. Cache responses where possible.
F. 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

Harnessing Vertex AI for Generative AI and Machine Learning on Google Cloud

Ahmed Alolaywi — Tue, 09 Sep 2025 05:46:17 +0000

Introduction

The interaction wit machine learning models has tremendously transformed with Generative AI in terms of deploying and building these models. It is possible via vertex AI as an open and flexible platform to build, deploy and connect agents for enterprise while having access to state of the art features. These features include agent builder, live API, thinking, grounding, generating embeddings, tuning, image generation, video generation and generative AI evaluation services. On vertex AI

What is Vertex AI?

Vertex AI is Google Cloud’s next-generation ML platform that simplifies building, deploying, and managing ML models. Unlike siloed tools, Vertex AI integrates every stage of the ML workflow into one environment—from data preparation and training to deployment and monitoring.
Key Features

Model Garden: Access pre-trained and foundation models for a wide range of use cases.

Generative AI Studio: Tools for prompt design, tuning, and rapid prototyping of LLMs.

MLOps Integration: Full lifecycle management—experiment, train, deploy, monitor.

Gemini API: Unlocks multimodal capabilities with text, images, and even video.

How Vertex AI Works

Vertex AI supports both AutoML (for low-code solutions) and custom training (for advanced users). The workflow is straightforward:

Gather your data – define problem scope.

Prepare your data – format and label.

Train – select training options (AutoML or custom code).

Evaluate – review performance metrics.

Deploy – scale predictions through batch or endpoints.

Monitor – ensure performance and fairness over time.

The Gemini API: Powering Generative AI

Google DeepMind’s Gemini models are integrated directly into Vertex AI via the Gemini API. This enables:

Gemini Pro: Natural language, multi-turn conversation, and code generation.

Gemini Pro Vision: Multimodal prompts that combine text, images, and video.

These capabilities open doors for building AI agents, domain-specific LLMs, and enterprise-ready GenAI applications.

Supported Frameworks & Datasets

Vertex AI natively supports frameworks like TensorFlow, PyTorch, XGBoost, and scikit-learn, allowing smooth integration with existing workflows.

It also accommodates diverse dataset types:

1.Images

2.Tabular

3.Text

4.Video

MLOps with Vertex AI

One of Vertex AI’s strongest advantages is end-to-end MLOps orchestration:

Continuous training & deployment

Unified endpoints for scalable serving

Predictive serving for real-time applications

Built-in monitoring for drift detection

Hands-On Labs

For learners who want to dive deeper, Google Cloud offers guided labs like:
👉 Build and Deploy Machine Learning Solutions on Vertex AI
https://www.cloudskillsboost.google/course_templates/684
These labs provide a safe environment to practice data prep, training, and deployment.

Conclusion

Vertex AI is more than just a tool—it’s an ecosystem for scaling Generative AI and ML responsibly. By combining foundation models, MLOps, and the Gemini API, Google Cloud enables developers to go from idea to production seamlessly.

If you’re building with GenAI, now is the perfect time to explore how Vertex AI can accelerate your journey.

Follow me for more deep dives on Generative AI and Google Cloud.

🌐 LinkedIn: https://sa.linkedin.com/in/ahmedalolaywi