Agentic AI - GCP Vertex AI And Google Gemini API

4.5 (300–500+ Learners)

Learn from industry experts with hands-on projects & live mentorship

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30 Sessions

Duration – 3 Months

English / Arabic

Certificate of Completion

Introduction to agentic AI and Google’s AI ecosystem

Designing and orchestrating autonomous AI agents

Integrating AI agents with data sources, APIs, and tools

Deploying, monitoring, and scaling AI solutions on GCP

Responsible AI, security, and governance best practices

Fundamentals of Agentic AI and autonomous systems

Overview of GCP Vertex AI and Google Gemini API

Prompt engineering and agent design patterns

Workflow orchestration and tool integration

Data handling and API integration for AI agents

Deployment, monitoring, and optimization on GCP

Security, privacy, and responsible AI practices

Understand core concepts of agentic AI and Gemini models

Identify business use cases suitable for AI agent implementation

Build and orchestrate AI agents using Vertex AI and Gemini API

Deploy and manage AI solutions in production environments

Apply responsible AI, security, and compliance principles

About the Course & Benefits

This course provides a practical and industry-focused introduction to building agentic AI solutions using Google Cloud Platform’s Vertex AI and the Google Gemini API. Participants will learn how to design, deploy, and manage autonomous AI agents that can reason, plan, and act within real-world business workflows, leveraging Google’s scalable AI infrastructure.

Benefits included:

KHDA-attested certificate + Medilearn certificate

Gain hands-on experience with agentic AI using GCP Vertex AI and Gemini

Learn to automate complex workflows using autonomous AI agents

Understand enterprise-grade AI deployment on Google Cloud

Build future-ready skills aligned with real-world business use cases

Course Circulation

Module 1

Module 2

Module 3

Module 4

Module 5

Module 6

Detailed Course Curriculum

Module 1: Foundations of Agentic AI and Gemini Models

Introduction to Agentic AI
- What is Agentic AI vs traditional AI & chatbots
- Characteristics of autonomous agents (planning, memory, tools, execution)
Understanding Large Language Models (LLMs)
- Transformer basics (high-level, no math overload)
- Prompting vs reasoning vs action
Overview of Google Gemini Models
- Gemini Nano, Pro, Ultra – capabilities & use cases
- Multimodal AI: text, image, audio, and video understanding
Gemini API Fundamentals
- Model inputs, outputs, tokens, and context windows
- System prompts, safety settings, and temperature control
Real-world Agentic AI use cases
- AI copilots, task automation, research agents, business workflows

Outcome:
Learners understand how agentic systems think, reason, and act using Gemini models.

Module 2: Google Cloud AI Ecosystem & Vertex AI Architecture

Introduction to Google Cloud AI ecosystem
Understanding Vertex AI
- Unified AI platform overview
- Managed vs custom AI workflows
Vertex AI components
- Model Garden (Gemini, foundation models)
- Workbench & notebooks
- Pipelines and experiments
Vertex AI vs direct Gemini API usage
- When to use APIs
- When to use managed Vertex AI services
IAM, billing, quotas & project setup
Hands-on:
- Setting up GCP projects
- Enabling Vertex AI & Gemini API
- Running first Gemini model on Vertex AI

Outcome:
Learners can confidently navigate GCP and understand where agentic workloads fit.

Module 3: Designing and Building Autonomous AI Agents

Agent architecture patterns
- Single-agent vs multi-agent systems
- Planner → Executor → Evaluator loop
Agent memory systems
- Short-term context memory
- Long-term memory using vector databases
Tool-using agents
- Function calling with Gemini
- Decision-making and task decomposition
Designing reliable agent behavior
- Instruction hierarchy (system, developer, user)
- Guardrails and fallback logic
Hands-on labs
- Build a task-planning agent
- Create a multi-step reasoning agent
- Implement retry, reflection, and self-correction

Outcome:
Learners can design intelligent agents that reason, plan, and execute tasks autonomously.

Module 4: Integrating Agents with APIs, Data, and Tools

Connecting agents to external systems
- REST APIs and web services
- Databases (SQL, NoSQL, BigQuery)
Retrieval-Augmented Generation (RAG)
- Vector embeddings
- Semantic search with Vertex AI
- Knowledge grounding for accuracy
Real-world integrations
- CRM, ERP, CMS, e-commerce, analytics tools
File, document & multimodal inputs
- PDFs, images, audio, spreadsheets
Hands-on labs
- Build a RAG-powered enterprise agent
- API-driven automation agent
- Data-aware business assistant

Outcome:
Agents move beyond chat—interacting with real systems, data, and workflows.

Module 5: Deploying, Scaling, and Monitoring (Production-Grade AI)

Deploying agents on Vertex AI
- Endpoints & inference services
- Serverless vs managed deployments
Scaling agentic systems
- Autoscaling strategies
- Cost optimization and token efficiency
Monitoring & observability
- Latency, throughput, error rates
- Model performance tracking
Logging & debugging agent behavior
CI/CD for AI agents
- Versioning prompts, models, and pipelines
Hands-on labs
- Deploy a production-ready agent
- Monitor and optimize performance
- Handle failures gracefully

Outcome:
Learners can confidently deploy and scale agentic AI systems in real production environments.

Module 6: Responsible AI, Security, and Governance

Responsible AI principles
- Bias, fairness, explainability
- Human-in-the-loop systems
Gemini & Vertex AI safety controls
- Content filtering and moderation
- Safety settings and policy enforcement
Security best practices
- IAM, service accounts, secrets management
- API security & data privacy
Compliance & governance
- Logging, auditing, and traceability
- Enterprise AI governance models
Ethical considerations for autonomous agents
Case studies & risk mitigation strategies

Outcome:
Learners build secure, ethical, and compliant agentic AI systems.

Fine-Tuning, Scale & Production (Capstone)

Fine-tuning Gemini models
- When fine-tuning is needed
- Dataset preparation & evaluation
Prompt engineering vs fine-tuning trade-offs
End-to-end agent optimization
- Accuracy, cost, latency tuning
Large-scale agent orchestration
- Multi-agent workflows
- Enterprise deployment patterns
Capstone Project
- Design, build, deploy, and monitor a full Agentic AI system
- Real-world business use case
Production readiness checklist

Final Outcome:
Learners graduate with **hands-on experience building, fine-tuning, deploying, and scaling agentic AI systems using Google Gemini API and Vertex AI.

MediLearn Certificate

Earn a Course Completion Certificate, an official credential from MediLearn, recognizing your dedication and hard work throughout the program. This certificate confirms that you have successfully met all course requirements, including assessments, practical activities, and guided learning sessions. It serves as a trusted proof of your newly acquired knowledge and skills, valued by employers and professional institutions. Display it with pride on your resume, LinkedIn profile, or portfolio to strengthen your professional credibility and career opportunities.

Student Success Stories

“Education is one of the most essential and valuable assets that an individual can possess, It plays a pivotal role in shaping.”

The Career Changer
"This course was the key to my career pivot. I went from a traditional IT role to an AI Specialist at a leading tech firm in Dubai. The hands-on GCP and Gemini API projects were exactly what employers were looking for."

The Promotion Earner
"After completing the Agentic AI course, I was able to lead a major project using Vertex AI, which directly led to my promotion to Senior Data Scientist. The practical skills are invaluable."

The Technical Expert
"As a backend engineer, I needed to upskill in AI to stay relevant. This course's deep dive into Google Gemini API on GCP was exactly what I needed to secure a new, higher-paying role."

The Strategic Leader
"To lead my team effectively, I needed to understand the strategic applications of agentic AI. This course gave me the knowledge to develop a new AI strategy that got me promoted to Director of Technology."

The Innovator
"I've always been interested in AI, but this course gave me the practical skills to actually build solutions. I was able to land a job at a cool tech startup in Dubai, working on cutting-edge projects."

The Specialist
"The healthcare sector is rapidly adopting AI. This course's focus on Google's advanced AI tools helped me secure a specialized role in medical data analysis, a field I'm passionate about."

FAQs

Which course is best for Agentic AI?

The most effective Agentic AI courses combine foundational AI concepts with hands-on engineering of autonomous agents. Programs that cover reasoning systems, RAG pipelines, tool integration, deployment, and evaluation provide the practical depth required to build and maintain real-world agentic AI applications.

How can one become an Agentic AI expert?

Becoming an Agentic AI expert requires learning autonomous agent design, reasoning frameworks, RAG architectures, API integration, and production deployment. Hands-on experience building, testing, and monitoring AI agents in real-world workflows is essential for developing advanced agentic AI expertise.

How much time does it take to learn Agentic AI?

Learning Agentic AI typically requires structured study over several months. A comprehensive program of approximately 60 hours enables learners to understand agent fundamentals, build RAG-based systems, integrate tools, and deploy AI agents in production-ready environments.

Is it worth learning Agentic AI?

Learning Agentic AI is highly valuable due to its growing adoption in enterprise automation, decision systems, and intelligent workflows. Skills in building autonomous AI agents position professionals for emerging roles in advanced and next-generation software development.

Can AI be learned in 3 months?

Yes, foundational and applied AI skills can be learned in approximately three months through focused, hands-on programs. Structured courses covering Generative AI, RAG, and Agentic AI enable learners to build functional AI systems within this timeframe.

Is AI a high-paying career?

AI is considered a high-paying career due to strong demand for skills in machine learning, generative systems, and agentic. Compensation varies by role and region, but professionals with hands-on AI deployment experience typically command premium salaries.

Which country is best for AI jobs?

Countries such as the United States, Canada, the United Kingdom, Germany, and the United Arab Emirates offer strong opportunities for AI professionals. These regions invest heavily in AI research, enterprise adoption, and innovation, creating sustained demand for talent.

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