• Generative AI

Understand foundation models, generative architectures, multimodal AI, AI content generation, and practical applications of Generative AI.

• Large Language Models

Learn how LLMs work, including tokens, context windows, model capabilities, limitations, prompting, APIs, and application integration.

• Prompt Engineering

Design structured prompts for different tasks, workflows, reasoning-oriented applications, content generation, and AI-assisted problem solving.

• Retrieval-Augmented Generation

Build AI applications that combine language models with external knowledge through document processing, embeddings, retrieval, vector search, and RAG architectures.

• AI Agents

Understand how AI agents can reason, plan, use tools, access information, execute tasks, and work through multi-step objectives.

• Agentic Workflows

Design workflows where AI systems can coordinate multiple tasks, make decisions, call tools, process information, and take actions.

• Tool Use & Function Calling

Learn how AI applications can interact seamlessly with APIs, databases, external software tools, and digital services.

• AI Memory & Context

Explore approaches for managing context, conversation history, persistent knowledge, state, and memory in intelligent AI systems.

• Multi-Agent Systems

Understand how multiple specialized AI agents can collaborate, communicate, and solve complex problems within structured workflows.

• AI Application Development

Connect AI models, APIs, data sources, tools, and user interfaces to create practical, production-ready AI-powered applications.

• AI Evaluation

Learn how to systematically evaluate AI outputs, reliability, accuracy, hallucination risks, agent performance, and application quality.

• Responsible AI

Understand important considerations around privacy, bias, safety, security, transparency, human oversight, and responsible deployment.

• GenAI & Systems Foundations

Understand the core technical foundations behind modern Generative AI architectures and intelligent systems.

• LLM Capabilities & Limitations

Explain the underlying mechanisms, operational boundaries, and practical limitations of Large Language Models.

• Advanced Prompt Design

Design precise, structured prompts and system instructions across diverse operational AI workflows.

• APIs & Dev Platforms

Work directly with Generative AI APIs, developer SDKs, and enterprise cloud AI platforms.

• LLM App Engineering

Architect and build production-ready applications powered by Large Language Model logic.

• RAG Application Design

Implement end-to-end RAG systems using vector indexing, embeddings, and context retrieval.

• AI Agent Architectures

Comprehend core agentic patterns including reasoning loops, tool routing, and goal planning.

• Tool-Equipped Autonomous Agents

Build practical AI agents capable of function calling, external tool usage, and multi-step execution.

• Agentic Workflow Automation

Design robust agentic workflows tailored to practical enterprise and technology automation needs.

• System Integration & Connectors

Connect AI agents with REST APIs, databases, software platforms, and external web services.

• Context & Memory Management

Incorporate persistent memory, state tracking, and long-term conversation storage into AI agents.

• Multi-Agent Collaboration

Understand multi-agent dynamics, delegation, specialized agent roles, and team collaboration frameworks.

• AI Output & Agent Evaluation

Rigorously benchmark agent performance, task success rates, hallucination, and system reliability.

• Safety, Security & Trust

Identify security vulnerabilities, prompt injection risks, data privacy constraints, and responsible oversight.

• Practical AI Automation

Develop automated solution pipelines that streamline complex, knowledge-intensive business operations.

• Portfolio-Grade Projects

Construct a showcase of functional Agentic AI implementations demonstrating market-ready skills.

• End-to-End Flagship Capstone

Design, build, and present a complete multi-agent automated solution tackling a complex real-world problem statement.

Agentic AI Focus

Go beyond conventional Generative AI and explore AI agents, agentic workflows, tool use, planning, memory, and multi-agent systems.

End-to-End Learning

Develop knowledge across Generative AI, LLMs, RAG, agents, AI applications, automation pipelines, and responsible deployment.

Build Real AI Applications

Apply theoretical concepts directly through hands-on implementation tasks, experiments, coding projects, and full-stack integration.

AI Automation

Explore how AI agents support repetitive, multi-step, knowledge-intensive operations and decision-support tasks.

Hands-On Learning

Master concepts through practical exercises, interactive coding activities, task implementations, case studies, and live labs.

Portfolio Development

Build practical, production-ready AI applications that demonstrate your technical proficiency to prospective employers or clients.

Business & Tech Use Cases

Apply agentic systems across customer support, research, marketing, operations, education, finance, productivity, and knowledge management.

Responsible AI

Prioritize system reliability, data privacy, threat mitigation, human oversight, evaluation, and safe AI deployment.

Live Online Learning

Learn through a structured, interactive live-online format crafted to accommodate full-time students and working professionals.

Knowledge Assessments

Evaluate theoretical and technical understanding across core domains:

Practical Assignments

Complete hands-on activities involving prompts, APIs, RAG architecture, AI agents, workflows, tool integration, and custom AI application development.

Applied Projects

Develop practical, production-ready AI solutions based on realistic technology and business use cases across various industries.

Final Flagship Capstone

Design, build, evaluate, and demonstrate an end-to-end Agentic AI solution showcasing technical mastery and real-world execution capability.

Earn Your Brainy Gaggle Certificate

Q. What does the PG Program in Agentic & Generative AI cover?

The program covers Generative AI, Large Language Models, Prompt Engineering, RAG, AI Agents, Agentic Workflows, Tool Use, AI Automation, Multi-Agent Systems, AI Application Development, AI Evaluation, and Responsible AI.

Q. Is this program suitable for beginners?

The program starts with foundational concepts before progressing toward advanced areas such as RAG, AI agents, agentic workflows, and multi-agent systems. Basic computer literacy and a willingness to learn programming are recommended.

Q. Do I need prior programming experience?

Prior professional programming experience is not mandatory. Python and technical foundations are introduced during the program. However, learners should be prepared to practice programming and technical implementation regularly.

Q. What is Agentic AI?

Agentic AI refers broadly to AI systems designed to pursue objectives through processes such as reasoning, planning, tool use, decision-making, and multi-step task execution.

Q. Will I build AI Agents?

Yes. The program includes practical learning around agent architecture, tool use, function calling, planning, memory concepts, workflows, evaluation, and multi-agent systems.

Q. Does the program include RAG?

Yes. Learners develop an understanding of document processing, embeddings, vector search, retrieval, context management, RAG architecture, implementation, and evaluation.

Q. Will I learn how AI agents use tools?

Yes. The program covers tool-enabled AI applications, function calling concepts, API integration, databases, external services, and workflow execution.

Q. Does the program cover Multi-Agent Systems?

Yes. Learners explore specialized agents, agent collaboration, task delegation, coordination, supervisor-worker patterns, and multi-agent workflows.

Q. Are practical projects included?

Yes. The program includes practical assignments, implementation tasks, applied projects, and a final flagship capstone.

Q. What career paths can this program support?

Q. Can non-technical professionals join?

Yes. The program introduces foundational concepts before progressing into technical areas. However, learners should be willing to develop programming and AI application skills.

Q. What is the learning format?

The program is delivered Live Online, supported by structured learning activities, practical assignments, projects, assessments, and a final capstone.

Q. How is the program assessed?

Assessment may include knowledge checks, programming exercises, practical assignments, AI application tasks, projects, and the final capstone, depending on the program's assessment structure.

Q. Is this a degree?

No. Unless separately stated and formally awarded through an appropriately authorized academic institution, this is a professional postgraduate program/certificate program offered by Brainy Gaggle and should not be represented as a university degree.

Q. How is this program different from a Generative AI course?

This flagship program goes beyond Generative AI fundamentals by covering LLMs, RAG, AI Agents, Agentic Workflows, Tool Use, AI Automation, Multi-Agent Systems, AI Application Development, Evaluation, and Responsible AI.