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Agentic AI

Build the future of AI. Learn to create autonomous agents that can reason, plan, and execute complex tasks using LLMs and modern frameworks.

6 Months
1:1 Mentorship
Project-Based

What You'll Learn

This cutting-edge program focuses on the rapidly evolving field of AI agents. You'll learn to harness the power of Large Language Models to build systems that can autonomously complete complex tasks, reason about problems, and interact with external tools and APIs.

Understand LLM architectures and capabilities
Master prompt engineering techniques
Build RAG systems for knowledge retrieval
Create autonomous AI agents with LangChain
Implement multi-agent systems with AutoGen/CrewAI
Deploy AI applications to production

Why Learn Agentic AI?

The next frontier in artificial intelligence is here

Revolutionary Technology

AI agents represent the next evolution of AI. They can reason, plan, and execute complex tasks autonomously.

Explosive Demand

Companies are racing to integrate AI agents. Skilled developers who can build these systems are highly sought after.

Practical Applications

From customer service bots to research assistants, code generators to data analysts - AI agents are transforming every industry.

Powerful Frameworks

LangChain, AutoGen, CrewAI - learn the tools that make building sophisticated AI systems accessible.

Build Real Products

Create chatbots, research assistants, document Q&A systems, and multi-agent workflows that solve real problems.

Premium Career Path

AI engineers with agent-building skills command top-tier salaries. This is one of the most in-demand specializations in tech.

300% Growth in AI Agent Jobs
$15B+ AI Agent Market by 2028
75% Enterprises Adopting AI Agents
#1 Hottest AI Skill 2024-25

Career Opportunities

Roles you can pursue after mastering Agentic AI

AI Agent Developer

Build autonomous AI agents using LangChain, AutoGen, and modern frameworks.

Hot Skill

Conversational AI Engineer

Design and build intelligent chatbots and virtual assistants for enterprises.

High Demand

RAG Systems Developer

Build knowledge retrieval systems that connect LLMs to enterprise data.

Trending

LLM Application Developer

Integrate LLM capabilities into applications and build AI-powered products.

High Demand

Prompt Engineer

Optimize prompts and design effective AI interactions for enterprise applications.

Growing

AI Solutions Architect

Design end-to-end AI solutions combining agents, RAG, and enterprise systems.

Senior Role

Technologies Deep Dive

Master the cutting-edge AI agent technology stack

LLMs & APIs

OpenAI (GPT-4)

Industry-leading LLM with function calling, vision, and code generation capabilities.

Anthropic (Claude)

Safe, helpful AI with long context windows and excellent reasoning abilities.

Local LLMs (Ollama)

Llama, Mistral, and other models for on-premise and cost-effective deployments.

Embeddings

OpenAI, Cohere, and sentence-transformers for semantic search and RAG.

Agent Frameworks

LangChain

The most popular framework for building LLM applications with chains and agents.

Chains & LCEL

Build composable AI pipelines with LangChain Expression Language.

LangGraph

Build complex, stateful multi-actor AI applications with graph workflows.

AutoGen

Microsoft's framework for building multi-agent conversational systems.

CrewAI

Framework for orchestrating role-playing, autonomous AI agents.

Agent Architectures

Design patterns: ReAct, Plan-and-Execute, Reflection, Router, Multi-Agent.

OpenAI Assistants

OpenAI's official API for building stateful agents with threads and tools.

DSPy

Stanford's declarative framework for optimizing LLM prompts programmatically.

Semantic Kernel

Microsoft's enterprise SDK for building AI applications with plugins.

Haystack

Production-ready framework for building search and RAG pipelines.

RAG & Infrastructure

Vector Databases

Pinecone, Chroma, Weaviate, and Qdrant for storing and retrieving embeddings.

LlamaIndex

Data framework for connecting LLMs to custom data sources and knowledge bases.

GraphRAG

Combine knowledge graphs with RAG for complex multi-hop reasoning queries.

Agent Memory

Short-term, long-term, episodic & semantic memory for conversational agents.

MCP Protocol

Anthropic's Model Context Protocol for universal AI tool integration.

LangSmith

Observability and debugging platform for LLM applications.

Agent Safety

Guardrails, input validation, output filtering for production AI agents.

Advanced & Production

Multimodal Agents

Build agents that process images, audio, and video with GPT-4V and Claude Vision.

Browser Agents

AI-powered web automation with Playwright for scraping and task automation.

Voice Agents

Speech-enabled AI with Whisper and TTS for voice conversations.

Streaming Responses

Real-time AI output with SSE, WebSockets, and async streaming.

Cost Optimization

Reduce LLM costs with caching, token optimization, and model selection.

Agent Evaluation

Test and benchmark AI agents with RAGAS, DeepEval, and LLM-as-judge.

Curriculum Overview

A comprehensive journey into the world of AI agents

  • How LLMs Work (Transformers, Attention)
  • OpenAI, Anthropic, & Open Source Models
  • API Integration & Best Practices
  • Token Management & Cost Optimization
  • Model Selection for Different Tasks
  • Prompt Design Principles
  • Few-Shot & Chain-of-Thought Prompting
  • System Prompts & Personas
  • Output Formatting & Structured Generation
  • Prompt Templates & Management
  • Vector Databases (Pinecone, Chroma, Weaviate)
  • Embeddings & Semantic Search
  • Document Chunking Strategies
  • Retrieval Optimization
  • Hybrid Search, Reranking & GraphRAG
04

LangChain Deep Dive

05

Multi-Agent Systems

06

Production & Deployment

Projects You'll Build

Cutting-edge AI projects that showcase your skills

LangChain

Intelligent Chatbot

Build a context-aware chatbot with memory, personality, and the ability to use external tools.

Key Features:

  • Conversation memory and context tracking
  • Custom personality and system prompts
  • Tool integration (web search, calculator)
  • Streaming responses with Streamlit UI
LangChain OpenAI Streamlit
RAG

Document Q&A System

Create a RAG application that answers questions about uploaded documents with citations.

Key Features:

  • PDF and document ingestion pipeline
  • Semantic chunking strategies
  • Hybrid search with reranking
  • Answer with source citations
LlamaIndex Pinecone FastAPI
Multi-Agent

Research Agent Team

Build a multi-agent system where agents collaborate to research topics and write reports.

Key Features:

  • Specialized agent roles (researcher, writer, editor)
  • Web search and data gathering
  • Collaborative workflow orchestration
  • Output quality validation
CrewAI AutoGen Tavily
LangGraph

Code Assistant Agent

Create an AI coding assistant that can understand codebases, write code, and debug issues.

Key Features:

  • Codebase understanding with RAG
  • Code generation and refactoring
  • GitHub integration for PRs
  • Stateful conversation with LangGraph
LangGraph Claude GitHub API

Skills You'll Master

Technical skills to build production-grade AI agents

Technical Skills

LLM APIs & Integration Advanced
Prompt Engineering Advanced
LangChain & LangGraph Advanced
RAG Systems Advanced
Multi-Agent Frameworks Intermediate
Vector Databases Intermediate

Professional Skills

AI System Design Prompt Optimization Agent Architecture Cost Management AI Safety Evaluation & Testing Production Deployment Observability

Who Is This Program For?

Software Developers

Developers who want to integrate AI capabilities into their applications.

Data Scientists

ML practitioners looking to expand into the LLM and agent space.

AI Enthusiasts

Anyone passionate about AI who wants to build practical applications.

Prerequisites

  • Python programming proficiency
  • Basic understanding of APIs
  • Familiarity with web development concepts
  • Curiosity about AI and LLMs

No prior AI/ML experience required - we focus on practical application, not theory!

Frequently Asked Questions

Everything you need to know about our Agentic AI program

Do I need ML/AI experience to join?

No prior AI/ML experience required! We focus on practical application using APIs and frameworks, not deep learning theory. Python proficiency is sufficient.

What is the program duration?

The program runs for 6 months with flexible scheduling. Sessions are personalized 1:1 to fit your availability.

What makes this different from online courses?

The AI agent landscape changes rapidly. Our 1:1 mentorship keeps you updated with the latest frameworks and best practices as they emerge.

Will I need to pay for API costs?

We guide you on using free tiers and cost-effective strategies. Most projects can be completed with minimal API costs (often under $10-20 total).

What kind of projects will I build?

You'll build chatbots, document Q&A systems, research agents, and multi-agent systems - all deployable, production-ready applications.

Is this relevant for enterprise roles?

Yes! Enterprise adoption of AI agents is accelerating. We cover production concerns like cost management, safety, and observability.

How is the mentorship conducted?

Sessions are 1:1 with your mentor, either online or at our Kochi center. You get personalized attention, code reviews, and career guidance.

What kind of support do I get?

Beyond sessions, you get doubt clearing support, project guidance, interview preparation, and access to our AI community.

Ready to Build the Future of AI?

Book a free consultation to discuss your goals and start your journey into Agentic AI.