Agentic AI

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Agentic AI Developer

An Agentic AI Developer course trains software engineers and AI practitioners to design, build, and deploy autonomous AI systems—known as AI agents—that can reason, plan, and take actions to achieve goals with minimal human intervention. 

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This advanced program moves beyond simple prompt-and-response AI to the creation of goal-oriented, self-sufficient applications. Learners master the architecture of intelligent systems that can perceive their environment, make autonomous decisions, utilize external tools (like APIs and databases), and learn from feedback.

TKey focus areas include:

Ethical AI: Understanding and implementing safeguards to ensure AI agents operate responsibly, safely, and align with human values. 

Agent Design Patterns: Implementing core reasoning frameworks such as Reflection, Tool Use, Planning, and Multi-Agent Collaboration.

Modern Frameworks: Hands-on experience using industry-leading tools like LangChain, CrewAI, AutoGen, and LangGraph to orchestrate complex workflows.

System Integration: Connecting agents to real-world data sources via APIs and vector databases using Retrieval-Augmented Generation (RAG) to ensure accuracy and provide up-to-date information.

Deployment & Operations (AgentOps): Learning how to test, evaluate, monitor, and deploy production-ready AI agent systems securely and efficiently on cloud platforms like AWS, Azure, or GCP.

Course Features

  • Instruct led interactive session
  • Access session recordings
  • Access session notes
  • Access E-Material
  • Access E-Materials
  • Periodic Assessment and Test
  • 120 Hours (48 Hours Instrector led)
  • Full Lifetime Access
  • Access on Mobile and TV
  • Certificate of Completion

Course Content

10 sections • 48 lecture • 48h 33m total length

Topics: Distinction between traditional AI, Generative AI, and Agentic AI; core components of an AI agent (perception, memory, planning, action, learning); types of agents (reflex, goal-based, utility-based).

Skills: Understanding the agentic paradigm, analyzing use cases, and grasping the role of Large Language Models (LLMs) as the agent’s “brain”.

Topics: Key architectural components (cognitive modules, action modules, security); essential design patterns like ReAct (Reasoning and Acting), Reflection, Tool Use, and Planning.

Skills: Designing scalable and secure AI architectures, implementing reasoning loops, and enabling agents to critique and refine their own outputs.

Skills: Building RAG pipelines, managing short-term and long-term memory for agents, and enabling agents to interact with real-world systems beyond their training data.

Topics: Integrating agents with external data using Retrieval-Augmented Generation (RAG); data ingestion, text splitting, embeddings, and vector databases (e.g., Pinecone, ChromaDB); Function Calling for accessing external APIs and tools.

Skills: Building agents with LangChain Expression Language (LCEL), managing state with LangGraph, and orchestrating multi-agent collaboration systems to solve complex tasks.

Topics: Hands-on training with leading frameworks like LangChain, CrewAI, AutoGen, and LangGraph.

Skills: Taking an agent prototype to a production-ready system, managing performance, and ensuring ethical and responsible AI behavior.

Topics: Testing, benchmarking, and evaluating agent performance; observability and monitoring using tools like LangSmith or Langfuse; security, safety, and guardrails; deployment strategies (e.g., Docker, FastAPI, AWS Bedrock, Azure OpenAI).

Projects: Trainees apply their skills to real-world projects such as a personalized financial advisor agent, an autonomous HR agent for onboarding, or a multi-agent market research team simulation.

Agentic AI developer training projects typically involve building autonomous systems that use reasoning, planning, and external tools to solve real-world problems. These hands-on projects are designed to demonstrate a trainee’s ability to build production-ready agents. 

Here are several project ideas suitable for trainees:

1. The Autonomous Financial Assistant

This project moves beyond a simple expense tracker to an agent that actively monitors finances and provides proactive, personalized advice. 

  • Goal: Create an agent that securely connects to mock bank data, categorizes spending, analyzes market conditions, and provides personalized reports and low-risk investment suggestions via an email or app notification.
  • Key Skills: Tool use (financial APIs), data analysis (using libraries like pandas), RAG, and generating structured, personalized reports.
  • Frameworks: LangChain, Python, a data aggregator API sandbox (e.g., Plaid sandbox), and a market data API (e.g., Alpha Vantage). 

2. The Multi-Agent Content Marketing Team

This project highlights multi-agent collaboration, where different AI personas work together to achieve a complex goal. 

  • Goal: Orchestrate a “team” of agents (e.g., a “Researcher,” a “Strategist,” and a “Copywriter”) that autonomously research trending topics, generate a content calendar, and draft ready-to-publish blog posts and social media snippets.
  • Key Skills: Multi-agent orchestration, prompt design for collaboration, tool use (web search/scraping APIs, a CMS API like WordPress), and managing a full workflow.
  • Frameworks: CrewAI or AutoGen, a web search API (e.g., Serper Dev), and a content management system API. 

3. The Intelligent Research Co-pilot with Citations

A valuable project that demonstrates how to improve the factual accuracy of AI systems. 

  • Goal: Build an agent that can answer complex research questions by searching multiple sources, synthesizing the information, and producing a comprehensive, cited report to avoid hallucinations.
  • Key Skills: RAG implementation, web browsing/scraping, document processing, and ensuring output transparency/citations.
  • Frameworks: LangChain, a vector database (e.g., Pinecone, ChromaDB), web search tools, and Pydantic for structured outputs. 

4. Automated Task Solver with Code Generation and Debugging 

This project is ideal for those interested in software development and DevOps. 

  • Goal: Create an agent that can take a high-level task request (e.g., “Write a Python script to sort files in a directory by size”) and autonomously generate, execute, and debug the necessary code.
  • Key Skills: Code generation, shell execution, testing/debugging loops, and safe sandboxing.
  • Frameworks: AutoGen, Python execution environments, and potentially LangGraph for managing the planning and execution loops. 

These projects provide practical, resume-worthy experience in designing and implementing goal-oriented AI systems. 

Feature Courses:

PROMPT ENGINEERING

Talking to AI for a desired task through code, content or images

Gen AI, Core Prompt Techniques, COT, TOT, RAG, AI Tools and Libray, Testing and Evaluation, LLMOps

₹ 32000

AI FULLSTACK DEVELOPER

Skill set to design, develop, and deploy an entire web application

UI/UX:HTML,CSS,BS,JS,React,Angular Back End: C# – ASP.NET, Python, Java Spring Boot, SQL. AI Tools.

₹62000

AGENTIC AI DEVELOPER

Develops goal oriented and self sufficient AI Agents, Autonomous AI

Agent design, LangChain, CrewAI, AutoGen, and LangGraph, RAG, AgentOPs, Ethical AI.

₹82000

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  • The future workforce will not lose their jobs to AI, but to people who know how to use AI. Our mission is to equip every aspiring engineer with the practical expertise and global certifications required to become the indispensable ‘AI whisperers’ of tomorrow’s major companies
    Kannan Madhesan
    Founder and CEO
Diverse Curiculum

National and International curriculum  provides a direct pathway to achieving globally recognized certifications and securing desirable career placements.

Expert Instructors

The Instructor are active industry professionals who apply their skills daily. They provide current, real-world practical insights that prepare learners for actual job demands.

AI Enabled Learning

Interactive live sessions with hands-on, live practical, leveraging modern digital tools and AI- automation for seamless learning, assessment, and support.

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