NIIT India

Building Agentic AI Systems (Copy)

Fees: ₹ 1,50,000 + 18% GST 300 hours
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Agents for every ambition

Building Agentic AI Systems

  • Develop job-ready capabilities for high-growth AI and automation roles
  • Design Agentic AI systems that accelerate innovation
  • Build real-world skills through sprints modeled on industry workflows
  • Stay current with evolving AI tools through guided, collaborative learning
  • Get Placement Assistance via NIIT’s trusted recruiter connect

Get Program Guidance from our advisor

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    Turning ambitions into achievements
    • Learn AI from experienced mentors and professionals
    • Earn NIIT certification for GenAI career readiness
    • Create and present practical AI projects — from chatbots to agent teams
    • Build real-world skills through sprints modeled on industry workflows
    • Develop job-ready capabilities for high-growth AI and automation roles
    • Stay current with evolving AI tools through guided, collaborative learning
    • Transform AI ideas into impactful business solutions
    • Design agentic AI systems that accelerate innovation
    Agents for every Ambition
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      The NIIT advantage

      Skills you gain

      • Use Python to write clean, modular code, build APIs, and integrate external services.
      • Develop conversational AI agents that interact with users through web applications.
      • Engineer multimodal RAG pipelines augmented with structured SQL database.
      • Build multi-agent systems using LangChain, LlamaIndex, LangGraph, CrewAI.
      • Apply agent patterns ReAct, Plan–Act–Check, and Reflection for reliability.
      • Build a portfolio of agentic AI projects across real-world use cases.

      What you’ll learn

        Python for AI-Embedded Applications
      • Set Up Python and Write Fundamental Programs
      • Process and Manipulate Data with Python Structures
      • Build Reusable Functions and Modular Code
      • Create Structured Data Models with Classes
      • Perform I/O, Error Handling, and Validation
      • Call LLM APIs and Process Responses
      • Develop and Document REST API Endpoints
      • Write and Execute Tests for REST APIs
      • Implement Real-Time Communication Across Protocols
      • Enhance Code, Tests, and Documentation Using Coding Assistance
        Build and Deploy Intelligent Conversational Agents
      • Build AI agent with No-Code
      • Configure and Call LLM APIs with LangChain
      • Manage Prompts for Agents
      • Enable Tool Calling for Agents
      • Create Multi-Step LLM Chains with LCEL
      • Implement Short-Term Memory for Multi-Turn Conversations
      • Build Agent Frontends
      • Implement User Authentication and Authorization
      • Observe Agent Workflows with Langfuse Observability
      • Build Conversational Agent on Cloud
        Build RAG based Systems
      • Setup infrastructure for a RAG solution
      • Build RAG ingestion pipeline with LangChain
      • Build RAG query pipeline with LangChain
      • Implement Advanced Document Processing with LlammaIndex
      • Implement Multi-Modal Content Processing with LlamaIndex
      • Implement Secure Agentic RAG with GuardrailsAI
      • Augment LLM knowledge with structured data (SQL)
      • Integrate External Systems with the Model Context Protocol (MCP)
      • Add human handoff with context transfer
      • Evaluate RAG pipeline against SLOs
        Build Autonomous Multi Agent Systems
      • Build Your First Stateful Agent with LangGraph
      • Design Multi-Agent Workflows as State Machines with LangGraph
      • Implement ReAct Agents with LangGraph
      • Build Plan-Act-Check Agent Loops with LangGraph
      • Build Self-Correcting Agents with Reflection using LangGraph
      • Orchestrate multi-agent teams in CrewAI
      • Augment agent teams with shared context and knowledge in CrewAI
      • Harden against adversarial attacks with red teaming
      • Observe and debug multi-agent systems
      • Complete Readiness Review, Industry trends, and Handover
        Capstone project: Engineer an autonomous agentic AI system (SLO-bound)
      • Define Scope, Risks, and SLOs
      • Design the System and Plan Work
      • Build Goldens and Test Fixtures
      • Ship the Chat Shell and API
      • Implement RAG Core and Citations
      • Connect MCP Tools and Audit Trails
      • Add Agentic Flow and Escalation
      • Observe, Secure, and Set Budgets
      • Shadow Traffic and Tune for SLOs
      • Demo, Report, and Document Runbooks

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      Am I eligible to apply?

      Accelerate your career with placement opportunities!
      • If you have completed your graduation, you’re eligible to apply.
      • Minimum 6 months of relevant experience with at least one modern programming language web application development stack.
      • For more details on eligibility, please refer to the FAQ section.
      • To know more about placement assistance, visit the FAQ section.

      Enroll in just a few easy steps!

      • Fill application

      • Pay fees

      1. Application
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