Building Agentic AI Systems
Agents for Every Ambition – a premium learning initiative by NIIT, building job-ready AI leaders with industry-driven training, real-world expertise, and in-demand artificial intelligence skills.
Program Overview
The Building Agentic AI Systems by NIIT is a future-ready program with placement assistance that builds job-ready AI and automation skills through hands-on projects, and industry-aligned learning. Learn to design intelligent agents, RAG-powered apps, and multi-agent workflows with real-world AI tools. This program is accredited by the National Council for Vocational Education and Training (NCVET), with industry certification awarded by the Confederation of Indian Industry (CII). Students can earn Academic Bank of Credits (ABC), gaining nationally recognized credentials that provide flexibility, and open pathways for seamless career progression across institutions and programs.
Curriculum
Comprehensive curriculum to build in-demand tech skills and real-world expertise.
Core Python & Data Foundations
Build strong fundamentals with Python structures, functions, modular code, and structured data models.
API Development & LLM Integration
Develop robust REST APIs, handle I/O and validation, and integrate LLM APIs into applications.
Testing, Real-Time Systems & AI Acceleration
Ensure reliability with testing, enable real-time communication, and leverage GenAI for code, docs, and optimization.
Build a modular FastAPI application delivering structured APIs and AI-powered responses from a clean backend design. Gain hands-on experience with Python, uv, FastAPI, Pydantic, pytest, and OpenAPI specifications.
Build AI Chat Applications and Agentic Workflows
Learn to develop conversational AI applications starting with no-code chatbot creation and progressing to advanced agentic workflows using LangChain and FastAPI.
Design Prompt-Driven Intelligent Agents
Build AI agents with structured prompting, tool calling, short-term memory, and multi-step LCEL chains for context-aware interactions.
Develop Interactive AI Frontends
Create responsive AI chat interfaces using Streamlit and React with authentication, session handling, and user authorization features.
Monitor and Optimize AI Systems
Implement observability with LangFuse to monitor traces, evaluate latency and token usage.
Build an AI agent system that integrates LLMs, tools, memory, and APIs into a reliable and observable application. Gain hands-on experience with LangFlow, LangChain, FastAPI, Pydantic, LCEL, LangFuse, Streamlit, React.
Build Knowledge-Backed RAG Applications
Learn to design and develop reliable Retrieval-Augmented Generation (RAG) systems that deliver accurate, grounded, and context-aware responses using enterprise knowledge sources.
Develop End-to-End RAG Pipelines
Build ingestion and retrieval pipelines with LangChain, implement chunking strategies, embeddings, semantic search, and vector database integration for efficient knowledge retrieval.
Implement Advanced and Multimodal Retrieval
Work with LlamaIndex and LlamaParse to process complex documents, multimodal content, tables, and images while enabling hybrid search and reranking techniques.
Integrate Structured Data and External Systems
Create SQL-powered RAG workflows, connect external systems using MCP architecture, and build agentic workflows combining structured and unstructured data retrieval.
Ensure Reliability, Security, and Observability
Implement guardrails, PII protection, citation generation, human handoff workflows, and evaluate RAG systems against quality, latency, and reliability goals.
Build a modular RAG-based intelligence system that retrieves, understands, and generates context-aware answers from complex domain-specific documents. Gain hands-on experience with PostgreSQL and pgvector, LangChain, LlamaIndex, GuardrailsAI, and MCP.
Build Stateful and Autonomous AI Agents
Learn to design intelligent AI agents using LangGraph that can plan, reason, act, and self-check through structured agent workflows and state machines.
Develop Multi-Agent Workflows with CrewAI
Create collaborative multi-agent systems with CrewAI using role-based agents, hierarchical crews, and orchestrated workflows for complex task execution.
Implement ReAct and Self-Corrective Agents
Build advanced agentic workflows with ReAct patterns, Plan-Act-Check loops, Reflection techniques, and self-correcting behaviors for reliable decision-making.
Optimize, Monitor, and Scale Agentic Systems
Implement observability, tracing, and evaluation frameworks while optimizing agentic systems for latency, quality, resilience, and cost efficiency.
Build an autonomous multi-agent system that collaborates, reasons, and executes complex tasks through structured workflows and intelligent delegation. Gain hands-on experience with LangGraph, CrewAI, ReAct, Reflection, Multi-Agent Workflows.
Build an End-to-End Agentic AI Solution
Design and develop a production-ready Agentic AI application by combining conversational AI, RAG pipelines, tool integration, and multi-agent workflows into a unified system.
Define AI System Goals and Architecture
Identify real-world use cases, risks, and Service Level Objectives (SLOs), then design scalable AI system architectures and execution plans.
Implement Intelligent Agentic Workflows
Build core agentic flows with LangGraph and CrewAI, integrate MCP-enabled tools, and expand workflows with reasoning-driven multi-agent collaboration.
Enable Observability, Evaluation, and Reliability
Create golden evaluation datasets, implement observability and tracing, apply guardrails, and optimize the system for quality, latency, and reliability targets.
Showcase a Production-Ready AI System
Deliver a live demo with logs, evaluation reports, documentation, and a fully integrated AI solution using modern agentic AI frameworks and tools.
Gain hands-on experience across all of the above and the chosen channel or cloud environment. Each capstone is defined with explicit SLOs (success rate, latency, cost, privacy).
Tools & Technologies
Explore industry-relevant tools through hands-on learning to master practical, in-demand skills.
Projects You'll Build
Build production-ready projects to showcase real-world skills and strengthen your portfolio.
Team Coding Standards Q&A Agent
An agent built on a no-code platform that answers programmers' questions on coding standards and guidelines for a software development team.
Trip Planner Agent
An intelligent assistant that designs personalized travel itineraries based on user preferences, optimizing routes, budgets, and experiences through AI-driven recommendations.
Personalized Job Placement Agent
A conversational agent that collects user details or a pasted resume, fetches live job descriptions, and suggests targeted resume improvements and personalized cover letters.
Smart Auto Advisor Assistant
A conversational agent for car dealerships that retrieves car model, features, pricing, and availability from a company database using RAG, and provides context-aware answers to customers.
Financial Advisor Agent
A conversational agent that gathers users' financial goals, risk preferences, and investment horizons, retrieves information from financial guides and market data using RAG, and delivers personalized recommendations.
Multi-Modal RAG System for Internal FAQs
A RAG system that integrates text and image data to answer internal FAQs accurately, enabling AI-powered knowledge discovery within organizations.
Customer Ticket Resolution Assistant
An AI agent that retrieves solutions from internal ticket histories, product guides, escalation procedures, and customer interaction logs using RAG, improving support response times while maintaining data privacy.
Multi-Agent Retail Policy Intelligence System
A crew of agents that interpret policies, validate decisions against business rules, and deliver consistent, explainable outcomes. Each capstone includes defined SLOs, a live demo, evaluation report, and production runbook.
Enterprise Software Support & Resolution Intelligence System
A hierarchical workflow of agents that diagnose issues using documentation and historical data, with intelligent escalation of unresolved or high-risk cases to human experts. Each capstone includes defined SLOs, a live demo, evaluation report, and production runbook.
Financial Risk & Investment Decision Intelligence System
A coordinated set of agents that analyze market signals, assess portfolio risks, and generate validated, risk-aware investment recommendations. Each capstone includes defined SLOs, a live demo, evaluation report, and production runbook.
Boost Your Career Visibility
Showcasing a professional Capstone Project on your LinkedIn increases recruiter interest significantly. Build production-ready work that speaks for itself.
Learning Outcomes
Develop industry-relevant skills to create real-world solutions and advance your career.
Use Python to develop clean, modular, and scalable applications, build robust APIs, automate workflows, and integrate seamlessly with external services and enterprise systems.
Build conversational AI agents across customer-facing use cases, integrating LLM APIs safely with structured outputs, tool calling, prompt management, and short-term memory.
Design and build production-grade RAG systems: ingest, chunk, embed, hybrid search (BM25 + vector), rerank, cite sources, apply PII guardrails, integrate external systems via MCP, and evaluate against SLOs.
Design and orchestrate autonomous multi-agent systems using LangGraph and CrewAI that plan, act, self-correct, and collaborate across stateful, multi-step workflows.
Implement agentic patterns - ReAct, Plan-Act-Check, Reflection, and Self-Corrective agents - as production engineering constructs, not just research concepts.
Instrument agentic systems with observability (Langfuse tracing), safety controls (guardrails, RBAC, cost caps).
Build and present a portfolio of Agentic AI projects - including a compliance agent team, financial analyst agent, and autonomous customer support workflow - demonstrating job-ready engineering competency.
Industry Salary Growth
Accelerate your career with strong salary growth across India.
Select Experience Level
Salary Growth
Annual PackagesCurrently Hiring Companies for this role
Industry-Recognized Certification
Certificate of Completion
This is to certify that
Has successfully completed the Building Agentic AI Systems
Signature
Date
Earn a trusted NIIT professional certificate and further strengthen your credentials with an additional paid industry certification. Refer FAQ.
Industry Certification by CII
Industry-recognized skills validated by Confederation of Indian Industry.
The Academic Bank of Credits (ABC)
Earn transferable academic credits for recognition, flexibility, and career growth.
Career Value with NCVET Accredited Program
Nationally recognized credentials enhancing employability and career growth.
Placement Assistance
Dedicated placement support with industry connections and recruiter interactions.
Placement Opportunity
Placement Assistance Policy
Dedicated placement assistance with leading industry hiring partners. Refer to the FAQ and T&C sections for placement eligibility details.
Our Hiring Network
800+ Top Recruiting Companies
Data-Driven Learning, Delivered with Quality
Experience a data-driven learning ecosystem with measurable progress.
Structured Learning Roadmap
Clear learning pathways delivered through our LMS with defined milestones and module progression.
Learner Connect Sessions
Regular live mentor interactions to resolve doubts, reinforce concepts, and maintain engagement.
AI Assisted Faculty Quality Monitoring
AI-assisted faculty performance analysis ensures consistent teaching quality and delivery excellence.
Program Performance Report
Track attendance, assignments, assessments, quizzes, and overall performance with structured progress tracking.
Meet Your Mentors
Learn from Industry Experts and Experienced Professionals.
Dr. Ayan Chatterjee
Chief Technology Officer
Dr. Ayan Chatterjee is the Chief Technology Officer at InsAnalytics, bringing over 8 years of research and development experience across Artificial Intelligence, Machine Learning, Mathematical Modeling, Medical Image Processing, and AI-driven Drug Discovery. A Gold Medalist in B.E, and a PhD in Computer Science from Northeastern University, Boston he combines academic excellence with global research credentials. His professional contributions include GPU architecture design at NVIDIA, with deployments in autonomous vehicle systems at Tesla. He has also led the end-to-end development of AI-powered medical imaging solutions for brain tumor diagnostics using MRI scans and built scalable AI platforms supporting drug discovery and gene therapy initiatives at AstraZeneca. With a strong foundation in both research and applied AI, Dr. Chatterjee brings deep technical insight and industry relevance to train the learners of Building Agentic AI Systems at NIIT.
Amrita Saha
Senior Data Scientist
Ms. Amrita Saha is a seasoned Senior Data Scientist with over 15 years of combined industry and R&D experience across AI/GenAI, Machine Learning, Big Data Analytics, and advanced Statistical and Econometrics Modeling. Her expertise spans diverse domains with hands-on proficiency in Robotics Process Automation (RPA), core economic research, and Business Intelligence with a strong focus on data visualization and dashboarding. She holds an M.Sc. in Economics, an M. Tech in Data Science & Engineering, and is a PhD Scholar in AI & Energy Economics at the Department of Finance & Economics, BITS Pilani. She plays a pivotal role in guiding learners in NIIT’s Building Agentic AI Systems program, enabling them to develop practical, industry-ready expertise in next-generation AI systems.
Frequently Asked Questions
Find answers to your queries about the program, curriculum, and admissions.
Graduates with 6+ months of hands-on experience in programming languages such as Python, Java, JavaScript, or C# are eligible to apply. Applicants should be comfortable writing and debugging code, using functions, classes, and APIs, working with REST APIs and JSON, and understanding client–server web applications. Experience building web apps using frameworks like Flask, Spring Boot, Node.js, or ASP.NET, along with basic familiarity with data handling and version control tools such as Git or GitHub, is preferred.
Admission to the program is direct. Eligible learners can apply by filling out the online application form, submitting the self-declaration and accepting the terms and conditions, and paying the program fee. Admission is confirmed once the payment is successfully completed.
The program follows NIIT’s Mastery Learning methodology, focusing on building one skill at a time through structured, hands-on sprints. Each sprint includes concept introduction through live mentor sessions, individual practice and assignments, mentor review and feedback, and refinement with re-submission of work. Learners typically spend around 4 hours per sprint, with most of the time dedicated to hands-on application and reflection
Graduates of the Building Agentic AI Systems program will be prepared for applied AI development roles with practical skills in Python, API development, conversational AI, RAG pipelines, and multi-agent AI systems. They will be able to design and deploy intelligent agents, build AI applications that interact with users and data sources, and orchestrate autonomous AI workflows using modern frameworks. Typical entry roles include AI Engineer, Generative AI Developer, AI Application Developer, and AI Automation Engineer.
Taking a loan is entirely optional. Loans are facilitated by third-party lenders, and NIIT has no role in the process. Applicants must review the loan terms carefully, including EMIs, interest rates, processing fees, and repayment schedules. All EMI payments and related queries must be handled directly with the lender.
Documents may include PAN Card and Aadhaar Card, last 6 months’ bank statements, 3 months’ salary slips (for employed learners) or ITR proof (for self-employed), and co-applicant details if the learner is under 21 years of age. Incomplete documentation may result in loan rejection.
Learners need a laptop or desktop with Intel i3 or AMD Ryzen 3 (or higher), at least 8 GB RAM, and 50 GB free disk space, along with a functional webcam and microphone. The system should run Windows 10 or macOS (or higher) with the latest Chrome or Edge browser, MS Office or equivalent tools, and a PDF reader installed. A stable Wi-Fi or broadband connection with a minimum speed of 5 Mbps is required, and a backup internet connection is recommended.
Yes, learners receive a digital certificate after successfully completing the program and meeting all the required conditions (overall performance score, attendance, payment clearance etc).
However, to obtain additional certification through the Confederation of Indian Industry (CII), learners are required to make a separate payment via the National Council for Vocational Education and Training (NCVET) portal.
Learners can choose to obtain NCVET (National Council for Vocational Education and Training) accreditation and CII (Confederation of Indian Industry) certification as additional credentials. These are optional and require separate registration and payment directly on the respective NCVET portal.
For academic progression, eligible learners can also receive credits through the Academic Bank of Credits (ABC). After successful program completion, learners must have an active ABC ID; the applicable credits are then submitted by the institution and deposited into the learner’s ABC account, where they can be accessed and used as per guidelines.
During the program, academic mentors will guide you through the process, including registration steps and requirements for these certifications and credits.
Learners with a minimum of 6 months and less than 5 years of work experience will be provided with up to 3 placement opportunities within 120 days, subject to eligibility. Placement services are available only to learners who have successfully completed the program with a minimum score of 70%, are graduates from a recognised University/Institute in Regular Mode, are declared placement-ready by the Delivery team, and are Indian citizens residing in India. NIIT’s responsibility is to provide relevant job opportunities for eligible learners to participate in the selection process; however, final placement depends on the learner’s preparation, participation, and performance in the hiring process. Learners with 5 years or more of experience will be eligible for Career Guidance support.
Hassle-Free Refund Policy
Your satisfaction is our priority. We offer transparent refund terms for your peace of mind.
100% Money-Back Guarantee
Full refund (excluding the booking fee) if you cancel before the batch starts
Quick Refund Review
Eligible refund requests are carefully reviewed within seven working days
Transparent Refund Timeline
Approved refunds are completed within 45 days for timely settlement
Important: Enjoy a transparent refund policy. Cancel 48 hours before the class start date to be eligible for a refund if you haven’t attended any class.
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