Advanced Certificate Programme in Agentic AI and RAG Engineering
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A Curriculum Designed by Domain Experts, Powered by IITM Pravartak
Note:
Schedule for faculty masterclass will be shared post programme orientation.
Most comprehensive programme among educators offering Agentic AI and RAG technical certificate programmes.
Engineer the Next Generation of AI with Agentic and RAG Systems
Agentic AI is transforming enterprise systems by enabling AI agents that can reason, plan, and execute tasks autonomously, moving beyond traditional chatbots and static AI models. At the same time, Retrieval-Augmented Generation (RAG) is emerging as a core architecture for enterprise AI, allowing large language models to retrieve and use organisational knowledge to generate accurate, context-aware responses.
As organisations accelerate AI adoption, professionals across technology, product, and business roles increasingly need to understand how these systems work - so they can design, implement, and lead AI-driven initiatives while staying competitive in an AI-powered landscape.
80%
20x
50%
Note:
Salaries for AI roles in India are among the highest in the country. Your salary will vary based on your skills, experience, and the city where you work.
What This Agentic AI and RAG Engineering Programme Is About
While many AI programmes introduce agentic AI or RAG concepts, very few programmes provide in-depth engineering expertise in both Agentic AI and RAG systems together.
The Advanced Certificate Programme in Agentic AI and RAG Engineering by IITM Pravartak is uniquely designed to bridge this gap.
Design and implement multi-agent systems using LangChain, LangGraph, CrewAI, and vector databases
Build advanced RAG pipelines with memory layers, evaluation frameworks, and structured outputs
Learn through hands-on assignments and a comprehensive, end-to-end capstone project
Engage in a production-driven curriculum delivered through weekly live sessions led by domain experts with extensive industry experience
Attend IITM Pravartak masterclasses and opt for an immersive campus experience
Built for professionals who want to move beyond prototypes and engineer reliable AI systems ready for real-world deployment.
Programme Highlights
Live Online Sessions by Domain Experts
Weekly domain expert-led sessions with hands-on walk-throughs of tools, techniques, and real-world applications
IITM Pravartak Certification
A verified digital certificate upon successful programme completion
IITM Pravartak Lead Faculty Masterclasses*
Learn directly from Prof. Madhusudhanan B via select live masterclasses
3 IBM Industry Certificates
Additional credentials in Retrieval-Augmented Generation, LangChain, and AI Agent Development
15+ Tools and Frameworks
LangChain, OpenAI API, Hugging Face, Pinecone, Docker, and more
IITM Research Park Immersion
Two-day campus immersion event at IIT Madras Research Park (Optional)
Capstone Project
Solve complex industry problems through a comprehensive capstone project
Career Services Support
Six-months IIMJobs Pro membership, resume builder, and career preparation
Note:
The entire programme curriculum will be taught by Domain Experts and will also include a select live exclusive masterclasses by IITM Pravartak Lead faculty.
All programme highlights stated in this section and across the programme are subject to change at the discretion of IITM Pravartak and Emeritus.
Only participants who have successfully completed the programme will be allowed to visit the IITM Research Park.
Overall, 50% attendance required for live sessions to achieve programme completion. Live sessions include both domain expert sessions and lead faculty masterclasses.
Domain expert is the programme leader responsible for conducting weekly live sessions.
The immersion will only be conducted with a minimum number of learners signing up.
Schedule for faculty masterclass will be shared post programme orientation.
How This Programme Gives You the Edge
Building next-generation AI systems today requires more than an understanding of prompts or workflows. It demands professionals who can design, orchestrate, deploy, and govern autonomous AI systems end to end. This Agentic AI and RAG Engineering programme is built for professionals who want to move beyond experimentation and develop the capability to architect production-grade multi-agent systems with retrieval, memory, and scalable deployment.
Advanced Certificate Programme in Agentic AI and RAG Engineering | Other Outdated/Non-Accredited GenAI and Agentic AI Programmes | |
|---|---|---|
Certification from a Top Ranked Institution | Industry certificate from IITM Pravartak; 3 IBM certifications focused on RAG, LangChain, and AI Agent Engineering | Certification from non-accredited or low ranked institutes and additional certifications are rarely offered or come with add-on costs |
Programme Structure | Structured 7-month engineering-first curriculum designed for building and deploying production-grade Agentic AI systems | Short-duration programmes primarily focused on prompting or basic RAG concepts without full systems integration |
Depth of Curriculum | Comprehensive coverage across reasoning workflows, multi-agent orchestration, memory tiers, evaluation frameworks, observability, and scalable deployment stacks | Conceptual and theory-driven learning with introductory exposure to embeddings, agents, and context handling |
Capstone and Projects | End-to-end production capstone with hands-on assignments covering multi-agent systems, advanced RAG pipelines, and deployment workflows | 1-2-week capstone and limited projects focused on isolated features without lifecycle deployment |
Advanced Tools and Frameworks | Hands-on execution using industry-grade frameworks such as LangChain, LangGraph, CrewAI, OpenAI API, vector databases, FastAPI, and Docker | High-level automation or low-code tools taught under workflow-first approaches with limited production exposure |
Notes:
The entire programme curriculum will be taught by Domain Experts and will also include a select live exclusive masterclasses by IITM Pravartak Lead Faculty.
Schedule for faculty masterclass will be shared post programme orientation.
Who is this Programme for?
This programme is designed for professionals across engineering, data, and technology-driven roles who are ready to architect, build, and deploy production-grade Agentic AI and RAG systems in real-world environments.
Specifically, this programme is ideal for:
Data Science, AI, and Engineering Professionals
Software engineers, AI/ML engineers, data engineers, backend developers, and solution architects looking to move beyond model experimentation into designing multi-agent systems, advanced RAG pipelines, and deployable AI services using Python and modern orchestration frameworks.
Product and Technical Leaders
Technical product managers, platform leads, and innovation heads responsible for integrating AI into enterprise systems and seeking structured expertise in agent architectures, evaluation, deployment, and governance.
Mid-Career Technology Professionals
Professionals with prior exposure to programming, APIs, or backend systems who want to upskill into Agentic AI engineering and transition into high-impact AI system design roles.
This is an engineering-focused programme. While foundational concepts are reinforced, prior exposure to Python programming is required, and strong technical problem-solving skills are recommended to fully benefit from the depth of multi-agent orchestration, RAG engineering, and deployment modules.
With This Programme, You Will Be Able To:
Design and deploy intelligent multi-agent systems capable of reasoning, planning, and coordinated execution
Build advanced RAG pipelines with memory layers, evaluation frameworks, and structured outputs
Engineer reliable AI services using production deployment stacks, including APIs, containers, and monitoring tools
Implement secure, governed, and cost-aware AI systems aligned with real-world performance constraints
Position yourself as an AI Systems Engineer or Agentic AI Architect capable of leading next-generation autonomous AI initiatives
Eligibility criteria for this programme:
Minimum Graduate (10+2+3) and diploma holders with a minimum of 5 years of work experience; programming knowledge required
Programme Modules
Python execution model
Environment and package management
Core language constructs and file handling
API interaction and asynchronous programming
FastAPI services
Embeddings intuition
Testing with pytest
LLM-assisted coding workflows
Lightweight deployment with monitoring
Prototyping AI and agent workflows in Python
AI transformation overview
Automation vs agentic systems
Identifying agent-worthy problems
ROI and feasibility analysis
Human-in-the-loop design and risk classification
Enterprise adoption patterns
Translating business problems into agent requirements
What are AI agents
Agentic AI vs traditional systems
Agent lifecycle and capabilities
Levels of autonomy in AI systems
Real-world examples of agent
Agent vs application mental models
Task decomposition
Reasoning strategies (CoT, ToT, ReAct)
Prompt structure design
Structured outputs with Pydantic
Reliability-first mindset
Role and persona prompting
Instruction hierarchies
afety boundaries
Deterministic outputs using schemas
Few-shot prompt libraries
Critic-creator loops
Self-refine and verification patterns
Unit-test-driven prompting
Automated critique rubrics
Failure-mode catalogues
Prompt chaining patterns
Guardrails and validators
Schema validation with Pydantic
Retries and error handling
Minimal orchestration in Python
Orchestrator-worker architecture
Evaluator-optimiser loops
Router patterns
Sequential vs parallel vs conditional workflows
Workflow design diagrams
Classifiers and intent routers
Routing strategies
Parallel fan-out/fan-in pipelines
Aggregation and conflict resolution
Idempotent workflows
Short-term state vs working memory
Message and state graphs
Ephemeral vs persistent memory
Context window management
Token budgeting
Specification-driven agents
Testing strategies
Invariants and safety checks
Exception-handling strategies
SLAs and SLOs for agent systems
Tool schemas
Secure tool adapters
Tool selection strategies
Loop prevention
API rate limits
Retries and timeouts
Pydantic models and structured outputs
Function-calling patterns
JSON/Avro data pipelines
Schema validation and versioning
REST and GraphQL integrations
Authentication flows and secrets management
Web search agents
Grounding and citation strategies
Text-to-SQL agents
Database interaction patterns
Constrained updates and transaction safety
Audit logging
Vector database fundamentals
Document indexing and chunking
Embeddings and vector search
Hybrid retrieval strategies
Query reformulation
Reranking
Hallucination control
Multi-corpus retrieval systems
Multi-tool retrieval orchestration
Caching strategies
Freshness and knowledge drift management
Semantic, episodic, and procedural memory
Embeddings hygiene
Session stitching
Privacy and lifecycle policies
Summarisation pipelines
Golden datasets and synthetic evaluation
RAGAS and evaluation frameworks
Step-wise vs outcome evaluation
Cost-quality trade-offs
Multi-agent architectures
Agent roles and capabilities
Shared tools
Communication patterns
Resource contention control
Global vs local state management
Locks and leases
Conflict detection
Consensus strategies
Concurrent agent coordination
Planner-executor models
Subgoal generation
Supervisor agents
Human-in-the-loop escalation
Failure-recovery strategies
Specialised retrievers
Synthesis agents
Cross-agent memory sharing
Multi-agent knowledge systems
Load testing and red-teaming
MCP servers and tools
Secure tool exposure
FastAPI services
Containerisation
Infrastructure choices (serverless vs Kubernetes)
System tracing
Metrics and logging
Prompt and version tracking
Token usage monitoring
Rate-limit strategies
Data governance and PII protection
Policy enforcement
Prompt injection defence
Tool misuse prevention
Red-team playbooks
Unit and integration testing for agents
Offline and online evaluation gates
CI/CD pipelines
Blue-green deployment
Rollback strategies
Production monitoring
Note:
All programme curriculum - topics, modules, submodules, tools — stated here is subject to change as per the discretion of IITM Pravartak or Emeritus.
The entire programme curriculum will be taught by Domain Experts and will also include a few live exclusive masterclasses by IITM Pravartak Lead faculty.
Programme Certifications
IITM Pravartak will award a certificate of successful completion to participants who complete the programme successfully with 70% of the score in the evaluation.
Note:
All certificate images are for illustrative purposes only and may be subject to change at the discretion of IITM Pravartak and IBM.
To receive the completion certificate, participants must score a minimum of 70% overall on mandatory assignments and successfully complete the capstone project.
Overall, 50% attendance in both domain-expert led live sessions and live lead faculty masterclasses are required to achieve programme completion.
More than 15 Agentic AI and RAG Tools Covered
Note:
This page highlights only a selection of tools from a more extensive list available.
All product and organisation names are trademarks or registered trademarks of their respective holders, and their use does not imply any affiliation with or endorsement by them.
Tools will be provided via virtual labs for learning, as per the curriculum. Access will be given when the respective modules are taught.
Gain Credentials from IBM, a Pioneer in AI and ML Technology
Introduction to RAG
Build Applications with RAG
Build RAG Applications with LlamaIndex
Introduction to Vector Databases and Chroma DB
Vector Databases for Recommendation Systems and RAG
RAG Framework
Prompt Engineering and LangChain
Note:
All programme curriculum stated here is subject to change as per the discretion of IITM Pravartak, Emeritus, or IBM.
More than 20 Projects and Cases for Practical Applications
Python Programming Refresher
Build a production-ready Python AI project including APIs, embeddings utilities, FastAPI services, testing pipelines, and LLM-assisted coding workflows.
AI Problem Framing and Agent Use-Case Design
Identify a high-value agent use case, design an agent solution canvas, and define risk controls, autonomy levels, and success metrics.
Introduction to Agents
Analyse real-world agent systems and map the lifecycle of an AI agent from input to reasoning, tool use, memory, and feedback loops.
Agentic Reasoning for Engineers
Design structured reasoning workflows using CoT/ReAct patterns and build a reliable reasoning pipeline with structured outputs and validation.
Prompt Systems and Personas
Create persona-based prompt systems with structured schemas and reusable few-shot example libraries.
Iterative Prompt Dev and Feedback Loops
Implement prompt refinement workflows using critic-creator loops and automated evaluation rubrics.
Build a basic tool-using agent (e.g., calculator or web search wrapper) using either code or visual builder
Prompt Chaining in Code
Build prompt chaining pipelines with guardrails, schema validation, retries, and controlled execution flows.
Agentic Workflow Design
Design and compare agent workflow architectures such as orchestrator-worker, router, and evaluator-optimizer patterns.
Routing and Parallelization
Implement routing systems and parallel execution workflows with aggregation and conflict resolution strategies.
State and Memory Fundamentals
Design agent state management and implement a lightweight memory system with context management and summarization.
Reliability by Design
Define reliability specifications, failure taxonomies, and performance SLAs for production-grade agent systems.
Tool Use and Function Calling
Build secure tool integrations and implement function-calling agents with policies for tool selection and execution control.
Structured Outputs and Data Ops
Develop structured-output pipelines with schema validation, versioning, and downstream data integration.
External Integrations and Web Search
Design and integrate external APIs and web search tools with grounding and citation strategies.
Databases and CRUD Agents
Build database-aware agents capable of safe text-to-SQL operations and transaction-controlled updates.
RAG Foundations and Agentic RAG
Develop an agentic RAG pipeline with hybrid retrieval, reranking, query reformulation, and hallucination control.
Multi-RAG Patterns
Design multi-source retrieval systems with routing, caching, and knowledge freshness management.
Memory Tiers and Long-Term Memory
Implement long-term memory architectures with semantic, episodic, and summarization-based memory systems.
Evaluation for RAG and Agents
Build evaluation datasets and measure agent performance using structured metrics and cost-quality analysis.
Multi-Agent Systems I
Design and implement a collaborative multi-agent system with defined roles, shared tools, and communication protocols.
Multi-Agent State Coordination
Implement coordination mechanisms for concurrent agents using locks, leases, and conflict resolution strategies.
Multi-Agent Orchestration
Build planner-executor workflows with supervisor agents and human-in-the-loop escalation mechanisms.
Multi-Agent RAG and Tool Mesh
Develop a multi-agent tool mesh where agents retrieve, synthesize, and share knowledge across systems.
Productionization: Services and MCP
Package agent systems as deployable services with APIs, containerization, and tool interfaces.
Observability and Cost Control
Implement tracing, monitoring, and cost tracking for agent workflows with token and API usage visibility.
Security, Privacy, Governance
Design governance policies and defenses against prompt injection, tool misuse, and data risks.
Note:
All programme curriculum stated here is subject to change as per the discretion of IITM Pravartak, Emeritus, or IBM.
Learn Agentic AI and RAG with Masterclasses from Renowned IITM Pravartak Lead Faculty
Lead Faculty, IITM Pravartak
- Ph.D. Degree in Wireless Sensor Network with Artificial Intelligence from Anna University
- M.Tech. Degree in Computer Science and Engineering from M.Kumarasamy College OF E...
Emeritus Career Services Benefits
15 recorded sessions and resources in the above categories
Pro-membership and features of IIMJobs and Hirist: Access to job insights recruiter action status, follow-up actions, and ability to chat with recruiters who have shortlisted your profile.
Spotlight on IIMJobs and Hirist: Profile boost for applied jobs (that align with acquired certification), greater profile visibility - highlighted with institute name along with a testimony of certificate acquisition by the candidate.
Spotlight Plus: All the benefits of Spotlight and added advantages like profile and rank boost in the recruiter search database.
Resume builder tool: 6-month access to DIY resume builder, auto resume creator, optimization suggestions based on key parameters, guide on information to be incorporated, and unlimited resume iterations within the duration.
Notes:
IITM Pravartak or Emeritus do NOT promise or guarantee a job or progression in your current job. Career Services are only offered as a service that empowers you to manage your career proactively.
The Career Services mentioned here are offered by Emeritus. IITM Pravartak is NOT involved in any way and makes no commitments regarding the Career Services mentioned here.
This service is available only for Indian residents enrolled into selected Emeritus programmes.
Programme FAQs
Agentic AI refers to intelligent systems capable of reasoning, planning, using tools, managing memory, and executing multi-step goals autonomously. Unlike traditional AI models that respond to isolated prompts, agentic systems integrate retrieval (RAG), structured outputs, evaluation frameworks, and orchestration layers to function as reliable, production-grade systems in real-world environments.
Yes. The Advanced Certificate Program in Agentic AI and RAG Engineering is offered by IITM Pravartak (the technology innovation hub of IIT Madras). This engineering-focused programme enables learners to design, build, deploy, and govern production-ready multi-agent and RAG systems through structured weekly live sessions and masterclasses by IITM Pravartak faculty.
You will gain hands-on experience with industry-grade tools including:
LangChain
LangGraph
CrewAI
AutoGen
OpenAI APIs
Hugging Face
ChromaDB
Pinecone
FastAPI
Docker
These tools are used for building multi-agent systems, advanced RAG pipelines, deployment services, and scalable AI architectures.
This is an engineering-focused programme. Prior exposure to programming (preferably Python) and basic mathematical proficiency are required. Familiarity with APIs, backend systems, Git/GitHub, VS Code, and Linux environments is preferred to fully benefit from the depth of multi-agent orchestration and RAG engineering modules.
IITM Pravartak provides academic oversight and certification for the programme. It includes structured live sessions by Doamin Experts and a few select masterclasses by IITM Pravartak faculty, ensuring academic rigor alongside real-world engineering relevance.
Graduates and professionals will be prepared for roles such as:
AI Systems Engineer
Agentic AI Engineer
RAG Engineer
LLM Application Engineer
AI Solutions Architect
AI Platform Engineer
The programme is designed to support career progression into advanced AI system design and deployment roles.
Unlike generic GenAI programmes focused primarily on prompting or basic RAG workflows, this programme emphasizes full-stack AI systems engineering. You will learn
multi-agent orchestration, memory architecture, evaluation frameworks, CI/CD, observability, governance, and scalable deployment — enabling you to build production-ready AI services rather than prototypes.
You will complete multiple hands-on engineering assignments and a comprehensive end-to-end capstone project. The capstone involves designing, orchestrating, deploying, monitoring, and optimizing a production-grade Agentic AI & RAG system with real-world performance considerations.
Upon successful completion, you will receive a Certificate of Completion from IITM Pravartak (the technology innovation hub of IIT Madras). You will also earn three IBM certifications focused on RAG, LangChain, and AI Agent development.
You can apply directly through the official programme page. The application process includes eligibility verification to ensure learners have the required programming background. Flexible payment options may be available
Yes. The programme covers deployment stacks such as FastAPI and Docker, along with observability, cost control, evaluation frameworks, CI/CD practices, and secure AI system design to ensure scalability and reliability.
Yes. You will learn multi-agent architectures including role separation, planner–executor patterns, shared memory coordination, tool orchestration, and performance validation across concurrent agents.
Yes. The curriculum includes governance, security, cost monitoring, evaluation, structured outputs, and deployment best practices aligned with real-world enterprise AI system design standards
Early registrations are encouraged. Seats fill up quickly!
Flexible payment options available.
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