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Advanced Engineering Program in AI Agent Workflows and Agentic Systems Development
Orchestrate and scale strategies trusted by enterprise AI teams
Bachelor’s degree(CS, IT, Mathematics)
1+ years of work experience required
Prior knowledge in programming preferred
Start Date
1st Aug 2026 (tentative)
Duration
7 Months | 140+ Hrs
Course Fee
₹1,25,000 + 18% GST
No. of Seats Left
6
Credits
18
Program Highlights
Build AI-driven business processes using multi-agent orchestration & context engineering.
Integrate AI agents with APIs and business systems, supported by human-in-the-loop governance.
Get hands-on experience with 30+ tools while building a deployable capstone.
Learn through weekend sessions with IIT faculty & industry leaders, with an optional 3-day campus immersion.
About IITM Pravartak
IITM Pravartak Technologies Foundation is the Technology Innovation Hub of IIT Madras, established under the Department of Science and Technology, Government of India. Embedded within the IITMRP ecosystem, it combines renowned faculty, cutting-edge labs, and specialised research facilities to drive skilling, innovation, and incubation.
Working at the intersection of academia, research, and industry, IITM Pravartak fosters the adoption of deep technology, nurtures next-generation talent, and enables real-world impact.
Our Program Director
Prof. Balaji Srinivasan
B.Tech from IIT Madras, MS from Purdue University, and PhD from Stanford University
Prof. Balaji Srinivasan is a Professor in the Department of Mechanical Engineering and founding core faculty at the School of Data Science and Artificial Intelligence at IIT Madras, pursuing research in the areas of fundamental Machine Learning and Deep Learning with focus on applications to science and engineering disciplines. He earned his B.Tech from IIT Madras, MS from Purdue University, and PhD from Stanford University, where he was the William K. Bowes, Stanford Graduate Fellow. Prior to his current role at IIT Madras, he was a faculty member at IIT-Delhi and a post doctoral fellow at University of Michigan, Ann Arbor. His current research involves developing computational algorithms and models for a range of practical engineering problems that use a combination of probabilistic models, PDE based approaches as well as data-driven approaches. He has published research papers across multiple domains including Machine Learning, Partial Differential Equations, Computational algorithms, and High performance computing.
Prof. Ganapathy Krishnamurthi
PhD from Purdue University, and MSc in Physics from IIT Madras
Prof. Ganapathy Krishnamurthi is a Professor in the Department of Engineering Design and founding core faculty at the School of Data Science and Artificial Intelligence at IIT Madras. He earned his PhD from Purdue University, and MSc in Physics from IIT Madras. He worked as a post-doctoral research fellow at Case Western Reserve University, USA and at Mayo Clinic, USA. His research and work experience focuses on applying Machine Learning and Artificial Intelligence techniques to problems in medical image analysis, computer vision, interpretability/explainability ability of Deep Learning models across various applications and using deep learning to solve inverse problems in medical imaging and computer vision. His current research involves developing deep learning solutions for time series data in business, engineering and imaging applications. He has published numerous research papers pertaining to Deep Learning and Machine Learning applied to many areas in science, engineering and technology.
Prof. Kushal Shah
B.Tech and Ph.D. in Electrical Engineering from IIT Madras
Prof. Kushal Shah is a Professor of Applied Mathematics and Computer Science. He previously served as Associate Professor in the Department of Electrical Engineering and Computer Science at IISER Bhopal and as Assistant Professor in the Department of Electrical Engineering at IIT Delhi. He earned his B.Tech and Ph.D. in Electrical Engineering from IIT Madras, and later pursued postdoctoral research as a Feinberg Graduate Fellow in Applied Mathematics at the Weizmann Institute of Science, Israel. His research spans Artificial Intelligence, Machine Learning, Large Language Models, and Dynamical Systems, focusing on developing ethical and practical frameworks for intelligence testing, data-centric AI systems, and algorithms for anomaly detection, biomedical text mining, and time-series analysis. He has published extensively in reputed journals including PNAS, Physical Review Letters, IEEE Transactions, and AI & Society, and is the author of the book Plasma and Plasmonics (Ane Books / De Gruyter). Recipient of the INAE Young Engineer Award, he has delivered invited talks at leading global institutions such as Imperial College London, University of Warwick, and the Weizmann Institute of Science.
The IIT Advantage
Certification From IITM Pravartak
Receive a Certificate of completion from IITM Pravartak,
recognizing your achievement.
in Just 7 Months
How You Go From Learning To Orchestrating Enterprise Multi-Agent Systems
Module 1: Essential AI Literacy
Module 1: Essential AI Literacy
Exploring Deep Learning
• What is Deep Learning and how it differs from traditional ML
• Key components – neurons, layers, weights, biases, propagation and its types
• Types of Neural Networks (Feedforward, ANN, CNN, RNN overview)
• Use cases of DL in AI Agents & NLP
NLP for AI Agents
• Why NLP matters in intelligent agents
• Tokenization, stemming, lemmatization
• Embeddings and word vectors (BoW, TF-IDF, Word2Vec, Word Embeddings and more)
• Connecting DL models with text understanding
Word Embeddings and Semantic Understanding
• Limitations of TF-IDF and BoW
• Concept of distributed representations
• Word2Vec: skip-gram and CBOW models
• Word Embeddings and contextual embeddings overview
Module 2: Large Language Models & Their Applications
Module 2: Large Language Models & Their Applications
Introduction to Large Language Models (LLMs)
• Evolution of LLMs — from rule-based NLP to transformer-based architectures
• What makes an LLM “large”: scale, parameters, data
• Overview of popular LLMs: GPT, BERT, T5, LLaMA
• Comparison: rule-based vs statistical vs deep learning vs LLMs• Understanding generative models: GANs and VAEs
• Generator vs Discriminator networks
• Applications in text, image, and data synthesis
Transformer Architecture & Attention Mechanisms
• Deep dive into encoder, decoder, and attention
• Self-attention explained with visualization
• Positional encoding, tokenization, embeddings
• Practical implications for language understanding and generation
Pre-training and Fine-tuning LLMs
• Concept of pre-training vs fine-tuning
• Masked language modeling and next-token prediction
• Transfer learning with domain data
• Fine-tuning small models (DistilBERT, T5-small)
Building with LLMs: Real-World Applications
• Using OpenAI GPT models and APIs for text generation
• Designing a chatbot using GPT-3/4 or open LLMs
• Prompt engineering basics for task-specific responses
• Ethical considerations, hallucinations, and bias mitigation
Module 3: Transitioning from AI Models to AI Agents
Module 3: Transitioning from AI Models to AI Agents
Understanding AI Agents and Their Evolution
• What are AI agents and how they differ from AI models
• Historical progression — from static models to interactive agents
• Components of an AI agent: perception, reasoning, action, and memory
• Real-world examples: customer support bots, recommendation agents
Integrating Neural Networks and LLMs into Agents
• Role of neural networks in decision pipelines
• Using LLMs for perception, conversation, and reasoning
• Combining structured data (NNs) and unstructured text (LLMs)
• Prompt-driven behavior and state maintenance
Contextual Decision-Making and Memory in Agents
• Importance of context in autonomous decision-making
• Techniques for persistent memory and reasoning chains
• Conversation history and retrieval-based context
• Planning and goal-oriented agent behavior
Designing and Deploying Multi-Agent Systems
• Multi-agent communication and collaboration
• Role assignment and message passing
• Decision-making with multiple specialized agents
• Deployment considerations and ethics
Module 4: Systems Thinking for Multi‑Agent Workflows
Module 4: Systems Thinking for Multi‑Agent Workflows
Holistic Systems Thinking for Multi-Agent Environments
• Introduction to systems thinking in AI contexts
• Understanding interconnected subsystems and feedback loops
• Identifying reinforcing and balancing feedback in AI ecosystems
• Mapping dependencies across agents, data flows, and decisions
• Recognizing emergent behaviors and unintended consequences
• Balancing optimization trade-offs in complex systems
Module 5: Human‑First Prototyping & Service Blueprints
Module 5: Human‑First Prototyping & Service Blueprints
Problem Immersion & Journey Mapping
• Introduction to human-first design and its relevance in AI systems
• Understanding user context and problem ecosystems
• Mapping the As-Is journey — identifying current pain points and inefficiencies
• Designing the To-Be journey — envisioning desired outcomes and improved flows
• Leverage point discovery — identifying high-impact intervention spots
Defining KPIs, Guardrails & Service Blueprinting
• Translating user journeys into measurable KPIs
• Defining ethical and operational guardrails for agent workflows
• Building a Service Blueprint linking user touchpoints, frontstage/backstage processes, and AI agent interactions
• Aligning AI capabilities with human-centric outcomes
Module 6: Agent Collaboration Topologies
Module 6: Agent Collaboration Topologies
Understanding Collaboration Topologies in Multi-Agent Systems
• Overview of collaborative structures in MAS (Hierarchical, Manager-Worker, Peer-to-Peer, Blackboard, Contract-Net, Debate/Hybrid Human-Agent)
• How communication protocols and roles influence efficiency and scalability
• Real-world examples of each topology (e.g., search and rescue agents, customer support agents)
• Decision criteria for selecting a collaboration model based on workflow requirements
Design and Evaluation of Agent Topologies
• Comparing topologies on metrics: communication overhead, fault tolerance, decision latency & scalability
• Exploring hybrid and human-in-the-loop approaches (Debate and Collaborative Agents)
• Ethical and control considerations in multi-agent coordination
• Evaluating performance trade-offs for different topologies
Module 7: Contextual Reasoning for Multi-Agent Systems
Module 7: Contextual Reasoning for Multi-Agent Systems
Understanding Context in AI Workflows
• What is context and why it matters in intelligent systems
• Static vs dynamic context in AI decision-making
• Context types: environmental, conversational, user, and task-based
• Real-world examples of context-aware systems (e.g., personal assistants, adaptive chatbots)
• Introduction to context representation in MAS
Passing Context Between Agents
• Context propagation models and shared memory spaces
• Inter-agent communication and message-passing mechanisms
• Challenges in maintaining coherence across agents
• Synchronization and state management strategies
Context Engineering for Task Completion
• Designing inputs and outputs that preserve context
• Schema alignment for structured information sharing
• Contextual chaining and RAG (Retrieval-Augmented Generation) principles
• Using vector stores (FAISS, Pinecone, Chroma) for memory-based retrieval
Designing Context-Aware Multi-Agent Workflows
• Integrating context management in multi-agent architectures
• Memory-enhanced agents: retrieval, summarization, and adaptive context
• Case study: Building a workflow where agents collaborate using shared context
• Debugging, evaluation metrics, and scaling considerations
Module 8: Multi-Agent Planning & Workflow Design
Module 8: Multi-Agent Planning & Workflow Design
Introduction to Multi-Agent System (MAS) Architecture
• Overview of MAS design principles and key components
• Understanding agent types: reactive, deliberative, hybrid
• Architecture layers — communication, reasoning, execution
• Identifying suitable use cases for MAS (e.g., research, automation, support systems)
• Demo walkthrough of CrewAI / AutoGen multi-agent setup
Agent Coordination and Task Delegation
• Agent roles: planner, executor, critic, verifier, etc.
• Task distribution strategies (sequential, parallel, hierarchical)
• Role-based collaboration patterns and dependencies
• Handling dynamic goal reassignment and error recovery
Message Passing and Context Sharing Between Agents
• Agent-to-agent communication channels (text-based, API calls, shared memory)
• Ensuring consistent context across agents
• Managing concurrent communications and synchronization
• Incorporating RAG components for shared contextual memory
End-to-End Workflow Design and Evaluation
• Building a coordinated multi-agent workflow (Planner + Executor + Evaluator)
• Testing coordination and context transfer across steps
• Debugging and evaluating communication latency & task accuracy
• Real-world applications: content pipelines, research assistants, automation agents
Module 9: Workflow Design & Optimization
Module 9: Workflow Design & Optimization
Designing Agent Workflows
• Introduction to workflow design in multi-agent systems
• Workflow design templates: linear, branching, parallel, and feedback-based
• Task decomposition — breaking complex goals into sub-tasks for specialized agents• Mapping interactions and dependencies between agents
• Integrating business process logic into AI agent systems (e.g., e-commerce, logistics)
Workflow Optimization for Scalability & Efficiency
• Identifying performance bottlenecks and communication overhead
• Optimization metrics — throughput, latency, resource utilization
• Leveraging LangChain Memory and LCEL (LangChain Expression Language) for efficiency
• Design-first principles for scalable agent orchestration
• Monitoring and improving workflow performance
Module 10: Prototyping Multi-Agent Systems
Module 10: Prototyping Multi-Agent Systems
Design-First Approach to Multi-Agent Prototyping
• Understanding design-first thinking in AI system development
• Translating conceptual workflows into technical blueprints
• Defining system goals, agent roles, and interaction models
• Selecting the right framework (LangChain, CrewAI, AutoGen, or Flowise) based on use case
Integrating Agent Workflow Design into Prototyping
• Mapping designed workflows into executable prototypes
• Defining communication protocols and message routes
• Using Flowise for visual orchestration and LangChain for logic
• Linking sub-agents (retriever, planner, executor, verifier) within a pipeline
Simulating Real-World Applications with Multi-Agent Systems
• Creating real-world context (customer queries, datasets, product data, etc.)
• Running the prototype end-to-end to simulate user-agent interactions
• Capturing logs, context flow, and decision outcomes
• Debugging agent behaviors and message passing
Validation, Iteration & Improvement
• Evaluating prototype performance (accuracy, latency, consistency)
• Feedback loops — how to iterate based on testing outcomes
• Scaling from prototype to production-ready systems
• Ethics, reliability, and user experience considerations in MAS design
Module 11: Real-World Tools for Agentic Workflows
Module 11: Real-World Tools for Agentic Workflows
Integrating Real-World Tools and External Systems
• Introduction to real-world integrations in AI agent workflows
• Using APIs and data connectors to extend agent functionality
• Leveraging memory systems (short-term vs long-term) for context continuity
• Hands-on: connecting agents to live data sources like CRMs, product catalogs, or databases
• Understanding API rate limits, authentication, and data security
Scaling and Optimizing Real-Time Agentic Workflows
• Designing scalable, event-driven agentic workflows
• Incorporating advanced vector stores (FAISS, Pinecone, Chroma) for retrieval
• Real-time optimization techniques for latency, reliability, and throughput
• Using orchestration tools (Flowise) to visualize and debug agent interactions
• Testing end-to-end performance in simulated real-world use cases
Module 12: Contextual Input & Output Management
Module 12: Contextual Input & Output Management
Dynamic Input Handling and Context Injection Basics
• Understanding dynamic input variations in agent workflows
• Context injection fundamentals — why adaptive context matters
• Role-based prompting and modular context design
• Managing evolving prompts in multi-turn conversations
Role-Based Prompts and Adaptive Output Management
• Designing role-specific prompts for different agents (planner, critic, executor)
• Multi-agent communication with adaptive inputs/outputs
• Simulating dynamic environments — handling incomplete or changing data
• Testing robustness of contextual reasoning
Module 13: Transforming Business Processes with Multi-Agent Systems
Module 13: Transforming Business Processes with Multi-Agent Systems
Introduction to Business Process Automation via Multi-Agent Systems
• Identifying business workflows suitable for multi-agent automation
• Use cases: customer service, lead generation, HR automation, supply chain
• Mapping business rules into agent logic
• Aligning KPIs and ROI with AI agent deployment
Automation and Orchestration Design
• Orchestrating agents for end-to-end process automation
• Workflow decomposition and communication protocols
• Integrating human-in-the-loop checkpoints
• Using Flowise or LangGraph for visualization
Coordination, Context Passing, and Scaling
• Coordination challenges in enterprise-scale systems
• Context passing in long workflows with diverse agents
• Scaling across data sources, departments, and time-sensitive operations
• Monitoring and failure recovery mechanisms
Business Use Case Deployment and Validation
• End-to-end project: design → build → validate
• Testing automation performance, latency, and cost-efficiency
• Ensuring smooth collaboration across agents and tools
• Presenting business impact metrics and results
Module 14: Monitoring & Managing Agent Systems
Module 14: Monitoring & Managing Agent Systems
Real-Time Monitoring and Management of Multi-Agent Systems
• Understanding why real-time monitoring is critical for MAS
• Logging frameworks and observability in LangChain-based systems
• Tracking communication latency, error rates, and decision flow
• Debugging agent communication breakdowns and workflow errors
• Interpreting logs for performance refinement
Module 15: Enterprise Integration with Multi-Agent Workflows
Module 15: Enterprise Integration with Multi-Agent Workflows
Integrating Multi-Agent Systems into Enterprise Workflows
• Understanding enterprise workflow architecture (ERP, CRM, SCM)
• Identifying integration points for AI agents
• Designing data and command interfaces between MAS and enterprise systems
• Security, authentication, and API access management
Building Real-Time Integrations
• Real-time event-driven task handling in enterprises
• Agent orchestration for live data exchange and decision triggers
• Managing concurrency and handoff mechanisms
• Error handling and logging in enterprise-grade agents
Scaling Multi-Agent Workflows Across Enterprise Systems
• Scaling strategies — modular agent clusters, microservices pattern
• Cross-department coordination and centralized orchestration
• Caching and rate-limiting for API-heavy workflows
• Version control and CI/CD for multi-agent pipelines
Validation and Performance Optimization
• Measuring business impact and system performance
• Creating enterprise dashboards for monitoring agent operations
• Ethical, compliance, and governance aspects in enterprise MAS deployment
Module 16: Advanced Context Engineering
Module 16: Advanced Context Engineering
Deep Dive into Context Engineering Concepts - Revisiting foundational context management principles and applications at an Enterprise level through Case Studies
Adaptive Context Switching in Large-Scale Multi-Agent Systems
• Designing adaptive context switching logic
• Handling concurrent context updates across multiple agents
• Synchronizing memory systems and vector databases
• Evaluating performance impact of dynamic context changes
Participants will work on 10+ diverse hands-on activities with AI Tools
Build a multi-agent telecom incident ticket analyzer using CrewAI and LangGraph.
Design a customer complaint resolution assistant with sentiment analysis and response generation.
Develop a telecom outage RCA assistant capable of analyzing synthetic network logs and generating troubleshooting recommendations.
Build an AI-powered telecom knowledge assistant using RAG over synthetic SOP and troubleshooting documents.
Create a healthcare clinical report summarization assistant for analyzing synthetic patient reports and lab documents.
Design a medical research paper analysis assistant with document summarization and question-answering workflows.
AI Clinic
Through the Futurense AI Clinic, you’ll gain hands-on exposure to real enterprise-grade AI projects, from ideation to deployment.
Work on Live Projects
Solve real business challenges using GenAI, Agentic AI, and automation workflows.
Use Industry-Standard Tools
Hands-on with LangChain, CrewAI, Flowise, OpenAI APIs, Zapier, HubSpot, and more.
Collaborate with Experts
Guided by IIT faculty and industry mentors through each phase of your project.
End-to-End AI Execution
Build, test, and deploy intelligent workflows that simulate real-world problem solving.
While mastering 20+ Tools
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By The End, You’ll Be Able To Do All This
This Program is for
Educational Qualification
Bachelor’s degree (minimum 3 years) in Computer Science, IT, Engineering, Mathematics, or a related field. MCA / M.Sc / M.Tech candidates with exposure to technology, analytics, or systems are also eligible
Work Experience
Minimum 1 year of experience in product management, software development, enterprise/solution architecture, data science, ML engineering, automation & process excellence, or consulting.
Freshers
Exceptional fresh graduates with strong programming skills and aptitude for AI workflows may be considered via screening.
Prior Knowledge
Familiarity with programming (Python preferred), AI concepts, APIs, and data-driven systems.
Selection Process
Clear a pre-screen exam testing programming fundamentals, logic, and workflow/AI readiness.
Selection Process
Clear a pre-screen exam testing programming fundamentals, logic, and workflow/AI readiness.
Qualifying Test
What You’ll Be Tested On
Duration: 60 minutes
Important Guidelines
Each section has its own pass criteria: you must score at least 40% in each area.
There is no sectional time limit; you may answer sections in any order.
Show all workings for coding or analytical questions where applicable.
No prior experience needed.
Software Engineers & Developers
looking to transition into agentic development.
Solution Architects & Enterprise Architects
designing AI-enabled business systems.
Data Scientists, ML Engineers & AI Developers
moving from prototypes to production-ready workflows.
Automation & Process Excellence Professionals
and Service Designers re-engineering processes with multi-agent systems.
Roles Thatʼll Be Looking for You
Role Now
AI Product Analyst
Salary
₹8-12 LPA
Role Upgraded
AI Workflow Architect
Earning Potential
₹18-28 LPA
Role Now
Junior Workflow Analyst
Salary
₹7-10 LPA
Role Upgraded
Senior Workflow Analyst
Earning Potential
₹20-30 LPA
Role Now
AI Agent Developer (Junior)
Salary
₹8-12 LPA
Role Upgraded
AgentOps Engineer
Earning Potential
₹22-32 LPA
Role Now
Associate Engineer
Salary
₹8-12 LPA
Role Upgraded
Multi-Agent Workflow Engineer
Earning Potential
₹20-30 LPA
Role Now
Context Engineering Associate
Salary
₹7-11 LPA
Role Upgraded
Context Engineering Specialist
Earning Potential
₹18-28 LPA
Role Now
Automation & AI Ops Associate
Salary
₹8-12 LPA
Role Upgraded
Automation Strategist (AI/Agentic)
Earning Potential
₹20-30 LPA
Role Now
Process Ops Analyst
Salary
₹6-10 LPA
Role Upgraded
Multi-Agent Automation Specialist
Earning Potential
₹18-30 LPA
Role Now
Prompting Intern/Associate
Salary
₹4-8 LPA
Role Upgraded
Contextual Reasoning Engineer
Earning Potential
₹18-28 LPA
Role Now
Solution Analyst
Salary
₹7-12 LPA
Role Upgraded
Enterprise AI Integration Engineer
Earning Potential
₹20-30 LPA
Role Now
API Integration Associate
Salary
₹6-10 LPA
Role Upgraded
AI Systems Integration Specialist
Earning Potential
₹18-28 LPA
Role Now
Business Process Analyst
Salary
₹7-12 LPA
Role Upgraded
MAS Orchestration Engineer
Earning Potential
₹20-40 LPA
Role Now
CRM/ERP Support Engineer
Salary
₹6-11 LPA
Role Upgraded
Multi-Agent Enterprise Workflow Engineer
Earning Potential
₹20-30 LPA
Role Now
AI Agent Developer
Salary
₹8-12 LPA
Role Upgraded
LangChain Developer
Earning Potential
₹15-30 LPA
Role Now
Workflow Automation Engineer
Salary
₹7-12 LPA
Role Upgraded
AI Agent Orchestration Specialist
Earning Potential
₹20-35 LPA
Role Now
Junior MAS Developer
Salary
₹7-11 LPA
Role Upgraded
MAS Orchestration Engineer
Earning Potential
₹20-40 LPA
Role Now
LLM Ops Associate
Salary
₹7-12 LPA
Role Upgraded
AgentOps Engineer
Earning Potential
₹22-32 LPA
Role Now
Compliance Analyst
Salary
₹6-10 LPA
Role Upgraded
AI Governance Architect
Earning Potential
₹18-28 LPA
Role Now
Model Testing Associate
Salary
₹6-10 LPA
Role Upgraded
Agent Monitoring & Safety Engineer
Earning Potential
₹18-30 LPA
Role Now
Customer Ops Analyst
Salary
₹5-9 LPA
Role Upgraded
Human-in-the-Loop Workflow Supervisor
Earning Potential
₹15-25 LPA
Role Now
Ops Coordinator
Salary
₹5-8 LPA
Role Upgraded
AI Workflow Governance Specialist
Earning Potential
₹15-25 LPA
Career Assistance By Futurense
Profile, Narrative & Resume Building
Craft a recruiter-ready identity with optimized resumes, LinkedIn profiles, and a strong career narrative.
Career-Specific Training
Develop job-ready skills with role-focused training, capability tests, AI tools workshops, and continuous upskilling to match real hiring expectations.
Futurense Job Board - Exclusive Opportunities
Access curated, pre-vetted roles before they hit public portals, with priority visibility for Futurense learners.
Interview Playbooks & Cheat Sheets
Get insider interview guidance with structured playbooks: FAQs, sample answers, frameworks, recruiter insights, and round-wise preparation.
Mock Interviews with Experts
Experience real interview simulations with personalized feedback from mentors, industry leaders, and FLC members.
Mentor Referrals & Networking
Unlock referral advantages, insider recommendations, alumni-driven opportunities, and FLC mentorship that accelerates your career entry.
Salary Negotiation Support
Get guidance on positioning, benchmarking, negotiation strategy, and communication to secure the compensation you deserve.
Words From Our Students
Our students are acing it!
Disclaimer: All products and company names are trademarks or registered trademarks of their respective holders. Use of product and company names does not imply any affilation with or endorsement by the holders.
They are working at companies which are a dream for most
Fee Structure
Total Admission Fee
₹1,25,000
+ 18% GST
Program fee is subject to change from the next cohort.
A non-refundable application deposit of ₹5,000 will be adjusted in the total fee. Program fees are refundable only if the student withdraws before the start date. No refunds will be made after the program begins.
An additional ₹10,000 will be applicable if you opt for the Campus Immersion (optional).
Deserving candidates opting for the upfront payment plan may be eligible for a scholarship of up to ₹9,000.
Application Deadline
5th July, 2026
Admissions close once the required number of students is enrolled for the upcoming cohort. Apply early to secure your seat.
How it Works
Application Process
1
Submit Your Application
Fill in your details and share your interest in joining the program.
2
Clear the Qualifying Test
A short test designed to assess your programming fundamentals and AI/workflow readiness
3
Pay and Confirm Your Seat
Secure your spot in the upcoming cohort with flexible payment options
Traditional AI Agent vs. IITM Pravartak: AI Agent Workflows and Agentic Systems Development
Dimension
This Programme
Other Programme
Core Philosophy
Workflow-first, designing systems where agents collaborate and deliver outcomes
Tool-first, focused on building individual agents
Curriculum Style
End-to-end orchestration of workflows, with modules on context, governance, and enterprise integration
Teaches how an agent works, often limited to frameworks and demos
Tool Exposure
Hands-on with 30+ enterprise tools with LangChain, Zapier, Relevance AI, GPT-4o tools, Claude, and many others, all applied within workflows
Covers isolated tools or frameworks (LangChain, AutoGPT, etc.)
Multi-Agent Coverage
Covers manager–worker, peer-to-peer, and hybrid orchestration patterns for real-world enterprise use
Often ignores collaboration models; focuses on single-agent demos
Product Architecture
Human-in-the-loop protocols, trust, safety, and continuous feedback loops embedded from the start
Rarely emphasized
Deployment Focus
Enterprise-grade workflows, APIs, databases, CRMs, and ERPs integrated for production readiness
Focus on PoCs and sandbox demos
Mentor Access
Direct mentorship from IIT faculty and Futurense Leadership Council with enterprise project reviews
Limited peer or tool-community guidance
Capstone Format
Real enterprise-ready agentic workflow project, reviewed by industry mentors and IITM faculty
Mock case studies or single-agent prototypes
Learning Outcome
Architect, orchestrate, and deploy scalable multi-agent systems with measurable business impact
Understand how to build a basic agent
Mindset Trained
Workflow-first AI leader who designs for scale, trust, and enterprise adoption
Tool-user or experimenter
Feeling Underconfident About Your Tech Skills?
Kick things off with a 2-Week Bridge Course that gets you course-ready
What you'll learn:
Maths Refresher
Programming Foundations
Data Manipulation & Representation
Cloud Foundations
Machine Learning Essentials
Worth ₹29,000
Included free with your enrollment.
Led by the Futurense Leadership Council (FLC)
A collective of CXOs, AI leaders, and digital transformation heads from global and Fortune 500 companies shaping the AI-native workforce.
Ready to Redefine Agentic Innovation in the AI Era?
Advance your career with IITM Pravartak's Advanced Certificate in AI Agent Workflows. Learn to design intelligent systems and multi-agent workflows with industry experts.
Frequently Asked Questions
We know you might have some questions before getting started in our platform
What is the name of the certificate program?
The program is called Advanced Engineering Program in AI Agent Workflows and Agentic Systems Development, offered by IITM Pravartak Centre of Excellence in collaboration with Futurense Technologies.
What is the duration of the program, and when does the first cohort begin?
The program duration is approximately 7 months (140+ hours), with the first cohort scheduled to begin as per the announced program start date.
What are the eligibility criteria for this program, and who should ideally enroll?
• Engineers interested in AI systems and automation
• Software professionals looking to build expertise in AI agents
• Data professionals working with AI and machine learning
• Product managers exploring AI-driven product development
• AI enthusiasts interested in agentic workflows and intelligent systems
How can I apply to the IITM Pravartak AI Agents and Agentic Workflows program?
Applicants can apply through the official Futurense admissions portal by submitting the application form along with the required documents.
When will the application process start?
The application process is currently open, and interested candidates can apply online as seats are limited.
Why is this program considered unique compared to other AI certificate programs?
The program focuses on AI agents, autonomous systems, and real-world agentic workflows, combining technical AI concepts with practical product and workflow implementation.
Is there a selection process for admission?
Yes. Admission typically requires meeting eligibility criteria and clearing a pre-screening or evaluation process.
What documents are required for application?
Applicants must submit identity proof, educational certificates, resume, and relevant professional documents as part of the application process.
Will there be any pre-screening exam for enrollment?
Yes, applicants may be required to complete a pre-screening assessment as part of the admission process.
Is coding experience required to qualify for the pre-screening exam?
Coding experience is helpful but not mandatory, as the program focuses on understanding AI systems and workflows along with practical implementation.
What documents should I keep ready during the application process?
Applicants should keep PAN, Aadhaar, educational documents, resume, and financial documents if applying for loan support.
What makes this certificate program particularly suitable for engineers and tech professionals?
The program combines AI engineering concepts with real-world agentic workflow implementation, helping professionals build skills required for next-generation AI-powered systems.
How is the teaching format structured for this hybrid program?
The program follows a hybrid format with live online sessions, hands-on learning, and a campus immersion experience.
What if I cannot take leave for campus immersion?
The campus immersion is scheduled toward the end of the program, allowing participants to plan in advance.
Will hostel accommodation be provided for outstation participants during immersion?
Yes, accommodation may be arranged for outstation participants, subject to availability.
Who is the program director, and why is their expertise important?
The program director provides academic leadership and ensures the curriculum remains aligned with the latest advancements in AI and agentic systems.
What makes IITM Pravartak a leader in technological education, particularly in AI and data science?
IITM Pravartak is associated with IIT Madras and focuses on cutting-edge research, AI innovation, and industry collaboration.
How do industry leaders and the Futurense Leadership Council (FLC) contribute to the program?
Industry experts conduct live sessions, masterclasses, mentorship programs, and real-world case discussions, ensuring strong industry relevance.
What areas of AI and agentic workflows does the program cover?
The program covers AI agents, generative AI, autonomous systems, agent orchestration, and real-world AI workflow implementation.
What tools and platforms are included in the program?
Participants learn tools such as ChatGPT, Claude, Gemini, LangChain, AI development frameworks, and product development tools.
Why are AI agents considered the next big evolution in AI technology?
AI agents enable autonomous decision-making, task execution, and intelligent workflow automation, making them essential for scalable AI systems.
What are the key modules in the program related to AI agents and workflows?
Modules include AI foundations, generative AI applications, agentic systems design, multi-agent collaboration, workflow automation, and AI-powered product development.
How does the program allow participants to specialize in specific domains?
Participants can apply AI agent concepts to domains such as fintech, SaaS, healthcare, e-commerce, and enterprise automation.
What practical projects or hands-on outcomes are included in the program?
Hands-on work includes building AI agents, designing automated workflows, creating AI-driven prototypes, and developing AI-powered MVPs.
What is the significance of learning multi-agent systems and agent collaboration?
Multi-agent systems allow multiple AI agents to collaborate, automate complex workflows, and improve decision-making efficiency.
What is the difference between traditional AI, generative AI, and agentic AI?
Traditional AI focuses on predictive models, generative AI creates new content, and agentic AI enables autonomous systems capable of planning and executing tasks.
What job roles can participants pursue after completing this program?
Participants can pursue roles such as AI Engineer, AI Agent Developer, GenAI Specialist, AI Product Manager, Automation Architect, and AI Solutions Consultant.
How do skills in generative AI and agentic AI impact salary and job opportunities?
Professionals with these skills often experience higher salary growth and increased demand across AI-driven companies.
How does this program align with the growing adoption of AI agents in companies?
The program prepares professionals to design and deploy AI agents for real-world enterprise workflows and product systems.
How will this program help future-proof careers in a rapidly evolving AI market?
It equips professionals with skills in AI systems design, automation, and agentic architectures, which are rapidly becoming industry standards.
How are agentic AI systems used across industries such as healthcare, manufacturing, and telecom?
Examples include AI healthcare assistants, intelligent manufacturing automation, and AI-powered customer support systems in telecom.
What is the projected market value of generative AI and enterprise adoption trends?
The generative AI market is projected to reach over $1 trillion in value by 2032 , with a large percentage of enterprises prioritizing adoption.
How does India rank globally in AI startups and innovation?
India is among the top global hubs for AI startups, with rapid growth in generative AI and AI agent development.
What is the program fee, and what financing options are available?
The program fee is announced during admissions and includes flexible EMI and loan options through financial partners.
Is there an additional cost for attending the campus immersion?
Yes, there may be an additional immersion fee of ₹10,000, which covers participation and campus-related expenses.
What is the payment schedule and process?
Participants must pay an application deposit first, followed by the remaining program fee within the specified timeline after receiving the offer letter.
Can I self-fund the program?
Yes, candidates can self-fund the program either fully or partially.
Does Futurense offer loan support?
Yes, Futurense partners with financial institutions to provide loan and EMI options.
What are the interest rates on education loans?
Interest rates depend on the financial partner and repayment plan, and are generally competitive.
What is the program's fee, and what financing options are available?
The program fee is ₹1,10,500 + 18% GSTand possible scholarships for deserving candidates.