Building AI Agents for Enterprise#
Key Details#
Design → Develop → Deploy cycle will be adopted for agent systems
The design will match production systems
We discuss trade-offs in model selection and pipeline choices
You will have lifetime access to the recordings and community
You are expected to know basic coding - no prior AI/ML knowledge is expected
December 2025 Live Classes -#
6 Weeks Course → Starts 6th December
Morning Batch: 8:00 AM to 10:00 AM (IST) on Saturdays and Sundays
Evening Batch: 8:00 PM to 10:00 PM (IST) on Saturdays and Sundays
You will get invite to BOTH batches - you decide on each day which one you want to join.
Price: ₹50,000
Program outline#
Week 1: Foundation
The first week is about learning the foundations of AI agents and understanding the enterprise landscape.
Topics and agenda
Gen AI Fundamentals and Introduction to Agents in Enterprise
Tools Landscape
Prompt Engineering and Optimization
Building workflow agents
Tools & Frameworks
LangChain, Groq, OpenAI, DSPy
Week 2: Retrieval Augmented Generation
This week, we learn everything about advanced retrieval systems and tool engineering for enterprise integration.
Topics and agenda
Retrieval Augmented Generation (RAG)
Advanced RAG and retrieval evaluations
Tools & Frameworks
FAISS / Chroma DB
Week 3: Agent Intelligence
The third week will be about understanding intelligent agent evaluation and reasoning patterns.
Topics and agenda
Tools Engineering
MCP Protocol
Agent Evaluations
Tools & Frameworks
MCP
Lovable
Langgraph
Week 4: Memory & Optimization
This week focuses on building agents with sophisticated memory systems and optimizing for production performance.
Topics and agenda
LLM as a Judge
Agentic RAG
Long and Short Term Memory in Agents
Tools & Frameworks
Langgraph
LangMem
Redis
Week 5: Production Deployment
This week will be about deploying, monitoring, and maintaining AI agents in production enterprise environments.
Topics and agenda
Inference Optimization & Caching Strategies
Observability & Guardrails
Tools & Frameworks
Langsmith, Opik
NVIDIA Nemo
Week 6: Multi-Agent Systems
The final week will be about exploring advanced multi-agent architectures and strategic implementation.
Topics and agenda
Deploying Agents to Azure
Multi Agent Architectures and A2A Protocol
Strategic Choices and Change Management
Tools & Frameworks
Docker
Azure Cloud
A2A Protocol
Instructors#
Abhijith Neerkaje#
Abhijith Neerkaje is a data science leader with over 20 years of experience driving business impact across the retail, semiconductor manufacturing, and energy sectors.
He currently heads the Data Science and Analytics function at Falabella India Pvt Ltd, where he leads a team building machine learning–powered products that enhance search, recommendations, and seller experience for Falabella’s e-commerce platform.
Previously as Senior Director, Data Sciences, at Target, Abhijith played a pivotal role in optimizing pricing, supply chain, and merchandising functions through data-driven decision-making. Known for building and mentoring high-performing teams, his work consistently bridges the gap between advanced analytics and real-world business value.
Abhijith holds a bachelor’s degree in engineering from PESIT, Bangalore, a postgraduate degree in engineering from the Indian Institute of Science (IISc), Bangalore, and a MS in Engineering and Management from the Massachusetts Institute of Technology (MIT), USA. While at MIT Abhijith was a recipient of MIT Tata fellowship and winner of MIT clean energy prize (renewable energy track ) in 2013.
Ajay Shenoy#
Ajay Shenoy has 14 years of experience in academia and industry in the field of machine learning, artificial intelligence, computer vision, and signal processing. He currently works as a consultant and trainer in the AI/ML field. Previously, he has worked in the AI/ML field at Target, Harman, AlphaICs, and Scaler.
Ajay holds a B.Tech from NITK, Surathkal, and Ph. D from IISc, Bangalore. He has published 10 articles in leading journals and conferences such as the IEEE Transactions on Signal Processing.