4.9 (102)
6 - 7 Weeks
·Cohort-based Course
Learn to make decisions tailored to business constraints, understand when & how to apply AI effectively & build a multi-agent application
This course is popular
36 people enrolled last week.
4.9 (102)
6 - 7 Weeks
·Cohort-based Course
Learn to make decisions tailored to business constraints, understand when & how to apply AI effectively & build a multi-agent application
This course is popular
36 people enrolled last week.
Worked/Taught at
Course overview
Enrollment Status (updated weekly, as of June 23rd):
- July Cohort: 65% full
- October Cohort: 15% full
October is our final cohort for the year. It includes an additional week of content, and the price will increase to $2,500 starting 1st August 2025, to reflect the expanded curriculum, meet rising demand, and maintain our current student-to-instructor ratio.
🚨🚨Note: This course is an independent offering and is not affiliated with, endorsed by, or related to the instructors' current or past employers.
⛳ The only prerequisite: you should have coded at least once in your life. The course includes low-code assignments, and even folks who hadn’t touched code in over 15 years have found it approachable and rewarding. That said, a basic understanding of coding really helps you get the most out of it — and of course, there’s AI to assist you along the way. The course is built for everyone, whether you’re a Product Manager, Architect, Director, C-suite leader, or someone seriously exploring agentic AI.
Agentic AI or AI systems capable of operating with some degree of autonomy, is transforming how we interact with technology. In the coming years, most software systems will integrate AI agents to enhance their capabilities. This shift will drive a growing demand for professionals who can move beyond surface-level understanding and apply AI effectively to solve real business challenges while navigating practical constraints.
This course focuses on practical AI agent development, covering key agentic design and usage paradigms. Instead of just explaining what these techniques are, we focus on when and how to use them, so you're equipped to make informed, business-driven AI decisions.
What You'll Learn
All core content is pre-recorded so students can focus on two-way interaction. Lectures are watched asynchronously, and we host four office hours each week for questions and brainstorming
Week 1 (Let’s get you to understand what problem-first means)
Decode why agentic AI breaks traditional software assumptions
Frame hallucinations, latency, and prompt brittleness through the determinism spectrum
Open vs. closed models: tradeoffs across compliance, latency, and cost
Problem-first, evaluation-driven design using early datasets and proxy metrics
Deconstruct a production-grade use case and redesign it across progressive system versions
Week 2 (Prompt engineering is still the core part of agents, but do it smarter with right evals)
Break down the evolution from zero-shot prompts to self-optimizing models
Master prompting: Decomposition, meta-prompts, algorithmic optimization
Analyze when to use prompting-only systems based on task, cost, and latency
Compare model-level strategies: reasoning vs. regular, and when each makes sense
Add guardrails and evaluation layers using LLM judges, semantic scoring, and offline tests
Week 3 (RAG is not dead, it’s in fact the basis of self-improving agents)
Address statelessness via dynamic retrieval and memory-backed context injection
Build robust RAG pipelines with advanced chunking, embedding selection, and retrieval methods
Explore GraphRAG, Agentic RAG and multimodal RAG and other advanced methods and learn tradeoffs
Architect episodic, semantic, procedural, and working memory layers for self-reflective agent behavior
Week 4 (MCP from an enterprise lens and multi-agents + Fine-Tuning)
Understand planning autonomy in agents and how dynamic tool use and multi-turn reasoning go beyond static workflows
Compare agent levels and their control dimensions: action, planning, evolution, and physical autonomy
Explore MCP (Model Context Protocol) and A2A as emerging agent-tool communication standards
Investigate critical security challenges in MCP and A2A. Understand how guardrails, tool signing, audit trails improve reliability
Analyze coordination patterns in multi-agent systems, including shared memory governance, state sync, AI collusion risks, evaluation, logging, and observability
Explore fine-tuning levers (SFT, RLHF, PEFT etc.), compare with RAG, and determine when to shift from context injection to model adaptation
Week 5–6 (Put it all together in a capstone)
Work in groups of 6
Take a business problem and design/implement a solution
Demo to 4000+ public attendees including leaders, VCs, and hiring managers
Homeworks: You'll supplement your learning by building an agentic search system (Perplexity like) in 3 iterations with the final iteration using agentic RAG, MCP and multi-agents. You can choose between low-code/code routes to complete assignments.
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❌ Who This Course Is Not For
For Those Who Have Already Deployed Gen AI in Enterprise: This course is designed as an applied foundations course for enterprise AI with only basic Python as a prerequisite and no ML background required. If you’re already familiar with deploying AI systems, you won’t gain much from the core content. However, if you're looking to network and refine best practices, you're welcome to join.
Those Seeking Heavy Theoretical Knowledge: This course emphasizes applied learning and practical problem-solving, not deep dives into theoretical topics like transformer architecture, pre/post-training optimization, inference techniques, or alignment.
Those Who Have Never Coded Before: While we provide low-code options, this course assumes you have some coding experience. It’s not suitable for those who have never written or worked with even basic code.
Individuals Expecting Deep AI Research Focus: While we’ll cover cutting-edge techniques, this course is centered on applying AI to business problems, not research-heavy exploration.
Scaling and Ops Enthusiasts: This course does not focus heavily on scaling or operational aspects (i.e., LLMOps). Deployment will be covered at a high level, but not in-depth.
01
Software/AI Engineers, Strategists, Data Professionals, Solution Architects and Consultants who want to master AI system design
02
Business Leaders and Product Managers seeking to gain the technical understanding needed to make informed decisions & lead AI initiatives
03
Entrepreneurs looking to understand common generative AI use cases and learn how to develop and implement AI-powered solutions
Solving Real Enterprise Challenges, Not Just Concepts
While most courses stop at teaching tools and frameworks, this course goes further by focusing on solving real-world business problems. You’ll tackle practical constraints like cost, scalability, latency, and performance, learning to design AI solutions tailored to real use cases
Apply Concepts to Build an Agentic Search System
While learning applied AI concepts, we’ll put them into action by building a Perplexity-like AI-powered search system through detailed, hands-on tutorials that demonstrate their practical application (Low code options will be provided)
Capstone Project
Learn how to connect cutting-edge research with real-world applications. For the capstone, you’ll use our curated list of the latest research papers to design and implement solutions for practical business use cases. Some of our capstones have received VC funding too. Examples
Understand Challenges and Effective Evaluation
Gain a deep understanding of key challenges in building AI systems, including handling hallucinations, adversarial attacks, security, privacy issues etc., and learn best practices to evaluate AI solutions comprehensively
Access to the Problem-First AI Community
The course includes guest lectures from industry experts, AMA sessions, and our Chai & AI discussions, culminating in a final in-person meetup in the Bay Area. You'll have plenty of opportunities to network and become part of our community.
Live sessions
Learn directly from Aishwarya Naresh Reganti & Kiriti Badam in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
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4.9 (102 ratings)
Nadia V Gill
Karla Congson
Rick Somra
Govind Manoharan
Ravi Nukala
Milli Comstock
Tech Lead | Lecturer | Advisor | Researcher | Speaker | Investor
Aishwarya Naresh Reganti is an Applied Science Tech Lead and leads initiatives to develop and deploy production-ready generative AI solutions enterprise clients. With over 9 years of experience in machine learning, she has published more than 35 research papers at top-tier AI conferences, including NeurIPS, AAAI, and CVPR.
Aishwarya has taught professional courses on generative AI at renowned institutions like MIT and Oxford. She has also designed free courses that have reached over 8,000 students globally and have formed the foundation for several academic programs and industry training curricula.
Recognized as one of the most prominent voices in enterprise AI, with over 95,000 professionals following her on LinkedIn, she is a sought-after thought leader frequently invited to speak at leading conferences and events, including TEDx, MLOps World, and ReWork.
Aishwarya actively collaborates with leading research professors and provides strategic advisory to organizations, enabling them to harness AI effectively to address complex business challenges.
Member of Technical Staff @ OpenAI | AI Advisor | Ex-Google
Kiriti Badam is a member of the technical staff at OpenAI, with over a decade of experience designing high-impact enterprise AI systems. He specializes in AI-centric infrastructure, with deep expertise in large-scale compute, data engineering, and storage systems.
Prior to OpenAI, Kiriti was a founding engineer at Kumo.ai, a Forbes AI 50 startup, where he led the development of infrastructure that enabled training hundreds of models daily—driving significant ARR growth for enterprise clients.
Kiriti brings a rare blend of startup agility and enterprise-scale depth, having worked at companies like Google, Samsung, Databricks, and Kumo.ai. At Google Ads, he built globally distributed key-value stores that powered ad systems generating tens of billions in annual revenue.
He holds a Master’s degree from Carnegie Mellon University and a Bachelor’s from IIT Madras, where his research focused on advanced storage systems and distributed databases for AI workloads. A sought-after mentor and advisor, Kiriti helps startups and organizations design scalable AI infrastructure, reach product-market fit, and build long-term product strategy.
Join an upcoming cohort
July 2025 Cohort
$1,950
Dates
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Oct 2025 Cohort
$1,950
Dates
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5-8 hours per week
Live Office Hours
4 hours per week
We offer generous office hours to support you throughout the course, along with 24-hour help on Slack. Typical weekly schedule:
Chai & AI Sessions
Sundays 8 am PT
Our weekly community discussion channel covers the latest trends in AI, industry updates, and all the unfiltered tea, open, casual, and freeform.
Guest Lectures
PT evenings
We bring in industry experts to share proven insights and discuss real enterprise challenges
Our Curated Generative AI Free-Resource Bundle
Access our curated collection of top reading materials, roadmaps, research papers, and more!
Get this free resource
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Sign up to be the first to know about course updates.
Join an upcoming cohort
July 2025 Cohort
$1,950
Dates
Payment Deadline
Don't miss out! Enrollment closes in 4 days
Oct 2025 Cohort
$1,950
Dates
Payment Deadline
$1,950
4.9 (102)
4 days left to enroll