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Building Agentic AI Applications with a Problem-First Approach

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

Google
University of Oxford
MIT research group
Amazon Web Services
OpenAI

Course overview

Design and build impactful agentic AI systems to solve business problems.

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.

Who is this course for

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

What you’ll get out of this course

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.

What’s included

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.

Course syllabus

23 live sessions • 28 lessons

Week 1

Jul 26—Jul 27

    Introduction, Setup & Our Favorite AI Tools

    6 items

    Week 1 (Let’s get you to understand what problem-first means)

    5 items

    Jul

    26

    Welcome Lecture

    Sat 7/264:00 PM—5:00 PM (UTC)

    Jul

    27

    Live: Chai & AI Session (Not Recorded)

    Sun 7/273:00 PM—4:00 PM (UTC)
    Optional

Week 2

Jul 28—Aug 3

    Jul

    30

    Week 1 Homework Office Hours

    Wed 7/304:00 PM—5:00 PM (UTC)

    Aug

    1

    Week 1 Content Office Hourse

    Fri 8/112:00 AM—1:00 AM (UTC)

    Aug

    2

    Week 1 Content Office Hours

    Sat 8/24:00 PM—5:00 PM (UTC)

    Aug

    2

    Week 1 Homework Office Hours

    Sat 8/25:00 PM—6:00 PM (UTC)

    Week 2 (Systemic Prompt Design, Evaluation Loops & Guardrail Design for Agents)

    5 items

    HW: Build your v1 Agentic Search System Perplexia (Choose code/no-code options)

    0 items

    Aug

    3

    Chai & AI Session (Not Recorded)

    Sun 8/33:00 PM—4:00 PM (UTC)
    Optional

Week 3

Aug 4—Aug 10

    Aug

    6

    Week 2 Homework Office Hours

    Wed 8/64:00 PM—5:00 PM (UTC)

    Aug

    8

    Week 2 Content Office Hours

    Fri 8/812:00 AM—1:00 AM (UTC)

    Aug

    9

    Week 2 Content Office Hours

    Sat 8/94:00 PM—5:00 PM (UTC)

    Aug

    9

    Week 2 Homework Office Hours

    Sat 8/95:00 PM—6:00 PM (UTC)

    Week 3 (RAG is not dead, it’s in fact the basis of self-improving agents)

    5 items

    HW: Build your v2 Agentic Search System Perplexia w/ Agentic RAG

    0 items

    Aug

    10

    Chai & AI Session (Not Recorded)

    Sun 8/103:00 PM—4:00 PM (UTC)
    Optional

Week 4

Aug 11—Aug 17

    Aug

    16

    Week 3 Content Office Hours

    Sat 8/164:00 PM—5:00 PM (UTC)

    Aug

    16

    Week 3 Homework Office Hours

    Sat 8/165:00 PM—6:00 PM (UTC)

    Aug

    13

    Week 3 Homework Office Hours

    Wed 8/134:00 PM—5:00 PM (UTC)

    Aug

    15

    Week 3 Content Office Hours

    Fri 8/1512:00 AM—1:00 AM (UTC)

    Week 4 (MCP from an enterprise lens, multi-agents, fine-tuning)

    6 items

    HW: Build your v3 Agentic Search System w/ MCP + Memory + Multi-Agents

    0 items

    Capstone Project Overview and Deliverables

    1 item

    Aug

    17

    Chai & AI Session (Not Recorded)

    Sun 8/173:00 PM—4:00 PM (UTC)

Week 5

Aug 18—Aug 24

    Aug

    20

    Week 4 Homework Office Hours

    Wed 8/204:00 PM—5:00 PM (UTC)

    Aug

    22

    Week 4 Content Office Hours

    Fri 8/2212:00 AM—1:00 AM (UTC)

    Aug

    23

    Final Lecture (Aish + Kiriti)

    Sat 8/234:00 PM—5:00 PM (UTC)

    Aug

    23

    Week 4 Homework Office Hours

    Sat 8/235:00 PM—6:00 PM (UTC)

    Comprehensive Guide: Decision Framework for Enterprise Agents

    0 items

    Aug

    24

    Capstone Brainstorming Workshop

    Sun 8/244:00 PM—6:00 PM (UTC)
    Optional

Week 6

Aug 25—Aug 30

    Work with Capstone team to design/implement enterprise agentic solution

    0 items

    Aug

    30

    Casptone Demo Day: Public Event (Open to All)

    Sat 8/304:00 PM—7:00 PM (UTC)

Post-course

4.9 (102 ratings)

What students are saying

What makes our course the most holistic Applied AI program out there?

What makes our course the most holistic Applied AI program out there?

What people are saying

        I'm loving the course. I considered myself an AI business expert, yet I'm learning so much and filling gaps I didn’t even realize I had!
Nadia V Gill

Nadia V Gill

Senior Vice President of Strategy (AI), Hitachi Digital
        Hands, down this is an excellent course - both in terms of pacing, coverage of the materials and technical depth. Aish has an extraordinary ability to take complex concepts and deliver it elegantly without oversimplifying. I teach gen AI for marketers and I know how hard this is so i appreciate the work put into this!
Karla Congson

Karla Congson

CEO, Agentiiv.com
        you are doing a great job with the course! Balancing the right level of pacing and content can be challenging, but Aish & Kiriti areable find that line.
Rick Somra

Rick Somra

Senior Software Engineer, Clear Aspect Solutions
        I can see the amount of effort that you have put into putting this together. It is well constructed love the depth at which things are being discussed. You have the perfect balance between gleaming at a surface level and explaining things at a ML level which might be hard for people to follow. 
Govind Manoharan

Govind Manoharan

Technical Architect, SapientRazorfish
        I’m learning a lot, and what stands out the most for me is Aish’s ability to curate extensive material into concise, well-structured slides, along with Kiriti’s demos that bring those concepts to life. Given the six-week duration, the content is crisp, easy to grasp, and aligns well with the course objectives.
Ravi Nukala

Ravi Nukala

Senior Director of Engineering, BlackLine
        The course structure is thoughtfully designed, with bite-sized weekly lessons that are engaging and easy to absorb. It takes an enterprise and industry-specific approach to qualifying agentic AI solutions for real-world problems. I especially appreciated the flexibility to dive deep into technical aspects while still covering the basics
Milli Comstock

Milli Comstock

Gen AI Digital Strategy Leader, Hitachi Energy

LinkedIn Shoutouts From Past Cohort Members!

LinkedIn Shoutouts From Past Cohort Members!

Other praise on assignments/lectures

Other praise on assignments/lectures

Our alumni come from

Our alumni come from

Meet your instructor

Aishwarya Naresh Reganti

Aishwarya Naresh Reganti

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.

Kiriti Badam

Kiriti Badam

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.

A pattern of wavy dots

Join an upcoming cohort

Building Agentic AI Applications with a Problem-First Approach

July 2025 Cohort

$1,950

Dates

July 26—Aug 31, 2025

Payment Deadline

July 5, 2025

Don't miss out! Enrollment closes in 4 days

Oct 2025 Cohort

$1,950

Dates

Oct 4—Nov 16, 2025

Payment Deadline

Oct 17, 2025
Get reimbursed

Course schedule

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:

    • Wednesdays at 10 am PT and Saturdays at 9 am PT: Assignment Support
    • Thursdays at 5 pm PT and Saturdays at 8 am PT: Content/Use-case discussions
  • 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

Free resource

Our Curated Generative AI Free-Resource Bundle

Access our curated collection of top reading materials, roadmaps, research papers, and more!


Get this free resource

Learning is better with cohorts

Learning is better with cohorts

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

Frequently Asked Questions

Stay in the loop

Sign up to be the first to know about course updates.

A pattern of wavy dots

Join an upcoming cohort

Building Agentic AI Applications with a Problem-First Approach

July 2025 Cohort

$1,950

Dates

July 26—Aug 31, 2025

Payment Deadline

July 5, 2025

Don't miss out! Enrollment closes in 4 days

Oct 2025 Cohort

$1,950

Dates

Oct 4—Nov 16, 2025

Payment Deadline

Oct 17, 2025
Get reimbursed

$1,950

4.9 (102)

·

4 days left to enroll