March 2025 Cohort

Learn to make decisions tailored to business constraints, understand when & how to apply AI effectively & build a multi-agent application

Aishwarya Naresh Reganti

Tech Lead | Lecturer | Advisor | Researcher | Speaker | Investor

Kiriti Badam

Member of Technical Staff @ OpenAI | AI Advisor | Ex-Google

Building Agentic AI Applications with a Problem-First Approach

Google

University of Oxford

MIT research group

Amazon Web Services

OpenAI

🚨🚨Note: This course is an independent offering and is not affiliated with, endorsed by, or related to the instructors' current or past employers. ⛳&nbsp;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 assumptionsFrame hallucinations, latency, and prompt brittleness through the determinism spectrumOpen vs. closed models: tradeoffs across compliance, latency, and costProblem-first, evaluation-driven design using early datasets and proxy metricsDeconstruct 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 modelsMaster prompting: Decomposition, meta-prompts, algorithmic optimizationAnalyze when to use prompting-only systems based on task, cost, and latencyCompare model-level strategies: reasoning vs. regular, and when each makes senseAdd 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 injectionBuild robust RAG pipelines with advanced chunking, embedding selection, and retrieval methodsExplore GraphRAG, Agentic RAG and multimodal RAG and other advanced methods and learn tradeoffsArchitect 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 workflowsCompare agent levels and their control dimensions: action, planning, evolution, and physical autonomyExplore MCP (Model Context Protocol) and A2A as emerging agent-tool communication standardsInvestigate critical security challenges in MCP and A2A. Understand how guardrails, tool signing, audit trails improve reliabilityAnalyze coordination patterns in multi-agent systems, including shared memory governance, state sync, AI collusion risks, evaluation, logging, and observabilityExplore 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 6Take a business problem and design/implement a solutionDemo 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. ---- ❌ 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.

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

Software/AI Engineers, Strategists, Data Professionals, Solution Architects and Consultants who want to master AI system design

Business Leaders and Product Managers seeking to gain the technical understanding needed to make informed decisions &amp; lead AI initiatives

Entrepreneurs looking to understand common generative AI use cases and learn how to develop and implement AI-powered solutions

Who is this course for

You should have coded at least once in your life. The course includes low-code assignments, and even folks who hadn

The only prerequisite

What you’ll get out of this course

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

Solving Real Enterprise Challenges, Not Just Concepts 

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)

Apply Concepts to Build an Agentic Search System

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. <a href="https://areganti.notion.site/Capstone-Projects-Problem-First-AI-1e0c91fa32368011b935e00a35d9bceb" rel="noopener noreferrer" target="_blank">Examples</a>

Capstone Project

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

Understand Challenges and Effective Evaluation
 

The course includes guest lectures from industry experts, AMA sessions, and our Chai &amp; 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.

Access to the Problem-First AI Community

Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

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

Senior Vice President of Strategy (AI), Hitachi Digital

Senior Software Engineer, Clear Aspect Solutions

Senior Director of Engineering, BlackLine

Gen AI Digital Strategy Leader, Hitachi Energy

What people are saying

LinkedIn Shoutouts From Past Cohort Members!

Other praise on assignments/lectures

Our alumni come from

Meet your instructor

We offer generous office hours to support you throughout the course, along with 24-hour help on Slack. Typical weekly schedule:<ul><li>Wednesdays at 10 am PT and Saturdays at 9 am PT: Assignment Support</li><li>Thursdays at 5 pm PT and Saturdays at 8 am PT: Content/Use-case discussions</li></ul>

Live Office Hours

Our weekly community discussion channel covers the latest trends in AI, industry updates, and all the unfiltered tea, open, casual, and freeform.

Chai & AI Sessions

We bring in industry experts to share proven insights and discuss real enterprise challenges

Guest Lectures

Course schedule

This course builds on live workshops and hands-on projects

Active hands-on learning

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

Course completed by

About this course

Get notified about the next cohort

Learn from live sessions