Problem-First AI

March 2025 Cohort

May 2025 Cohort

July 2025 Cohort

Self-paced Cohort

Oct 2025 Cohort

Hitachi Cohort

Jan 2026

March Cohort

Access our fully free and open-source AI evals course on Github, it'll give you a good high level overview of this course

AI Evals For Everyone Course

We put together a set of 90+ hand-curated FREE resources that cut through the noise. &nbsp;What you'll find: &nbsp;✅ 5 structured learning paths - from fundamentals to production deployment&nbsp;✅ 60+ project ideas categorized by difficulty (so you can build while learning)✅ 10 DeepLearning.AI short courses&nbsp;✅ Complete coding agents section - GitHub Copilot, Cursor, and moer!

Year End AI Resources Handbook

Download a list of over 50 capstone projects from our course with full end-to-end architecture designs, demo video, iterations, evaluations, guardrails, and more across enterprise verticals like healthcare, finance, education, real estate, retail, and legal.

Capstone Projects Showcase

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

Our Curated Generative AI Free-Resource Bundle

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

AI Founder & Advisor to F500s | Ex-AWS

Aishwarya Naresh Reganti

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

Applied AI @ OpenAI | Ex-Google

Kiriti Badam

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

Building Agentic AI Applications with a Problem-First Approach

Independent investor

Director of Analytics

Systems Analyst

Design Researcher

Product Manager

Group Product Manager

Principal Product Manager

Associate Partner

Senior Product Manager

Amazon Web Services

University of Oxford

Microsoft

OpenAI

Google

Samsung

Databricks

We teach RAG, evals, agents, MCP, multi-agents, context engineering, and all that jazz. But always as tools to solve a business problem. If you want a checklist of hype items without knowing when or why to use them, please don’t join our course. :) 🚨🚨Notes:<ul><li>We hold a 4.9 rating across 430 +reviews, making us the #1 rated course on Maven at this scale. So why are students raving about out course? Check out our <a target="_blank" rel="noopener noreferrer nofollow" href="https://testimonial.to/building-agentic-ai-applications-with-a-problem-first-approach/all">wall of love</a>!</li><li>Many of our students have No AI experience walking into the course. To estimate what you can achieve at the end of this course check out our student capstones <a target="_blank" rel="noopener noreferrer nofollow" href="https://bit.ly/capstones_pai">here.</a></li><li>We follow a flipped-classroom format. All lectures are pre-recorded so folks can go at their own pace, but we’ll still meet 3 times a week for office hours and live sessions. Check schedule below for more details</li><li>For questions or bulk requests, reach out to: problemfirst.ai@gmail.com</li></ul>This course is an independent offering and is not affiliated with, endorsed by, or related to the instructors' current or past employers.

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

Develop a Problem-First AI Intuition For Enterprise Use Cases

Build an Evals Driven Mindset & Iterative Design

Master Practical Prompt and Context Engineering

Build and Optimize Agentic Pipelines

Apply Everything in a Real-World Capstone

You should have coded at least once in your life. The course includes no-code assignments but some knowledge of code helps!

The only prerequisite

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

Evals aren't your product moat. A continuous improvement flywheel is.

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


Most teams building AI products today know about evals and LLM judges, but they’re deeply confused about how to actually turn them into actionable improvement signals post production. Just throwing in LLM judges won’t magically fix your product. It won’t.<ul><li>This course shows how to use offline evals, online evals, and production monitoring to drive continuous improvement, based on real production experience.</li><li>We believe that going forward, a thoughtfully designed data flywheel will be the moat for AI applications. This course shows you how to build it.</li><li>This course is for people who’ve already built AI systems and are stuck with evals that are noisy, expensive, or uselessIf you need something more foundational, check out our top-rated flagship<a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aishwarya-kiriti/genai-system-design"> enterprise AI course</a> taken by over 1500+ builders and leaders.</li><li>For questions or bulk enrollments please email problemfirst.ai@gmail.com</li><li>We follow a flipped-classroom format with async lecture content and meet twice a week for office hour style sessions to optimize two-way/interactive live time.</li></ul>

Move beyond one-time evals to continuous improvement flywheels for AI products

Understand what evals actually mean and why it’s misunderstood

Build a pre-deployment evaluation foundation from scratch

Choose the right types of metrics for the system you’re building

Monitor in production and close the improvement loop

You should have built small AI applications before and be familiar with concepts like RAG, MCP, and tool use.

A good understanding of AI system design

The course includes optional Python assignments, you're free to use coding agents, but basic coding skills are recommended

Working knowledge of Python/Coding (Optional)

Software/AI Engineers, Strategists, Data Professionals, Solution Architects and Consultants looking for systematic evaluation practices

Business Leaders and Product Managers seeking to gain the technical understanding of AI Product Lifecycle and intentional iteration

Founders &amp; teams stuck at systematic evaluations and monitoring, unsure how to turn signals into real product improvements.

AI Systems Under Pressure: Red-Team Before You Ship

Krystal Jackson

AI Standards Development Researcher

Learn how unclear problem definitions introduce risk in AI systems before prompts or models are involved.

Define the Real AI Problem

Examine a real AI workflow to understand how system behavior and problem mismatches create exploitable failures.

See How AI Systems Break Under Pressure

Learn how a red-teaming mindset helps validate problem framing and surface failures earlier.

Test Assumptions Before You Ship

Cursor for Product Managers

Rohan Varma

1st Product Manager at Cursor

Emily Gavrilenko

Growth at Cursor

Zoë Barnswell

Generate PRs, architecture diagrams, and implementation plans before an engineer picks up the work.

Start every ticket 90% complete

PMs can own frontend changes autonomously. Learn how to push code for small fixes without handoffs.

Ship the features your team will never prioritize

Debug production issues, prune backlogs, and ship doc updates without leaving Cursor

Use MCPs to work across your tech stack

Store PRDs in your codebase as source of truth—then code review your specs and kick off agents from them.

Turn PRDs into specs agents can execute

State of Applied AI in 2025 Report

AI Founder & Advisor to F500 Leaders

Member of Technical Staff @ OpenAI | AI Advisor

How the applied AI stack evolved, what was hyped and what teams actually adopted

2025 Hype vs. Enterprise Trends

How evals, memory, multimodal reasoning, and better context engineering will shape next year’s systems

What to prepare for in 2026

Sharing some best practices from working on several enterprise ai implementations

5 rules for building robust agents in the wild

#2 Designing Agentic AI Systems for Enterprise Use-Cases

Aishwarya Reganti

AI Advisor, Founder, Ex-AWS

Applied AI Engineer @ OpenAI, Ex-Google

Understand how to take an enterprise use-case, scope it and build solutions with state-of-the-art techniques

Watch 20+ Agentic AI Application Solutions

Use-cases include verticals like finance, real-estate, manufacturing, retail and more!

See Live Solutions For a Wide Range of Enterprise Verticals

What sets a good AI practitioner apart is their ability to work within business constraints. Watch it in real time!

Learn About Our Problem-First Framework

A 25% off coupon will be shared during the session and will be valid only until the session ends.

Get An Exclusive Discount On Our Next Cohort

Sign up for #1: https://maven.com/p/f9951a/1-designing-agentic-ai-systems-for-enterprise-use-cases

🔔 Note: Projects are spread across 2 sessions

#1 Designing Agentic AI Systems for Enterprise Use-Cases

Sign up for #2: https://maven.com/p/22d0f6/2-designing-agentic-ai-systems-for-enterprise-use-cases

Applied AI Engineer, OpenAI, Ex-Google

Watch 15+ Agentic AI Application Solutions

A 15% off coupon will be shared during the session and will be valid only until the session ends.

Sign up for #1: https://maven.com/p/545da1/1-designing-agentic-ai-systems-for-enterprise-use-cases

Sign up for #2: https://maven.com/p/69f3e6/2-designing-agentic-ai-systems-for-enterprise-use-cases?utm_medium=ll_share_l

Don't Build AI Products Like Traditional Software

Founder @ LevelUp Labs | AI Advisor | Ex-AWS

Applied AI @ OpenAI | AI Advisor | Ex-Google

Traditional software behaves predictably. AI doesn’t. Learn how to build around the non-determinism

AI Products & Non-Determinism

Most enterprise teams rush into building autonomous agents, and fail. Learn how to think through the tradeoffs first.

Understanding the Agency/Control Tradeoff

CI/CD doesn't quite make the cut for AI products, learn about our new CC/CD framework!

CI/CD for AI Products

#3 Single vs. Multi-Agent AI Systems

AI Tech Lead | Advisor

We’ll break down what qualifies as multi-agent, common misconceptions, and the core coordination patterns they use.

What Multi-Agent Systems Are (And Aren’t)

Understand when multi-agent systems are helpful and when they can lead to unnecessary debugging overhead

When to Use One Agent vs Many

We’ll cover the real-world challenges: context sharing, memory, handoffs, failure handling etc.

Hidden Complexities in Multi-Agent Setups

#2 AI Protocols 101: What You Should Know About MCP, A2A etc

Tech Lead | AI Advisor

Get a clear picture of the protocol ecosystem (MCP, A2A, and others) and how they fit into the agentic AI stack.

The AI Protocol Landscape Today

See how enterprises are using these protocols today, and what to keep in mind around security, modularity & more

What Practitioners Need to Watch For

Know about current gaps that exist in the space and best enterprise practices while building

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