Building Agentic AI Applications with a Problem-First Approach
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Aishwarya Naresh Reganti
AI Founder & Advisor to F500s | Ex-AWS
Kiriti Badam
Applied AI Lead | Ex-Google
This course is popular
3 people enrolled last week.
Design and build impactful agentic AI systems to solve business problems.
We teach all the the latest & greatest skills, including context engineering, evals, agentic retrieval and RAG, multi agent systems, harness engineering, MCP, skills, fine tuning and more! but as part of a decision map for solving business problems, based on our experience building 50+ AI products, not as a disconnected checklist.
We focus on the why and the how, not just the what. If all you want is a checklist of topics, YouTube already has plenty of that.
Check out out wall of love to see what our students think and to estimate the outcomes of the course, see our capstone showcase here.
No hard prerequisites: We have hands on assignments for both, with no-code and code heavy options. Do check out our FAQs before signing up
We follow a flipped classroom format with 35+ hours of live time
This course is an independent offering and is not affiliated with the instructors' employers.
Contact: problemfirst.ai@gmail.com
What you’ll learn
Learn to make decisions tailored to business constraints, understand when & how to apply AI effectively & build a multi-agent application
Identify where agentic AI can add value by reframing business challenges through a systems lens
Understand why traditional software assumptions fail in AI-driven environments
Evaluate tradeoffs between model choices, latency, performance and cost
Learn to frame AI problems through measurable outcomes rather than features or model choices
Understand how evaluation acts as the backbone of reliable agentic systems
Identify and quantify failure modes early using proxy metrics and iterative testing
Design smarter prompts using decomposition, meta-prompts, and algorithmic optimization
Compare reasoning and non-reasoning models for different business tasks
Implement evaluation and guardrail techniques using LLM judges and semantic scoring
Integrate retrieval, memory, and self-reflective behavior in agentic systems
Balance tradeoffs between accuracy, latency, and adaptability in agentic systems
Analyze multi-agent coordination patterns and challenges & learn about protocols like MCP/A2A
Work in small teams to design and implement an end-to-end agentic AI solution for a real business problem
Build an agentic search system across three iterations, integrating RAG, MCP, and multi-agent components
Present your final project to 2000+ attendees including enterprise leaders, investors, and hiring managers
Learn directly from Aishwarya & Kiriti
Aishwarya Naresh Reganti
AI Founder | Lecturer | Advisor | Researcher
Kiriti Badam
Applied AI Lead | AI Advisor | Ex-Google
Who this course is for
Software/AI Engineers, Strategists, Data/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 & lead AI initiatives
Entrepreneurs looking to understand common generative AI use cases and learn how to develop and implement AI-powered solutions
Prerequisites
The only prerequisite
A willingness to work with a few basic terminal commands. The course does include a no code assignment track, but does require a small setup
What's included
Live sessions
Learn directly from Aishwarya Naresh Reganti & Kiriti Badam in a real-time, interactive format.
Lifetime access to all future cohort material
Go back to course content and recordings whenever you need to. Any changes made to future cohorts will be available for you to access offline
Community of peers
Our alums are product and engineering leaders from some of the best companies: Amazon, Anthropic, Databricks, Google, Snowflake, Notion, Meta, Microsoft and 130+ other companies
Certificate of completion
Share your new skills with your employer or on LinkedIn.
30+ hours of live interaction time
Flipped content w/ office hour style sessions for two way interaction
2 Separate assignment tracks (Low-Code and Code)
You can choose to use a visual agent builder for or python based agentic framework
Access to curated no non-sense AI resources
We've curated a large collection of practitioner approved free resources for you to continue your learning journey
Option to be part of our invite only Chai & AI community post-course
About our community: https://levelup-labs.ai/community.html
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
20 live sessions • 80 lessons
Week 1
Week 2
Aug
4
Content Office Hours 1a (US/EU/UK Friendly)
Aug
5
Homework Office Hours 1b (US/APAC Friendly)
Aug
6
Homework Office Hours 1a (US/EU/UK Friendly)
Aug
7
Content Office Hours 1b (US/APAC Friendly)
Week 2: Context Engineering in 2026 & Building Agents For The Enterprise
[Build] Assignment 2
Free resources
Schedule
Live sessions
5 hrs / week
Sat, Aug 1
4:00 PM—5:00 PM (UTC)
Tue, Aug 4
3:00 PM—4:00 PM (UTC)
Wed, Aug 5
1:00 AM—2:00 AM (UTC)
Projects
3 hrs / week
Async content
4 hrs / week
What makes our course the most holistic Applied AI program out there?
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Shoutouts From Past Cohort Members!
Our alumni come from
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Frequently asked questions
❌This course is not for:
Those who have already deployed GenAI systems in production and want advanced scaling or optimization content
Individuals looking for deep theoretical or research-heavy discussions (e.g., transformer internals, pretraining, or alignment math)
Participants who have never written or worked with code before, even at a basic level
Learners expecting detailed coverage of LLMOps, infrastructure, or large-scale deployment practices
Maven for Teams
Reimbursement
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Everything L&D needs: email template, receipts, and certificate of completion.
Get reimbursedTeam discount
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