Maven - Building Agentic AI Applications with a Problem-First Approach [Update 09/2025]
English | Size: 8.7 GB
Genre: eLearning[/center]
Learn to make decisions tailored to business constraints, understand when & how to apply AI effectively & build a multi-agent application
Design and build impactful agentic AI systems to solve business problems.
Yes, 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:
- For current offers/promotions check out the FAQs section below
- Check out our student capstones: https://areganti.notion.site/Enterprise … 4dde771262
-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
- For questions or bulk requests, reach out to: problemfirst.ai@gmail.com
- We're running another cohort in Jan 2026 on popular demand!
- 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, do it smarter with right evals)
Break down the evolution from zero-shot prompts to self-optimizing models
Master context engineering: 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.
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