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Maven - Building Agentic AI Applications with a Problem-First Approach - OneDDL - 09-19-2025 Free Download Maven - Building Agentic AI Applications with a Problem-First Approach Released 9/2025 By Aishwarya Naresh Reganti and Kiriti Badam MP4 | Video: h264, 2048x1002 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 53 Lessons ( 44h 38m ) | Size: 9.72 GB 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 -We follow a flipped-classroom format. All lectures are pre-recorded so folks can go at their own pace, but we'll still meet 5 times a week for office hours and live sessions. The course includes 35+ hours of live time. Check schedule below - For questions or bulk requests, reach out to: - October will be our final cohort. We don't plan to run this course in 2026, it's simply gotten too hectic. As much as we love teaching AI, we love building enterprise AI systems a bit more. So if you've been considering it, this is your last chance to join. - 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. ---- ❌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. 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. Homepage [To see links please register or login] Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [To see links please register or login] No Password - Links are Interchangeable