Product Management for Agentic AI
Bridge the gap between engineering and user needs. Learn how to design, scope, and measure the success of non-deterministic AI systems.
Full access to curriculum, live sessions, systems architecture guidance, and private cohort network.
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About this program
Managing AI products is fundamentally different from traditional SaaS. LLMs are non-deterministic, hallucinatory, and expensive. This program teaches you how to define guardrails, set up robust evaluation pipelines (Evals), manage user expectations through clever UX, and successfully launch Agentic products.
Who is this for?
Product Managers, Tech Leads, UI/UX Designers
What you'll actively build & learn
Understanding Fundamentals
Grasp the core mechanics of AI systems, from transformers to retrieval algorithms, moving beyond superficial APIs.
Production-Ready Architecture
Learn how to architect scalable, resilient generative AI applications that handle edge cases and high throughput.
Hands-on Engineering
Write custom PyTorch models, build multi-agent swarms using LangGraph, and deploy to Kubernetes.
Verifiable Execution
Complete rigorous capstone projects that serve as a proof-of-work portfolio for your next AI engineering role.
Time Commitment & Schedule
Live Engineering
2-3 hrs / week
Deep-dive interactive technical sessions focusing on architecture, code walkthroughs, and edge cases. Fully recorded.
Independent Build
4-6 hrs / week
Asynchronous reading materials, implementing weekly milestones, and collaborating via Discord for unblocking code errors.
Weekly Syllabus
Each week is structured around three things: what you'll cover, what capability you'll walk away with, and the concrete deliverable that moves you toward the final capstone.
5 weeks of product strategy and AI systems framing
An executive-ready AI product PRD and launch plan
Case studies, frameworks, and capstone planning reviews
Anatomy of Probabilistic Features
- Traditional CRUD apps are deterministic; if X, then Y.
- AI breaks this paradigm.
- We dive deep into architectural tradeoffs, analyzing exactly when to use a simple LLM call vs.
- a Multi-Agent framework.
- We will audit real-world AI product failures and learn precisely how to constrain models to ensure reliable, narrow success paths.
Understand where AI fits and where it creates product risk.
A decision framework for choosing AI interaction patterns.
Defensive UX for AI Interfaces
- A raw chatbox is lazy product design.
- We engineer advanced patterns for user control.
- You'll learn to implement 'Streaming' for perceived latency reduction, 'Fallback UI' when models inevitably fail, 'Steering Capabilities' to let users correct the agent, and 'Confidence Indicators' to manage expectations during long-running tasks.
Design clearer AI experiences with stronger user trust patterns.
A UX flow with steering, fallback, and confidence states.
Evals & Hallucination Mitigation
- If you cannot measure it, you cannot ship it.
- We move beyond manual vibe-checks into building programmatic Evaluation Pipelines (Evals).
- You will learn how to structure 'Golden Datasets', use LLM-as-a-Judge frameworks to score outputs automatically, and define strict regression testing protocols before allowing engineers to merge new prompts.
Define how success and failure will be measured before launch.
An evaluation plan with regression and quality criteria.
Token Economics & Unit Margins
- AI margins can easily flip negative.
- We rigorously break down API cost structures across OpenAI, Anthropic, and local deployment options.
- You will build financial models to calculate the exact cost per agentic action, learning how caching, prompt compression, and smaller specialized models (SLMs) can save your company millions in AWS bills.
Model the financial reality behind AI product decisions.
A token-cost and unit-economics model for your concept.
The Agentic Go-To-Market Capstone
- You will draft a complete, executive-ready PRD (Product Requirements Document) for a complex multi-agent system.
- This includes defining the exact UX flows, the required Evals criteria for launch, the unit economics breakdown, and the specific fallback states.
- You will pitch this to a mock executive board.
Package the strategy, UX, economics, and evals into one cohesive proposal.
An executive-ready AI product PRD and pitch deck.
The syllabus builds toward a final proof of work.
The weekly syllabus is designed to stack toward a capstone that demonstrates what you can actually build. By the end of the cohort, you are not just finishing modules. You are presenting a concrete output that ties the learning arc together.
View Alumni CapstonesIndustry-Grade Certification
Earn a credential that actually matters. Every certificate is tied to your Capstone Project repo, valid for life, and optimized for your professional technical profile.
View Certification TiersEngineering Trust
Our alumni don't just 'use' AI. They architect the core infrastructure at forward-thinking engineering labs. This is a high-trust collective of senior talent.
"We've created a zero-noise environment for senior talent. This is where staff and principal engineers from Silicon Valley and beyond come to cross-pollinate their knowledge of agentic systems and distributed training."
The most technically rigorous program I've attended. No fluff, just pure architectural deep-dives into transformer blocks and swarm logic. This isn't just about calling APIs; it's about understanding the stochastic internals of LLMs.
LangGraph and Multi-agent orchestration was the missing link for our production pipeline. Highly recommended for senior devs who need to move beyond single-prompt engineering into complex, stateful workflows.
Direct 1:1 access to instructors who are actually shipping AI products. The focus on evaluations and evals-driven-dev is unique. We've implemented their RAG evaluation pipeline for our entire stealth startup.
Lead Instructor
Deep pedagogical philosophy balanced with production engineering rigor.
Meet
Anubhav
Anubhav is an AI solutions and engineering leader with two decades of global experience executing machine learning, generative AI, and physical intelligence initiatives.
With a proven track record of founding startups and building 0-to-1 engineering teams, he has architected and delivered production-grade systems across B2B SaaS, industrial robotics, sports tech, and massive-scale consumer streaming platforms serving over 600 million users.
At skilling academy, he personally mentors every student, bringing extensive experience in enterprise strategy, multi-agent workflows, computer vision, and scalable distributed architectures from the boardroom to the IDE.
Technical Expertise
- Transformers / Attention
- GNNs & Graph Search
- RLHF / DPO Alignment
- Distributed Training
- vLLM / NVIDIA Triton
- Kubernetes / Ray
- VectorDB Scaling
- Hybrid Retrieval
- Knowledge Graphs
- Autonomous Execution
- ReAct / Tool-use
- Planner Architectures
System FAQ
Addressing technical edge cases and curriculum logistics for the committed engineer.
Our cohorts are crafted for mid-to-senior level software engineers, data scientists, and technical product managers who are comfortable with Python and basic web architecture. If you've been 'prompt engineering' but want to understand the underlying mechanics—transformer blocks, vector algebra, and autonomous agent orchestration—this is for you.
Plan for 6-8 hours of focused effort per week. This breaks down into 2 hours of live, interactive deep-dives on Saturdays, 1 hour of midweek Q&A/Office Hours, and 3-5 hours of dedicated hands-on project implementation where you'll build production-ready AI modules.
Life happens. Every live session is recorded in 4K and uploaded to our private portal within 2 hours. You'll have lifetime access to these recordings, including all updated versions of the curriculum. Our Discord community and mentors are active 24/7 to help you get back on track.
Not necessarily. While we discuss hardware optimization, most of our practical work utilizes cloud-based environments (Google Colab, Modal, or Lambda Labs). We provide credits and setup guides so you can run large-scale inference and fine-tuning without burning through your own hardware.
We keep cohorts focused (max 60) to maintain a high mentor-to-student ratio. You’ll be split into smaller review pods, and you’ll get dedicated feedback via office hours and code review workflows. This keeps discussions high-bandwidth and practical.
We teach 'First Principles'. While we use popular frameworks for speed, we spend significant time building core components (like Custom RAG retrievers or ReAct loops) from scratch. This ensures that when the next big framework arrives, you'll understand exactly how it works under the hood.
Absolutely. Our final project is a portfolio-grade AI system that solves a real business problem. We also provide a dedicated session on the AI Engineering interview landscape, resume reviews for technical roles, and introductions to our network of hiring partners in the AI space.
We want you to be 100% satisfied. If after the first week you feel the cohort isn't the right fit, we offer a full, no-questions-asked refund. Our goal is to build a community of committed builders, and we stand by the quality of our curriculum.
Yes. All students get lifetime access to our internal repository of production-ready templates, deployment scripts, and evaluation benchmarks. These are the same tools our instructors use to build and scale AI solutions in their day-to-day professional work.
Upon successful submission and review of your final 3 project modules, you will receive a cryptographically signed digital certificate. This certificate is recognized by our network of partner companies and can be directly shared on LinkedIn or included in your professional portfolio.