Build AI Agents
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This program trains TPMs to own AI systems end to end, from coordination to production scale.
This program trains TPMs to own AI systems end to end, from orchestration to production scale.
Outcome: Build intuition for agentic AI systems and deploy a first working agent.
Outcome: Frame AI initiatives correctly and define launch readiness criteria.
*TPM interview prep is available with EdgeUp
This curriculum trains TPMs to own AI systems end to end, from orchestration and risk to production launch and scale, the way AI platforms are run in 2026.
Outcome: Build intuition for agentic AI systems and deploy a first working agent.
Outcome: Frame AI initiatives correctly and define launch readiness criteria.
*TPM interview prep is available with EdgeUp
This curriculum trains TPMs to own AI systems end to end, from orchestration and risk to production launch and scale, the way AI platforms are run in 2026.
Build a hybrid FAQ agent combining deterministic reflex logic with LLM-based reasoning, including decision-logic visualization to understand cost, latency, and reliability tradeoffs.
Develop a RAG-based assistant that ingests PDFs and Notion documents, retrieves grounded answers, and uses evaluation logs to track retrieval relevance and response quality
Design a multi-agent research automation flow where specialized agents (researcher → analyst → critic) collaborate to gather, analyze, and validate information.
Create a voice-enabled customer feedback analyzer with persistent memory blocks, focusing on conversational flow, memory retention, and multimodal interaction.
Produce a complete system architecture for an AI feature (e.g., support bot or analyzer), covering data flow, orchestration, integrations, and enterprise constraints.
Design a KPI dashboard that tracks model accuracy, cost per user, and response quality to support launch readiness and iteration decisions.
Build a fine-tuned Q&A model integrated into LangFlow and create a comparison report evaluating prompting vs RAG vs fine-tuning outcomes.
Build a hybrid FAQ agent combining deterministic reflex logic with LLM-based reasoning, including decision-logic visualization to understand cost, latency, and reliability tradeoffs.
Develop a RAG-based assistant that ingests PDFs and Notion documents, retrieves grounded answers, and uses evaluation logs to track retrieval relevance and response quality
Design a multi-agent research automation flow where specialized agents (researcher → analyst → critic) collaborate to gather, analyze, and validate information.
Create a voice-enabled customer feedback analyzer with persistent memory blocks, focusing on conversational flow, memory retention, and multimodal interaction.
Produce a complete system architecture for an AI feature (e.g., support bot or analyzer), covering data flow, orchestration, integrations, and enterprise constraints.
Design a KPI dashboard that tracks model accuracy, cost per user, and response quality to support launch readiness and iteration decisions.
Build a fine-tuned Q&A model integrated into LangFlow and create a comparison report evaluating prompting vs RAG vs fine-tuning outcomes.
Build Mira an agentic AI assistant that generates high level project plans from documents like scope briefs and risk assessments and auto produces weekly status reports by pulling live data from Trello. Designed for Nexora’s overloaded TPMs Mira reduces manual planning and reporting effort improves risk visibility and keeps AI adoption projects like ABCDE Ltd’s on track using grounded inputs human in the loop review and clear productivity metrics.
Design and implement CalendarMate, an Agentic AI assistant built on the low-code platform n8n to streamline meeting scheduling, consolidate meeting notes, summarize daily emails, and provide a quick activity overview. By integrating with multiple calendars and communication tools, CalendarMate resolves conflicts, improves productivity, and reduces missed meetings. You will also create a comprehensive program charter outlining objectives, scope, success metrics, risks, and a rollout plan.
Choose from one of 10 Capstone Projects.
Build Mira an agentic AI assistant that generates high level project plans from documents like scope briefs and risk assessments and auto produces weekly status reports by pulling live data from Trello. Designed for Nexora’s overloaded TPMs Mira reduces manual planning and reporting effort improves risk visibility and keeps AI adoption projects like ABCDE Ltd’s on track using grounded inputs human in the loop review and clear productivity metrics.
Design and implement CalendarMate, an Agentic AI assistant built on the low-code platform n8n to streamline meeting scheduling, consolidate meeting notes, summarize daily emails, and provide a quick activity overview. By integrating with multiple calendars and communication tools, CalendarMate resolves conflicts, improves productivity, and reduces missed meetings. You will also create a comprehensive program charter outlining objectives, scope, success metrics, risks, and a rollout plan.
Automate risk assessment for large-scale AI/ML programs by building a system that includes agents for scanning project documents, tracking dependencies, and suggesting mitigation strategies. Built with LangChain, CrewAI, OpenAI function calling, and RAG-based retrieval, it will ensure proactive risk management.
Automate workload balancing and developer resource forecasting by creating an agentic system that will analyze Jira and GitHub activity, predict bandwidth constraints, and integrate hiring needs with sprint planning. Using LangChain, CrewAI, and OpenAI embeddings, it optimizes engineering resource allocation.
+ Instructors to Train You
FAQs
What is Agentic AI, and how is it different from traditional AI?
Agentic AI focuses on autonomous systems that operate proactively to achieve goals using LLMs and other tools, without constant human intervention. Unlike traditional AI, which is often reactive and generally requires explicit instructions for each task, Agentic AI understands its environment, thinks through the goals and how to achieve them, makes decisions, takes actions, learns from its experiences, and adapts its behavior over time.
What are the practical applications of Agentic AI?
Applications of Agentic AI include:
How is this different from a general AI or ML course?
This course focuses on building and deploying autonomous AI agents using low-code and no-code tools, specifically for enterprise program management — not on deep ML theory.
Do I need a background in AI or machine learning to join this course?
No. The course is designed to be accessible to TPMs with no prior AI or ML experience. It starts with foundational concepts and progressively builds up to advanced topics.
What is the duration and structure of the course?
The course runs for 13 weeks: 8 weeks of core modules and 5 weeks of domain-specific learning and capstone projects.
What kind of projects will I build in the course?
How do Capstone Projects help my career?
Capstone Projects are designed with FAANG+ hiring managers in mind. Over 67% of hiring managers now demand to see practical know-how rather than certification or theoretical understanding. They’re reviewed for scalability, robustness, and relevance—showcasing your readiness for AI-enhanced roles
How does this course help me land a FAANG+ job?
How much time do I need to commit weekly?
Expect around 10 hours of learning per week, covering live sessions, project work, and domain-specific learning. Bonus content and interview preparation sessions are available for those who want to dive deeper.
Who are the instructors?
All our instructors are current or former FAANG+ professionals with deep expertise in Generative AI, LLMs, and AI/ML.
What tools and platforms will I learn?
You’ll work with tools like LangChain, AutoGen, CrewAI, LangGraph,n8n, Python, Streamlit, and more.
How is this different from a typical AI bootcamp?
This program goes beyond generic AI or prompt engineering training. It is designed for TPMs who want to lead AI implementation—not just build models—focusing on deploying intelligent systems into real-world programs.
What support do I get during the course?
You get access to:
What are the career outcomes or placement support offered?
What happens if I miss a live session?
All live sessions are recorded and accessible on-demand. You can catch up anytime and even rewatch for revision.
Is there a payment plan?
Yes! We offer multiple financing options to make the course more accessible to working professionals.
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