Build AI Agents
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This program prepares Engineering Managers to own AI platforms end to end, the way EM roles will be defined in 2026 and beyond.
This program prepares Engineering Managers to own AI platforms end to end, the core EM mandate in 2026 and beyond.
Outcome: Understand agent behavior and make informed design tradeoffs.
Outcome: Understand and design structured agent communication.
Outcome: Understand agent behavior and make informed design tradeoffs.
Outcome: Understand and design structured agent communication.
Build your first LLM-powered agent to understand how agents differ from chatbots. Learn how LLMs act as reasoning engines, how tools and memory fit into agent architecture, and how agent behavior is controlled.
Build a document-grounded knowledge assistant using Retrieval-Augmented Generation (RAG). Ingest PDFs and text files, design chunking strategies, retrieve relevant context, and evaluate retrieval quality to prevent hallucinations.
Design a multi-agent system using a Planner → Executor → Critic pattern. Agents collaborate to research, summarize, and critique outputs, demonstrating task decomposition, coordination, and quality loops.
Build a stateful, voice-enabled conversational agent with memory. Handle multi-turn conversations, track intent across sessions, and explore memory drift and summarization trade-offs in long-running agents.
Create a buyer–seller negotiation system where agents communicate using structured protocol messages instead of free text. Implement state transitions, branching logic, retries, and error handling to prevent loops and ambiguity.
Build a domain-specific vertical agent that compares data from multiple APIs and sources. Handle authentication, rate limits, retries, caching, and return structured, schema-validated insights.
Design a recommendation pipeline that summarizes information, scores options, ranks results, and presents insights through a visual dashboard. Emphasize faithfulness, bias awareness, and human-in-the-loop review.
Build a production-ready customer support agent with RAG, safety guardrails, evaluation pipelines, and cost/latency dashboards. Learn how to operate agents responsibly under real-world constraints.
Build a domain-adapted agent by fine-tuning a language model for a specific vertical such as Finance, Healthcare, or SaaS.
Learn how to prepare datasets, apply parameter-efficient fine-tuning techniques, and integrate the fine-tuned model into an existing agent workflow. Evaluate performance improvements against prompting and RAG baselines, and analyze cost–benefit trade-offs to decide when fine-tuning is justified in real-world systems
Build your first LLM-powered agent to understand how agents differ from chatbots. Learn how LLMs act as reasoning engines, how tools and memory fit into agent architecture, and how agent behavior is controlled.
Build a document-grounded knowledge assistant using Retrieval-Augmented Generation (RAG). Ingest PDFs and text files, design chunking strategies, retrieve relevant context, and evaluate retrieval quality to prevent hallucinations.
Design a multi-agent system using a Planner → Executor → Critic pattern. Agents collaborate to research, summarize, and critique outputs, demonstrating task decomposition, coordination, and quality loops.
Build a stateful, voice-enabled conversational agent with memory. Handle multi-turn conversations, track intent across sessions, and explore memory drift and summarization trade-offs in long-running agents.
Create a buyer–seller negotiation system where agents communicate using structured protocol messages instead of free text. Implement state transitions, branching logic, retries, and error handling to prevent loops and ambiguity.
Build a domain-specific vertical agent that compares data from multiple APIs and sources. Handle authentication, rate limits, retries, caching, and return structured, schema-validated insights.
Design a recommendation pipeline that summarizes information, scores options, ranks results, and presents insights through a visual dashboard. Emphasize faithfulness, bias awareness, and human-in-the-loop review.
Build a production-ready customer support agent with RAG, safety guardrails, evaluation pipelines, and cost/latency dashboards. Learn how to operate agents responsibly under real-world constraints.
Build a domain-adapted agent by fine-tuning a language model for a specific vertical such as Finance, Healthcare, or SaaS.
Learn how to prepare datasets, apply parameter-efficient fine-tuning techniques, and integrate the fine-tuned model into an existing agent workflow. Evaluate performance improvements against prompting and RAG baselines, and analyze cost–benefit trade-offs to decide when fine-tuning is justified in real-world systems
Projects are subject to change as per industry inputs. Choose from one of 3 Capstone Projects.
This project equips Engineering Managers with an AI-powered multi-agent system that transforms complex BRDs into structured engineering plans and technical designs. Using n8n for orchestration, LLMs for parsing and generation, and a user-friendly UI, learners build agents for planning, scheduling, architecture, PoC scoping, and tech stack recommendations. The solution streamlines project initiation, enforces standardization, and accelerates decision-making, enabling EMs to produce stakeholder-ready artifacts quickly and efficiently.
This Project enables Engineering Managers to gain real-time visibility and insights into the hiring funnel using a multi-agent intelligence system. Built with n8n orchestration and LLM-driven analysis, agents track sourcing quality, rejection trends, panel workloads, and offer declines. The system generates actionable recommendations, highlights bottlenecks, and presents metrics via a dashboard-style UI. By standardizing insights and automating interventions, learners build tools that enhance hiring efficiency, improve candidate experience, and accelerate role closures.
This project empowers Engineering Managers to build a multi-agent system that automates team surveys, extracts insights, and drives actionable improvements in culture, learning, and career growth. Using n8n orchestration, LLM-powered survey generation, adaptive feedback logic, and dashboard-style interfaces, learners design agents for survey cycles, sentiment analysis, action recommendations, and follow-up tracking. The system helps EMs foster engagement, track progress over time, and translate team feedback into tangible outcomes for morale, retention, and performance.
Projects are subject to change as per industry inputs. Choose from one of 3 Capstone Projects.
This project equips Engineering Managers with an AI-powered multi-agent system that transforms complex BRDs into structured engineering plans and technical designs. Using n8n for orchestration, LLMs for parsing and generation, and a user-friendly UI, learners build agents for planning, scheduling, architecture, PoC scoping, and tech stack recommendations. The solution streamlines project initiation, enforces standardization, and accelerates decision-making, enabling EMs to produce stakeholder-ready artifacts quickly and efficiently.
This Project enables Engineering Managers to gain real-time visibility and insights into the hiring funnel using a multi-agent intelligence system. Built with n8n orchestration and LLM-driven analysis, agents track sourcing quality, rejection trends, panel workloads, and offer declines. The system generates actionable recommendations, highlights bottlenecks, and presents metrics via a dashboard-style UI. By standardizing insights and automating interventions, learners build tools that enhance hiring efficiency, improve candidate experience, and accelerate role closures.
This project empowers Engineering Managers to build a multi-agent system that automates team surveys, extracts insights, and drives actionable improvements in culture, learning, and career growth. Using n8n orchestration, LLM-powered survey generation, adaptive feedback logic, and dashboard-style interfaces, learners design agents for survey cycles, sentiment analysis, action recommendations, and follow-up tracking. The system helps EMs foster engagement, track progress over time, and translate team feedback into tangible outcomes for morale, retention, and performance.
FAANG+ 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:
What does this course teach?
This course teaches Engineering Managers how to integrate Agentic AI into engineering workflows to drive automation, efficiency, and strategic decision-making.
Do I need prior AI or ML experience to enroll in this course?
No, our Applied Agentic AI Program for Engineering Managers is beginner-friendly and designed for EMs from diverse technical backgrounds. A Python crash course is included.
What kind of projects will I build in the course?
You’ll build hands-on projects like an AI agent for stock analysis, a multi-agent healthcare system, an automated data insights generator, using python and low-code platform like n8n.
Will I learn how to lead AI teams?
Yes. Specialized sessions focus on technical guidance, AI leadership, ROI evaluation, and managing AI/ML professionals.
How does this course help me land a FAANG+ job?
Through live sessions led by FAANG+ practitioners, FAANG-focused interview prep, and mock interviews with hiring managers and tech leads. You’ll also build a compelling portfolio with capstone projects reviewed by mentors from companies like Google, Amazon, and Meta.
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. The structure is of 14 weeks—9 weeks of core curriculum, 5 weeks of domain-specific Capstone Projects, with live sessions and peer feedback.
Who are the instructors?
All our instructors are current or former FAANG+ Engineering Leaders with deep expertise in Generative AI, LLMs, and AI/ML.
What tech stack and tools will I learn?
You’ll work with LangChain, LangGraph,n8n, Streamlit, Pinecone, LlamaIndex, FastAPI, and more.
What support do I get during the course?
You get access to:
How is this different from other AI or ML courses?
This course focuses on real-world engineering use cases, with no-code/low-code tools, leadership development, and Agentic AI—not abstract theory or deep ML.
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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