Build AI Agents
Switch to AI/ML/DS
This is a living curriculum, continuously updated to reflect how agentic AI systems are built and operated in 2026 and beyond.
Outcome: Set up the stack and ship a working agent.
Outcome: Design predictable and controllable agents.
Outcome: Build reliable, retrieval-backed agents.
Outcome: Design coordinated multi-agent workflows.
Outcome: Build stateful conversational agents.
Outcome: Design structured, debuggable agent communication.
Outcome: Build production-ready domain agents.
Outcome: Ship agents that support real decisions.
Outcome: Operate agents safely and efficiently.
Outcome: Integrate custom models responsibly.
Outcome: Design a production-grade agentic AI platform.
Outcome: Explain system design choices clearly in interviews.
Outcome: Defend orchestration and data decisions confidently.
Outcome: Handle senior SWE interviews focused on production AI systems.
Outcome: Set up the stack and ship a working agent.
Outcome: Design predictable and controllable agents.
Outcome: Build reliable, retrieval-backed agents.
Outcome: Design coordinated multi-agent workflows.
Outcome: Build stateful conversational agents.
Outcome: Design structured, debuggable agent communication.
Outcome: Build production-ready domain agents.
Outcome: Ship agents that support real decisions.
Outcome: Operate agents safely and efficiently.
Outcome: Integrate custom models responsibly.
Outcome: Design a production-grade agentic AI platform.
Outcome: Explain system design choices clearly in interviews.
Outcome: Defend orchestration and data decisions confidently.
Outcome: Handle senior SWE interviews focused on production AI systems.
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.
Capstones stay aligned with industry needs. Pick from 10 production-grade projects or build your own system.
Build a personalized financial education experience with an AI-powered Finance Assistant that leverages multi-agent LLM systems and Retrieval-Augmented Generation (RAG). The assistant delivers context-aware investment guidance, real-time market insights, and portfolio analysis tailored to each user. Designed for scale, it simplifies complex financial concepts and empowers beginners to make informed decisions.
Accelerate marketing efforts with ContentAlchemy, an AI-powered content creation platform that leverages multi-agent LLM systems to generate high-quality blogs, LinkedIn posts, visuals, and research-driven content. The system uses intelligent agent orchestration to ensure SEO optimization, brand voice consistency, and platform-specific formatting. Designed for creators and businesses, it enables scalable, on-demand content production across multiple formats and channels.
Transform raw call data into actionable insights with an AI-powered Voice-to-Insights system that leverages multi-agent LLMs and speech-to-text technology. The system automatically transcribes, summarizes, and quality-checks support calls, providing structured evaluations and key takeaways at scale. Designed for modern call centers, it standardizes QA processes, improves compliance, and enables faster, data-driven decision-making.
Streamline communication with an AI-powered Email Assistant that uses multi-agent LLM workflows to generate personalized, context-aware email drafts. The system intelligently detects intent, applies custom tone styling, and ensures high-quality output through review and validation agents. Built for productivity and scalability, it enables teams to draft professional emails in seconds while maintaining consistent voice and messaging.
Build an agentic system that automates DevOps workflows through four specialized agents: a Code Analyzer for security reviews, a CI/CD Monitor for deployment oversight, an Infrastructure Scaler for resource management, and an Incident Resolver for system diagnostics. Build it with LangChain, CrewAI, and OpenAI API and integrate with GitHub Actions, AWS Lambda, and containerization tools, while using vector databases and monitoring solutions.
Build an assistant that streamlines healthcare services through four specialized agents: a Symptom Checker for initial assessments, an Appointment Scheduler for EHR/EMR integration, a Medical FAQ Bot for patient queries, and an Insurance Advisor for claims guidance. Use LangChain, GPT-4, and healthcare APIs to create a system that offers comprehensive patient support while maintaining secure data management through VectorDB storage.
Build a comprehensive agentic system utilizing four specialized agents to protect applications: a Vulnerability Scanner for detecting common threats, a Code Security Analyzer for OWASP Top 10 compliance, a Log Analyzer for anomaly detection, and a Compliance Checker for regulatory standards. Use tools like LangChain, OpenAI GPT, and OWASP ZAP to ensure robust security through integrated monitoring and analysis.
Employ four specialized agents to streamline legal document processing: a Contract Analyzer for extracting key elements, a Compliance Checker for regulatory validation, a Case Law Researcher for finding precedents, and a Summary Generator for creating digestible content. Use LangChain, OpenAI, and OCR tools to offer comprehensive legal document analysis through an interactive interface.
Build a multi-agent system designed to automate supply chain processes, including inventory management, demand forecasting, and logistics tracking. The system consists of four agents: a demand forecaster using time-series ML models, an inventory manager analyzing stock levels, a logistics tracker monitoring shipments, and a procurement assistant optimizing supplier contracts. Leverage Python, TensorFlow, XGBoost, LangChain, OpenAI API, SQL/NoSQL databases, and visualization tools like Streamlit in this project.
Enhance software development with an AI-powered pull request (PR) reviewer bot that automates code reviews using Large Language Models (LLMs). This bot provides detailed feedback, identifies bugs, security vulnerabilities, and coding violations, and suggests best practices to streamline the code review process. It improves efficiency and code quality while assisting human reviewers. Integrate with GitHub/GitLab for seamless operation and use models like GPT-4 or Hugging Face Transformers for accurate code analysis. Build with React or Streamlit, and deploy using Docker and AWS for smooth execution.
Streamline the hiring process with an AI-powered assistant that automates resume screening and scoring using large language models (LLMs). This tool evaluates resumes against job descriptions, identifying strengths, weaknesses, and alignment with role requirements. It enhances ATS platforms by providing actionable feedback and recommendations to find the best-fit candidates. Integrate with tools like GPT-4, Gemini Pro, and LangChain for seamless operation. Build a user-friendly interface using React, Node.js, and MongoDB, and deploy it on the cloud with Docker and AWS.
Work on personal or professional projects of your choice. BYOP offers mentorship, structured guidance, and feedback to ensure projects are aligned with industry standards and best practices. It fosters creativity, innovation, and real-world problem-solving, enabling participants to build impactful solutions. You will receive guidance on selecting the right tools and frameworks based on project requirements.
Build a personalized financial education experience with an AI-powered Finance Assistant that leverages multi-agent LLM systems and Retrieval-Augmented Generation (RAG). The assistant delivers context-aware investment guidance, real-time market insights, and portfolio analysis tailored to each user. Designed for scale, it simplifies complex financial concepts and empowers beginners to make informed decisions.
Accelerate marketing efforts with ContentAlchemy, an AI-powered content creation platform that leverages multi-agent LLM systems to generate high-quality blogs, LinkedIn posts, visuals, and research-driven content. The system uses intelligent agent orchestration to ensure SEO optimization, brand voice consistency, and platform-specific formatting. Designed for creators and businesses, it enables scalable, on-demand content production across multiple formats and channels.
Transform raw call data into actionable insights with an AI-powered Voice-to-Insights system that leverages multi-agent LLMs and speech-to-text technology. The system automatically transcribes, summarizes, and quality-checks support calls, providing structured evaluations and key takeaways at scale. Designed for modern call centers, it standardizes QA processes, improves compliance, and enables faster, data-driven decision-making.
Streamline communication with an AI-powered Email Assistant that uses multi-agent LLM workflows to generate personalized, context-aware email drafts. The system intelligently detects intent, applies custom tone styling, and ensures high-quality output through review and validation agents. Built for productivity and scalability, it enables teams to draft professional emails in seconds while maintaining consistent voice and messaging.
Build an agentic system that automates DevOps workflows through four specialized agents: a Code Analyzer for security reviews, a CI/CD Monitor for deployment oversight, an Infrastructure Scaler for resource management, and an Incident Resolver for system diagnostics. Build it with LangChain, CrewAI, and OpenAI API and integrate with GitHub Actions, AWS Lambda, and containerization tools, while using vector databases and monitoring solutions.
Build an assistant that streamlines healthcare services through four specialized agents: a Symptom Checker for initial assessments, an Appointment Scheduler for EHR/EMR integration, a Medical FAQ Bot for patient queries, and an Insurance Advisor for claims guidance. Use LangChain, GPT-4, and healthcare APIs to create a system that offers comprehensive patient support while maintaining secure data management through VectorDB storage.
Build a comprehensive agentic system utilizing four specialized agents to protect applications: a Vulnerability Scanner for detecting common threats, a Code Security Analyzer for OWASP Top 10 compliance, a Log Analyzer for anomaly detection, and a Compliance Checker for regulatory standards. Use tools like LangChain, OpenAI GPT, and OWASP ZAP to ensure robust security through integrated monitoring and analysis.
Employ four specialized agents to streamline legal document processing: a Contract Analyzer for extracting key elements, a Compliance Checker for regulatory validation, a Case Law Researcher for finding precedents, and a Summary Generator for creating digestible content. Use LangChain, OpenAI, and OCR tools to offer comprehensive legal document analysis through an interactive interface.
Build a multi-agent system designed to automate supply chain processes, including inventory management, demand forecasting, and logistics tracking. The system consists of four agents: a demand forecaster using time-series ML models, an inventory manager analyzing stock levels, a logistics tracker monitoring shipments, and a procurement assistant optimizing supplier contracts. Leverage Python, TensorFlow, XGBoost, LangChain, OpenAI API, SQL/NoSQL databases, and visualization tools like Streamlit in this project.
Enhance software development with an AI-powered pull request (PR) reviewer bot that automates code reviews using Large Language Models (LLMs). This bot provides detailed feedback, identifies bugs, security vulnerabilities, and coding violations, and suggests best practices to streamline the code review process. It improves efficiency and code quality while assisting human reviewers. Integrate with GitHub/GitLab for seamless operation and use models like GPT-4 or Hugging Face Transformers for accurate code analysis. Build with React or Streamlit, and deploy using Docker and AWS for smooth execution.
Streamline the hiring process with an AI-powered assistant that automates resume screening and scoring using large language models (LLMs). This tool evaluates resumes against job descriptions, identifying strengths, weaknesses, and alignment with role requirements. It enhances ATS platforms by providing actionable feedback and recommendations to find the best-fit candidates. Integrate with tools like GPT-4, Gemini Pro, and LangChain for seamless operation. Build a user-friendly interface using React, Node.js, and MongoDB, and deploy it on the cloud with Docker and AWS.
Work on personal or professional projects of your choice. BYOP offers mentorship, structured guidance, and feedback to ensure projects are aligned with industry standards and best practices. It fosters creativity, innovation, and real-world problem-solving, enabling participants to build impactful solutions. You will receive guidance on selecting the right tools and frameworks based on project requirements.
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:
Do I need prior AI or ML experience to enroll in this course?
No, prior AI/ML experience isn’t mandatory. However, a strong foundation in software engineering and familiarity with Python/other coding languages are expected. We start with essentials before progressing to advanced Agentic AI concepts.
What kind of projects will I build in the course?
You’ll build hands-on projects like a Financial Bot, Conversational Audio Bot, and choose from 10+ Capstone options (e.g.,Finance Assistant, AI Call Center Assistant, Email Generator). These simulate real-world AI use cases and help build a portfolio for job applications.
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 software roles.
Can I bring my own project?
Absolutely. With BYOP, you can work on your unique project idea with mentor guidance, ensuring it aligns with industry best practices and makes your portfolio stand out.
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 8 hours of learning per week. This includes 60+ hours of live sessions, 30+ hours of guided project work, and 21+ hours of specialized sessions over 15 weeks. Bonus content and interview prep sessions are available for those who want to go 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 tech stack and tools will I learn?
You’ll work with 30+ industry tools including LangChain, CrewAI, LlamaIndex, Hugging Face, OpenAI APIs, LangGraph, Streamlit, Docker, and Kubernetes—tools widely used in modern AI workflows.
Is the course live or self-paced?
It’s a hybrid format, with weekly live expert-led sessions for core learning and projects, plus self-paced bonus content and career prep modules to support flexible schedules.
How is this different from a typical ML bootcamp?
This course is domain-specific for software engineers—not a generic AI training or prompt engineering course. It focuses specifically on building real-world agentic systems, integrating LLMs with production environments, and preparing for AI software engineering roles, not just research.
What support do I get during the course?
You get access to 1:1 mentoring, career coaching, resume reviews, and mock interviews. Plus, there’s ongoing support from teaching assistants, technical coaches, and peer communities.
What are the career outcomes or placement support offered?
Past learners have landed roles with average packages of over $312K. Our career team offers job targeting strategies, referrals, and personalized application help to support your transition.
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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