Build AI Agents
Switch to AI/ML/DS
7 weeks
2 Live Guided Projects, 1 Capstone Project
AI Engineering centric approach with Python-based frameworks for Software Engineers
Use-case centric approach with No-Code/Low-Code platforms for Tech Managers & other tech and product professionals
FAANG and Tier-1 AI engineers working on Generative and Agentic AI systems.
Foundations of Agentic AI, Multi-Agent Financial Systems, Optimization Strategies for AI Agents, End-to-End Capstone Project
Additional FAANG+ interview preparation for AI-enhanced tech roles
Gain mentorship and practical insights from top FAANG+ experts who are actively shaping the future of Agentic AI.
Covers Agentic AI and automation concepts necessary for all tech roles, to solve problems for any kind of business use case.
Master critical Agentic AI skills even if you have limited exposure to coding, at a pace that fits your schedule and responsibilities.
Learn to build low-code AI Agent Systems through projects guided live by instructors and showcase practical experience.
Learn how Agentic AI is transforming workflows—from decision-making to customer support—and gain the skills to lead or implement these changes in your organization.
With an NPS of 55 and an average learner rating of 4.75+, our structured, hands-on approach consistently earns high praise from tech professionals like you.
Simplify data analysis by allowing users to generate insights from datasets using natural language inputs, eliminating the need for complex queries or coding. The system will integrate a robust data pipeline to load CSV files into an SQL database using the LangChain toolkit to connect with the database. The project utilizes SQL, Python, LangChain, and Streamlit, with plans to expand to other databases and BI tools.
AI-Powered Feature Prioritization Tool
Design a tool meant to evaluate feature requests based on user impact, development effort, and business alignment. It leverages AI to analyze customer feedback and trends using LLMs, ensuring data-driven decision-making. The tool integrates with Jira and Asana to automatically generate tickets for prioritized features, streamlining the product development workflow and improving efficiency.
Customer Sentiment Analysis & Roadmap Alignment
Build a tool that leverages AI to analyze customer complaints, app reviews, and support tickets. It should be able to classify and cluster feedback into product themes, providing insights into common pain points. The system automatically generates reports that align key customer concerns with upcoming roadmap items, ensuring that product development stays focused on user needs and priorities.
AI-Driven Competitive Landscape Analysis
Build an AI-powered research assistant that monitors competitor websites, product releases, and industry news. Using retrieval-augmented generation (RAG), it will generate reports summarizing key competitive insights, including pricing strategies, feature gaps, and market trends. This enables businesses to stay ahead by making informed strategic decisions based on real-time industry analysis.
AI-Powered Stakeholder Management Bot
Build a system that will help TPMs track and manage stakeholder interactions by summarizing emails, meeting transcripts, and sentiment trends. It alerts TPMs when key stakeholder engagement scores decline, enabling proactive relationship management.
Multi-Agent AI System for Program Risk Management
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.
AI-Driven Engineering Capacity & Resource Allocation Agent
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.
Multi-Agent System for Engineering Productivity & Burnout Monitoring
Build a system that tracks engineering efficiency and detects burnout risks. It includes a workload analysis agent monitoring sprint progress, a burnout detection agent analyzing work hours and meeting fatigue, and an optimization agent suggesting workload balancing. Integrated with Jira and Slack, this CrewAI-based system will enhance team well-being and productivity.
Multi-Agent AI System for Engineering Roadmap & Strategy Planning
Automate strategic planning by designing a system that analyzes industry trends, workload, and tech stack evolution. It features agents for tracking technology trends, evaluating new frameworks, and adjusting roadmaps based on resources. Use LangGraph, OpenAI, and real-time web scraping to ensure data-driven decision-making.
AI Agent for Cloud Cost Optimization in Engineering Workloads
Automate cost tracking and optimization for AI/ML workloads with a multi-agent system that monitor cloud spend across AWS, GCP, and Azure, suggest cost-effective configurations, and alert teams to unexpected spikes. Leverage LangChain, AWS Cost Explorer API, and Terraform, to optimize cloud expenses efficiently.
Smart Retail Bot with Agent Collaboration
Build a multi-agent AI assistant for retail support. The bot should classify user queries (e.g., product info, order tracking), retrieve data from Google Sheets, and respond using OpenAI. Use modular workflows for routing, classification, and response generation. This project teaches real-world automation and task orchestration in an e-commerce context.
Lead Qualification & Outreach Engine
Create an AI-powered system that automatically qualifies inbound leads from a form or CRM, enriches the data (e.g., company size, role), and sends personalized follow-ups. This project is ideal for sales and marketing teams, showing how to streamline the top of the funnel without code.
Automated Interview Scheduler & Resume Screener
Build an AI-powered hiring assistant that automates early-stage recruitment. The system screens incoming resumes using OpenAI to extract key details (skills, experience, relevance), matches candidates to job roles, and sends personalized interview invites via Gmail with embedded Calendly scheduling links. The entire candidate journey is logged in a Notion board for easy tracking. This project helps streamline talent operations for busy HR or hiring teams using low-code tools.
Projects are subject to change as per industry inputs. Choose one of the Capstone Projects.
AI-Powered Feature Prioritization Tool
Design a tool meant to evaluate feature requests based on user impact, development effort, and business alignment. It leverages AI to analyze customer feedback and trends using LLMs, ensuring data-driven decision-making. The tool integrates with Jira and Asana to automatically generate tickets for prioritized features, streamlining the product development workflow and improving efficiency.
Customer Sentiment Analysis & Roadmap Alignment
Build a tool that leverages AI to analyze customer complaints, app reviews, and support tickets. It should be able to classify and cluster feedback into product themes, providing insights into common pain points. The system automatically generates reports that align key customer concerns with upcoming roadmap items, ensuring that product development stays focused on user needs and priorities.
AI-Driven Competitive Landscape Analysis
Build an AI-powered research assistant that monitors competitor websites, product releases, and industry news. Using retrieval-augmented generation (RAG), it will generate reports summarizing key competitive insights, including pricing strategies, feature gaps, and market trends. This enables businesses to stay ahead by making informed strategic decisions based on real-time industry analysis.
AI-Powered Stakeholder Management Bot
Build a system that will help TPMs track and manage stakeholder interactions by summarizing emails, meeting transcripts, and sentiment trends. It alerts TPMs when key stakeholder engagement scores decline, enabling proactive relationship management.
Multi-Agent AI System for Program Risk Management
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.
AI-Driven Engineering Capacity & Resource Allocation Agent
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.
Multi-Agent System for Engineering Productivity & Burnout Monitoring
Build a system that tracks engineering efficiency and detects burnout risks. It includes a workload analysis agent monitoring sprint progress, a burnout detection agent analyzing work hours and meeting fatigue, and an optimization agent suggesting workload balancing. Integrated with Jira and Slack, this CrewAI-based system will enhance team well-being and productivity.
Multi-Agent AI System for Engineering Roadmap & Strategy Planning
Automate strategic planning by designing a system that analyzes industry trends, workload, and tech stack evolution. It features agents for tracking technology trends, evaluating new frameworks, and adjusting roadmaps based on resources. Use LangGraph, OpenAI, and real-time web scraping to ensure data-driven decision-making.
AI Agent for Cloud Cost Optimization in Engineering Workloads
Automate cost tracking and optimization for AI/ML workloads with a multi-agent system that monitor cloud spend across AWS, GCP, and Azure, suggest cost-effective configurations, and alert teams to unexpected spikes. Leverage LangChain, AWS Cost Explorer API, and Terraform, to optimize cloud expenses efficiently.
Smart Retail Bot with Agent Collaboration
Build a multi-agent AI assistant for retail support. The bot should classify user queries (e.g., product info, order tracking), retrieve data from Google Sheets, and respond using OpenAI. Use modular workflows for routing, classification, and response generation. This project teaches real-world automation and task orchestration in an e-commerce context.
Lead Qualification & Outreach Engine
Create an AI-powered system that automatically qualifies inbound leads from a form or CRM, enriches the data (e.g., company size, role), and sends personalized follow-ups. This project is ideal for sales and marketing teams, showing how to streamline the top of the funnel without code.
Automated Interview Scheduler & Resume Screener
Build an AI-powered hiring assistant that automates early-stage recruitment. The system screens incoming resumes using OpenAI to extract key details (skills, experience, relevance), matches candidates to job roles, and sends personalized interview invites via Gmail with embedded Calendly scheduling links. The entire candidate journey is logged in a Notion board for easy tracking. This project helps streamline talent operations for busy HR or hiring teams using low-code tools.
Projects are subject to change as per industry inputs. Choose one of the Capstone Projects.
+ Instructors to Train You
FAQs
What is the Applied Agentic AI Tech Pathway?
This course is a 7-week, low-code program designed to teach working professionals how to build and deploy AI Agents using cutting-edge GenAI technologies. It blends theory, hands-on projects, and live mentorship to ensure career-relevant skills.
Who is this course best suited for?
It’s ideal for tech professionals who want to build AI-powered applications without deep ML/AI backgrounds.
Do I need a strong coding background to join this program?
No. This course is designed for people with limited coding exposure.
What will I learn in 7 weeks?
You’ll gain practical experience with:
What makes this program unique compared to other GenAI or LLM courses?
It focuses specifically on Agentic AI—an emerging field beyond just prompt engineering—through structured, project-driven learning with real-world application development in low-code environments.
What are AI Agents, and why should I learn to build them?
AI Agents are systems that autonomously plan, reason, and execute tasks using LLMs. They represent the next generation of AI apps—far more dynamic and capable than static chatbots or scripted workflows.
What are the prerequisites for enrollment?
Are these sessions live or self-paced?
The program includes live instructor-led sessions, live project walkthroughs, and hands-on mentoring, providing personalized support throughout.
Is this a beginner-friendly GenAI program?
Yes, it is designed as an accessible entry point to GenAI application building, especially for tech professionals new to LLMs or AI agents.
Will I receive a certificate upon completion?
Yes, you will earn a certificate from Interview Kickstart validating your skills in Applied Agentic AI development.
Can I showcase the projects from this course in my portfolio?
Absolutely. These projects are designed to be production-grade, showcasing your ability to build real-world AI solutions.
How will this course help my career?
It equips you with in-demand skills in AI agent development—future-proofing your role and opening up advanced opportunities in AI-powered software, product roles, and automation engineering.
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600+ MAANG+ Instructors
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