Build AI Agents
Switch to AI/ML/DS
Outcomes
Outcomes
This curriculum equips professionals to design, deploy, evaluate, and scale real-world agentic AI systems using low-code tools, enabling them to lead automation and decision-making initiatives confidently in 2026 and beyond.
Outcomes
Outcomes
This curriculum equips professionals to design, deploy, evaluate, and scale real-world agentic AI systems using low-code tools, enabling them to lead automation and decision-making initiatives confidently in 2026 and beyond.
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
+ Instructors to Train You
FAQs
Who is this course for?
This course is best suited for professionals in technical or operational roles with limited exposure to coding, who want to integrate AI into workflows without needing deep coding expertise. It’s ideal for individuals who:
Do I need any prior experience in AI or machine learning?
This course is perfect for people with limited exposure to coding. No prior experience in AI, ML, or software engineering is required. If you’re comfortable using tools like spreadsheets or basic automation tools, you’re ready to start.
What if I’m not a developer—can I still build AI agents?
Yes! This course is built for people with limited exposure to coding. You’ll use low-code and no-code tools like LangGraph, CrewAI, and n8n to build powerful AI systems without writing traditional code.
What tools and platforms will I learn in this course?
You’ll gain hands-on experience with leading low-code agentic AI tools including LangGraph, CrewAI, LangChain, n8n, Streamlit, and LLM APIs. You’ll also explore vector databases and multi-agent orchestration systems.
What kind of projects will I build?
Projects include building intelligent assistants for lead qualification, resume screening, HR automation, customer support bots, and business insight agents. All are deployable, real-world systems.
How is this course different from a traditional AI or ML course?
Unlike traditional ML courses that focus on model training and theory, this course focuses on applied AI—specifically agentic systems—and teaches you how to use tools that bring these systems to life, fast and with minimal code.
Is this a self-paced course or are there live sessions?
This is a live course. You’ll participate in guided project walkthroughs and hands-on lab sessions.
Will I get to work with instructors or mentors?
Yes, you’ll learn from instructors with deep experience in applied AI and low-code platforms. You’ll also get mentorship on projects and career guidance.
How much time do I need to commit each week?
What will I be able to do after completing the course?
You’ll be able to design, build, and deploy AI agents for business use cases in your domain. You’ll also develop a portfolio of intelligent automation projects to showcase your skills.
Will I get a certificate?
Yes, you’ll receive an Interview Kickstart certification upon successful completion, validating your ability to build and deploy agentic AI systems using low-code tools.
What if I fall behind or can’t attend a live session?
All live sessions are recorded and available on-demand. You can catch up anytime, and support is available through office hours and community channels.
Is there any technical support available during the course?
You get access to:
How do I enroll and when does the next cohort start?
You can apply through the Interview Kickstart website. New cohorts start every month, and spots are limited to maintain a high-quality learning experience.
Time Zone:
Join 25,000+ tech professionals who’ve accelerated their careers with cutting-edge AI skills
25,000+ Professionals Trained
₹23 LPA Average Hike 60% Average Hike
600+ MAANG+ Instructors
Webinar Slot Blocked
Register for our webinar
Learn about hiring processes, interview strategies. Find the best course for you.
Time Zone: Asia/Kolkata
Time Zone: Asia/Kolkata
Hands-on AI/ML learning + interview prep to help you win
Explore your personalized path to AI/ML/Gen AI success
