Agentic A.I Mastery Course
Build 8 production-ready autonomous agents and a multi-agent business operations capstone. Project-first, mentor-led training: weekly hands-on builds, deployment workshops, and portfolio deliverables that get you job- and client-ready.
- 6 weeks · Weekend cohorts · Hybrid (online + classroom)
- Instructor-led live classes + hands-on labs, weekly assignments, recorded sessions
- 8 real-world agent projects + deployable capstone + certificate
About The Course
The Agentic A.I. Mastery Course is an intensive, outcome-driven program that teaches you to design, build, test, and deploy autonomous AI agents used in real business workflows. Over six weekends you will implement eight end-to-end agent projects — from intelligent customer routing and document intelligence to RAG-powered knowledge agents and multi-agent orchestration — culminating in a production-ready capstone that integrates multiple agents into a business operations system.
The curriculum focuses on practical engineering: prompt design, tool integration, vector databases, workflow orchestration, agent safety/guardrails, monitoring, and deployment patterns. Each week includes live mentor review, code walkthroughs, and graded deliverables so you graduate with deployable code, a polished portfolio entry, and a certificate of completion.
Why This Course Stands Out
Hands-on, portfolio-first: complete 8 deployable agent projects and a capstone designed for interviews or client pitches.
Industry mentors: live guidance from practitioners who have shipped production automation and agent systems.
Toolchain breadth: practical experience with vector DBs, agent frameworks, orchestration tools, and modern LLM integrations.
Compact, career-oriented format: a six-week weekend programme for working professionals who want fast, demonstrable skill gains.
Detailed Curriculum
Build a multi-channel routing agent that automatically routes queries between sales and support. Implement decision logic, channel adapters (email/WhatsApp/live chat), authentication checks, office-hours handling and automated responses. Deliverable: working router + integration tests.
Create an autonomous content agent that generates platform-appropriate social posts using LLMs, manages scheduling, deduplicates content using embeddings, and orchestrates cross-post workflows. Deliverable: content pipeline + sample post schedule.
Develop an intelligent qualification chatbot that collects lead details, asks smart qualifying questions, answers product FAQs, and hands off context-rich leads to human agents or CRM. Deliverable: chatbot with handoff integration.
Create an agent that ingests invoices, contracts, and forms; extracts structured fields with LLM/NLP; validates and normalizes data; and stores outputs in a database for downstream workflows. Deliverable: document pipeline + extractor microservice.
Deploy an agent to monitor inboxes, classify incoming mail, draft context-aware replies, route messages to teams, and manage follow-ups. Deliverable: automated triage workflow + sample templates.
Implement a Retrieval-Augmented Generation agent: ingest knowledge articles into a vector DB, build embeddings, and expose a semantic search interface with cited answers. Deliverable: RAG demo with accuracy checks.
Build an automation pipeline that converts spreadsheet rows to curated social posts, enriches content via LLM prompts, and schedules publishing through APIs. Deliverable: ETL-to-post workflow.
Combine the agents built earlier into a unified multi-agent orchestration: job scheduling, message passing, error handling, monitoring, and a small web dashboard for operations. Deliverable: production deployable capstone with README, deploy scripts, and a demo.
Learning Outcomes
- Design and implement agentic AI systems for real business workflows.
- Build and deploy 8 agent projects that demonstrate production patterns (RAG, RPA, chatbots, document extraction).
- Integrate LLMs with external tools and services using secure, maintainable connectors.
- Use vector databases and embedding pipelines for semantic search and deduplication.
- Apply basic monitoring, observability and guardrails to agent deployments.
- Produce a portfolio-ready capstone with deploy scripts and operational documentation.
Who Should Enrol
- Developers and ML engineers who want practical experience building agentic systems.
- Product managers, founders and operations professionals aiming to automate business tasks.
- Data engineers and automation specialists who need deployment patterns and integration skills.
- Anyone with basic programming literacy who wants to transition into agent engineering.
Tools & Software Taught
You’ll work with industry-standard tooling throughout the course: modern LLM APIs, agent frameworks such as LangChain / LangGraph / AutoGen, and vector databases for RAG pipelines. Practical integration and deployment skills include REST/webhook adapters and messaging integrations, datastores, containerization and basic CI/CD with Docker, plus developer utilities like Git, Postman, ngrok and notebook-based workflows to build, test and deploy production-ready agents.
Student Reviews
This course finally helped me connect LLM theory to real production workflows. Building multiple agents end-to-end — from planning and tool calls to monitoring and deployment — made a huge difference. The projects felt like real client use cases, not demos, and I now have a clear blueprint for designing agent-based systems at work.
I joined to understand how agentic AI could be applied to business operations, and the course delivered exactly that. The structured approach to agent design, decision routing, and automation helped me confidently discuss technical trade-offs with engineering teams. The mentor feedback on projects was especially valuable.
What stood out was the emphasis on reliability and deployment, not just building agents. We covered error handling, monitoring, and safe tool use, which is rarely taught elsewhere. I was able to prototype an internal automation agent for email and document processing within weeks.
Frequently Asked Questions
A: No. Basic programming (Python) and a willingness to learn LLM concepts are sufficient. We cover core concepts and provide starter notebooks.
A: Comfortable with basic Python syntax, Git basics, and REST APIs. We provide pre-course materials to get you ready.
A: Six weeks on weekends: 16 sessions total (2 hours each). Recorded sessions available for missed classes.
A: Yes — a certificate of completion after successful project submission and assessment.
A: Graduates leave with 8 portfolio projects and a capstone; we provide placement guidance and employer-facing project summaries to aid interviews.
A: We run hybrid cohorts — online live classes plus optional in-person lab days for local participants (where available).
A: Yes — we offer installment plans and early-bird discounts. Contact admissions for details.