Agentic AI Engineering Program

 

Course Structure & Learning Framework

 

1. Course Information

 

Course Title	Agentic AI Engineering Program
Duration	12 weeks (3 months) — 9 weeks live instruction + 15-day final project + 15-day career workshop
Total Live Lectures	27 (3 classes per week)
Mode of Delivery	Live online classes (Zoom) supported by LMS recordings and resources
Class Schedule	Monday, Wednesday & Friday — evening sessions (timings announced per batch)
Instructors	Ali Hussain, Ume Farwa, Dr. Arham Muslim
Contact	0319-5027701 | earnify@earnify-edu.com | www.earnify-edu.com
Prerequisites	Basic computer literacy; no programming background required

 

2. Course Synopsis

 

This program is a live, structured training in the engineering of agentic AI systems — intelligent systems that plan, make decisions, use tools, and execute multi-step tasks autonomously. Students progress from AI workflow fundamentals and advanced prompt engineering to building practical agents on Make.com and n8n, including tool-calling agents, multi-agent systems, agent memory, cloud databases (Supabase), Retrieval-Augmented Generation (RAG) with vector stores, voice AI agents (ElevenLabs), and autonomous multi-workflow business systems with human-in-the-loop approval.

The program concludes with the development and deployment of a complete AI SaaS product with user management and a credits-based billing model, followed by a guided final project and a career workshop on freelancing and job hunting. The focus throughout is on understanding how intelligent systems work and building production-ready solutions rather than merely using AI tools.

 

3. Course Learning Objectives (CLOs)

 

By the end of this program, learners will be able to:

CLO	Learning Objective
CLO 1	Explain the structure and working of agent-based AI systems and distinguish automation, AI workflows, and AI agents.
CLO 2	Apply advanced prompt engineering techniques to control and guide AI behavior reliably.
CLO 3	Design and build multi-step and multi-agent AI workflows that plan and execute tasks autonomously on Make.com and n8n.
CLO 4	Develop tool-calling AI agents integrated with APIs, external services, and business applications.
CLO 5	Implement agent memory, cloud databases (Supabase), and Retrieval-Augmented Generation (RAG) for grounded, custom-knowledge agents.
CLO 6	Construct autonomous, event-driven business systems with human-in-the-loop approval, including voice-enabled agents.
CLO 7	Evaluate, debug, and harden AI agents for production using guardrails, error handling, logging, and cost control.
CLO 8	Build, deploy, and commercialize a complete AI SaaS product, and present it professionally to clients and employers.

 

4. Teaching & Learning Methodology

 

No.	Type	Implementation
1	Active learning	Students learn by doing: during live sessions they build, test, and debug AI agents through guided tasks and real-time problem solving.
2	Cooperative learning	Students collaborate in small groups to design and build agent workflows, discuss approaches, divide tasks, and review each other’s work.
3	Blended learning	Live instructor-led sessions are combined with LMS-based recordings, resources, and between-session assignments for continuous learning.
4	Project-based learning	Every module ends in a working system; assessments accumulate into one portfolio, concluding with a guided real-world final project.

 

5. Course Contents — Phase 1: Live Instruction (Weeks 1–9)

 

Module	Lectures	Topics
1. Foundations of Agentic AI	1–3	Traditional vs generative vs agentic AI; anatomy of an autonomous agent; advanced prompt engineering (6-part framework); system vs user prompts; automation vs AI workflow vs AI agent; trigger–reasoning–action–result; human-in-the-loop design.
2. AI Tools & Automation Foundations	4–6	AI tool ecosystem; Make.com vs n8n; cloud vs self-hosted automation; API credentials, integrations and security; building and testing a first complete AI automation workflow.
3. Practical AI Agents with Make.com	7–10	AI content creation agent; AI communication agent (Gmail automation, context-aware replies); agent memory and persistent data; multi-agent systems, handoffs and collaboration.
4. Advanced Multi-Agent Business Automation	11–14	Five-agent marketing campaign system; JSON-based agent communication, routing and structured data; autonomous scheduling and distribution agents (social media, WhatsApp, calendar); complete AI campaign operating system with approval points.
5. Professional Agent Engineering with n8n	15–18	n8n architecture: nodes, expressions, executions, credentials; AI research and planning agent; tool-calling AI agents (AI Agent node, tools, decision-making); advanced multi-tool AI assistant.
6. Databases, Memory & RAG	19–21	Multi-stage research assistant agents; AI knowledge vault with Supabase (tables, storage, retrieval); RAG concepts, embeddings and vector stores; importing custom datasets; grounded, hallucination-resistant knowledge agents.
7. Advanced Autonomous Business Agents	22–23	Executive AI assistant with autonomous tool selection, memory and action boundaries; autonomous project acquisition and delivery system (three connected workflows: acquisition, approval/communication, execution); event-driven agents and orchestration.
8. Voice AI Agents (ElevenLabs)	24	Text-to-speech and voice cloning; conversational voice agent design; connecting voice agents to n8n workflows; use cases: voice receptionists, support lines, booking assistants.
9. Deployment, Publishing & Commercialization	25	Publishing options: web apps, APIs, webhooks, embedded and white-label agents; hosting; pricing models: subscriptions, usage-based billing, credits systems, retainers; client ownership vs managed services.
10. Building a Real AI SaaS Product	26–27	Full-stack AI SaaS live build (Lovable frontend, n8n webhook API, Supabase user validation, credits check, AI agent, billing logic); production-grade reliability: failure modes, hallucination control, validation, retries, fallbacks, logging, guardrails, cost tracking; capstone kickoff.

 

6. Phase 2 — Final Project (15 Days)

 

Each student develops one complete, real-world agentic AI system under instructor supervision, selected from 15 professional project tracks (for example: customer support and action agent, sales and lead management agent, recruitment screening agent, research and report generation agent, appointment and booking agent, multi-agent business operations system) or a self-proposed project.

Every project must demonstrate:

• A clearly defined real-world problem
• Multi-step reasoning
• Tool/function calling with real data or services
• Structured outputs and error handling
• Memory where required
• Human approval before critical actions
• A professional working demonstration presented on Demo Day

 

7. Phase 3 — Freelancing & Job Hunting Workshop (15 Days)

 

A practical career workshop covering:

• Positioning as an AI automation specialist
• Building Upwork and Fiverr profiles and gigs
• Writing effective proposals
• Pricing services: projects, retainers, and credit-based offers
• Client communication and delivery
• CV and portfolio presentation
• LinkedIn optimization
• Interview preparation
• A personal 30-day post-graduation action plan

 

8. Assessment Structure

 

Assessment	Coverage / Focus	Weightage
Quiz 1	Agentic AI foundations, prompt engineering, and AI workflows (Modules 1–2)	5%
Quiz 2	Agents, memory, and multi-agent systems (Modules 3–4)	5%
Assignment 1	Build a complete AI-powered automation workflow	6%
Assignment 2	Design and build a multi-agent business automation system	6%
Assignment 3	Tool-calling AI assistant with at least three external capabilities	6%
Assignment 4	Database-connected knowledge agent using Supabase and RAG on custom data	6%
Assignment 5	Connected multi-workflow agent system with approval and action stages	6%
Major Project	Complete AI SaaS application with frontend, backend, database, and credits system	25%
Final Capstone	Production-ready agentic AI product, demonstrated on Demo Day	35%
Total		100%

 

Note: All assessments are cumulative — each builds toward the student’s final portfolio of 5–7 working agent systems plus one deployed capstone product. Certification is awarded on successful completion of all assessment components.

 

9. Software & Technologies Used

 

Category	Tools / Notes
LLMs	OpenAI / ChatGPT APIs, Claude, Gemini
Automation & Agent Platforms	n8n (primary), Make.com
APIs & Integration	REST APIs, webhooks; Gmail, WhatsApp, Google Calendar, Google Sheets
Knowledge & Retrieval	Supabase (cloud database), RAG, embeddings and vector store concepts
Voice AI	ElevenLabs (text-to-speech, voice cloning, voice agents)
Frontend & Deployment	Lovable (AI app builder), webhook APIs, credits and billing logic
Docs & Delivery	Earnify LMS, Zoom live sessions, session recordings

 

10. Learning Management System (LMS) Support

 

• Access to structured course modules through the LMS
• Session recordings available after live classes
• Supporting resources, templates and workflow files uploaded regularly
• Progress tracking throughout the program
• 24/7 chat support and student community for interaction and collaboration

 

11. Enrollment Information

 

This is a batch-based program with limited seats to ensure quality learning and meaningful interaction. Early enrollment is recommended for those planning to join the upcoming batch.

 

For registration and queries:
Phone / WhatsApp: 0319-5027701
Email: earnify@earnify-edu.com
Website: www.earnify-edu.com