Become a Production-Ready Agentic AI Developer
Build AI agents that can reason, use tools, work with data, and complete multi-step tasks. Learn agent architecture, RAG, MCP, multi-agent systems, evaluation, and deployment through practical projects.
What You Will Learn?
Build intelligent AI agents from the ground up and learn how to connect them with real tools, data, APIs, and business workflows.
What Is Agentic AI?
Agentic AI systems can understand a goal, plan steps, use tools, and act with limited human intervention.
An Agentic AI Developer builds these systems and connects them to real business workflows.
- Design AI agents for real tasks
- Connect agents to tools and APIs
- Build RAG and memory systems
- Create multi-agent workflows
- Test, evaluate, and monitor agents
- Deploy agents into production
Why This Role Is Exploding in 2026?
AI Is Becoming Action-Oriented
Businesses want AI that can execute tasks, not just generate answers.
Enterprise Adoption Is Growing
Agents are being explored across software, finance, support, operations, and analytics.
New Engineering Roles Are Emerging
Agent Engineer, AI Engineer, Forward-Deployed Engineer, and AI Solutions roles are expanding around agentic systems.
Who Is This For?
Skills Covered in This Course
Agent Architecture
Understand agent loops, planning, reasoning, actions, and workflow design.
Prompt Engineering
Work with modern LLMs, structured outputs, system prompts, and model selection.
Function Calling
Give agents access to APIs, databases, search, code, and external tools.
RAG
Build knowledge retrieval, short-term memory, long-term memory, and context flows.
Agent Frameworks
Build stateful workflows and reliable agent systems using modern frameworks.
Multi-Agent Systems
Design specialist agents that collaborate, delegate, and complete complex tasks.
Mentorship Learning Model
Live Mentorship
Direct interaction with Mehul Prajapati during weekend live sessions on Zoom.
Hands-on Projects
Work on real-world AI applications, from RAG systems to multi-agent production apps.
Scenario-Based Learning
Solve enterprise-level problems using specific AI case studies and workflows.
1-on-1 Feedback
Get personalized review on your code and architecture designs via Discord.
Career Coaching
Optimize your LinkedIn, resume, and portfolio for the 2026 AI job market.
Exclusive Resources
Access proprietary prompt libraries, deployment scripts, and project templates.
Tools and Technologies You Will Master
Job Roles After Completing
Salary Potential in AI Careers (India)
Detailed Course Curriculum
A structured, fully visible syllabus — no hidden content.
Agentic AI Foundations
- Generative AI vs Agentic AI
- Agent architecture and control loops
- Goals, planning, reasoning, actions, and feedback
- Tool use and human-in-the-loop workflows
Python for AI Applications
- Data Cleaning u0026 Handling Missing Values
- SQL Queries, Joins u0026 Aggregations
- Working with APIs u0026 Real Datasets
LLMs, Prompting And Structured Outputs
- EDA Techniques u0026 Storytelling with Data
- Matplotlib u0026 Seaborn Visualizations
- Building Interactive Dashboards
Tool Calling & Function Calling
- Regression u0026 Classification Models
- Clustering u0026 Unsupervised Learning
- Model Evaluation u0026 Hyperparameter Tuning
RAG, Knowledge & Memory
- Time Series Forecasting
- Introduction to Deep Learning
- Feature Engineering at Scale
LangChain & LangGraph
- End-to-End Capstone Project
- Model Deployment Basics
- Portfolio u0026 Interview Preparation
Multi-Agent Systems
- Single-agent vs multi-agent architecture
- Agent roles, delegation, routing, and collaboration
- CrewAI and AutoGen concepts
- Build a multi-agent research and reporting system
MCP, API & Enterprise Intergrations
- Model Context Protocol fundamentals
- MCP servers, tools, resources, and clients
- API integrations and business workflow automation
- Build an agent that works across external services
Agent Evaluation
- Agent evaluation and test datasets
- Tracing, monitoring, latency, cost, and quality metrics
- Guardrails, permissions, validation, and failure handling
- Debugging unreliable agent workflows
Deployment & Projects
- FastAPI endpoints for agent applications
- Docker, environment variables, secrets, and deployment
- Capstone: end-to-end business automation agent
- GitHub portfolio, project documentation, and interview walkthrough
Course Logistics
Build Your AI Career
Don't wait for the AI revolution to replace your skills. Lead it by becoming an Agentic AI Developer.