Cohort Description

The Agentic AI for Data & Analytics Engineers Cohort is a hands-on program designed to help data professionals evolve from building pipelines and dashboards to designing intelligent, autonomous data systems.

Modern teams often struggle with fragile pipelines, manual data quality checks, and reactive monitoring. This cohort shows you how to build AI agents that automate data workflows, improve reliability, and accelerate insights.

Through live builds and real-world use cases, you will create production-style agents that:

• Monitor pipelines

• Validate data

• Generate insights

• Interact with real databases

Along the way, you’ll develop:

• Architecture thinking

• Governance awareness

• Deployment skills

This will enable you to design and deliver enterprise-ready agentic solutions with confidence.

Cohort Structure & Schedule

Duration

• 6 Weeks (Live Sessions)

Sessions

• 12 Live Sessions

• 2 hours each

• Includes Q&A and live builds

Format

• Live Training

• Hands-on Builds

• Architecture Labs

• Capstone Project

Project

• Production-ready agentic data system portfolio project

Course Structure / Session Outline

SESSION 1 — Build Your First AI Agent + Prompting Foundations

You Will Build

• A simple working AI agent in Python or Copilot

• A reusable prompt template powering a Data Quality Agent

Live Build

• Create and run your first agent

• Assign a task and observe reasoning + execution

• Create prompts that audit and validate datasets

• Test outputs across edge cases

Concepts

• What AI & Agentic AI are

• Evolution: scripts → pipelines → LLM apps → autonomous agents

• Why agents transform data engineering productivity & quality

• Tokens, context & prompting mechanics

• Retrieval basics

• Enterprise safety & governance fundamentals

SESSION 2 — Connect Agents to Real Data Systems

You Will Build

• An LLM agent connected to a SQL database

Live Build

• Tool calling setup

• Query real data via the agent

• Return structured results

Concepts

• LangChain, AutoGen, OpenAI/Azure Agents overview

• Tool calling architecture

• External system integrations

• Structured output handling

SESSION 3 — Agent Design for ETL & Monitoring

You Will Build

• A troubleshooting agent blueprint for ETL failures

• AI-powered pipeline monitor logic

Live Build

• Break down pipeline failures into agent tasks

• Design workflow logic

• Monitor pipeline outputs

• Detect anomalies & trigger actions

Concepts

• Single vs multi-agent design

• Agent lifecycle & task decomposition

• Workflows for ETL & monitoring

• Agent-driven pipeline auditing

• Replacing brittle business logic

SESSION 4 — Metadata Search Agent (RAG)

You Will Build

• A metadata search agent using embeddings + vector storage

Live Build

• Chunk metadata

• Generate embeddings

• Store vectors

• Query lineage & schema information

Concepts

• Enterprise RAG patterns

• Vector stores & semantic retrieval

• Embedding strategies

• Context injection patterns

SESSION 5 — Capstone Phase 1: Architecture Design Lab

You Will Build

• Capstone project blueprint

Live Build

• Define problem statement

• Create architecture diagram

• Map agent roles & workflow logic

• Design data flow

• Estimate cost & infrastructure

Concepts

• Translating business problem → agentic solution

• Architecture thinking

• Scoping & complexity control

• MVP vs enterprise build

• Risk planning

✔ Outcome: Approved architecture before coding

SESSION 6 — Multi-Agent Collaboration System

You Will Build

A 3-agent system:

• Planner

• Executor

• Critic

Live Build

• Input: business requirement

• Planner generates workflow

• Executor builds solution

• Critic validates and improves

• Produce tested & documented pipeline

Concepts

• Planner / Executor / Critic patterns

• Multi-agent collaboration workflows

• Feedback loops

• Coordination strategies

SESSION 7 — AI-Driven Data Quality & BI Companion

You Will Build

• Intelligent Data Quality Analyst Agent

• BI Companion Agent

Live Build

• Auto-detect data quality rules

• Validate datasets

• Generate root-cause analysis

• Convert business questions → SQL queries

• Produce insights & visual summaries

Concepts

• Automated validation & RCA

• Intelligent alerting & observability

• Query generation logic

• Insight summarization

• Business requirement interpretation

SESSION 8 — Deploy Agents to Production + Governance

You Will Build

• A deployed agent workflow

• Guardrails + approval flows

Live Build

• Deploy using Azure Container Apps (or similar)

• Add logging & monitoring

• Implement approval gates

• Design restricted access workflows

• Human-in-the-loop integration

Concepts

• Evaluation & guardrails

• Cost control & observability

• Production deployment patterns

• Responsible AI & governance

• Data privacy & compliance

• Risk mitigation patterns

SESSION 9 — Capstone Phase 2: Build Sprint & Review

You Will

• Build your capstone system live

• Debug implementation issues

• Improve performance

• Add governance & guardrails

Focus Areas

• Observability

• Evaluation metrics

• Failure handling

• Cost review

• Performance optimization

✔ Structured implementation support

✔ Architecture validation

SESSION 10 — Resume, LinkedIn & Positioning for Agentic AI Roles

You Will Build

• A high-impact AI Engineer resume

• Optimized LinkedIn profile aligned to Agentic AI roles

Live Build

• Convert capstone work into impact-driven resume bullets

• Rewrite experience for AI/Agentic positioning

• Optimize LinkedIn headline & About section

• Structure Featured section with project demos

• Portfolio positioning for GitHub

Concepts

• How recruiters evaluate AI profiles

• Resume storytelling for LLM & agentic systems

• Impact-first resume framework

• Positioning for AI Engineer / Applied AI / GenAI roles

• Avoiding generic “LLM buzzword” resumes

Outcome

• AI-focused resume

• LinkedIn optimized for recruiter search

• Capstone positioned as flagship portfolio project

SESSION 11 — AI Engineer System Design Interview Masterclass

You Will Build

• End-to-end AI system design solution live

Live Build

• Design a production-ready agentic data system

• Handle scaling, cost, security & governance

• Conduct mock whiteboard simulation

Concepts

• Designing RAG systems at scale

• Multi-agent orchestration architecture

• Latency vs cost tradeoffs

• Guardrails & evaluation frameworks

• Observability & monitoring architecture

• Failure handling & fallback strategies

Interview Practice

• Design a data quality AI system

• Design a metadata intelligence assistant

• Design a multi-agent analytics system

Outcome

• Structured system design framework

• Confidence for AI Engineer interviews

• Enterprise-grade thinking

SESSION 12 — Final Demo Day & Hiring Simulation

Live Format

• 10-minute project presentation

• 10-minute system design questioning

• 5-minute hiring-style feedback

Simulation Includes

• Real AI Engineer interview environment

• Technical + architecture defense

• Feedback on clarity, scalability & production readiness

Complimentary Inclusions

• Session Recordings (Lifetime Access)

• Capstone Architecture & Prompt Templates

• Resume & LinkedIn Optimization Toolkit

• AI Engineer Interview Preparation Framework

• Private Community & Expert Support

• Portfolio & GitHub Positioning Guidance

• Career Mentorship & Hiring Visibility Support

Ankita Gulati

Testimonials