Overview

The Agentic AI for Engineers Cohort is a hands-on program designed to help you move from traditional workflows to building intelligent, autonomous systems powered by AI agents.

Modern systems often struggle with fragile processes, manual checks, and reactive handling. This cohort focuses on solving these problems by helping you design systems that are automated, reliable, and production-ready.

What You’ll Build

AI agents to monitor systems and workflows
Automated validation and quality assurance systems
Insight generation and reporting agents
Real-world integrations with APIs, databases, and platforms

You’ll also develop:

Architecture thinking
Governance and responsible AI understanding
Production deployment skills

Cohort Structure

Duration: 6 Weeks
12 Live Sessions (2 hours each)
Format: Live training, hands-on builds, architecture labs, capstone
Outcome: Portfolio-ready agentic AI system

Course Outline (Labs First → Then Concepts)

Session 1 — Build Your First AI Agent & Foundations

Live Build / Lab

Create a simple AI agent (Python or Copilot)
Assign tasks and observe reasoning + execution
Build reusable prompt templates
Test outputs across scenarios

Concepts

What AI and Agentic AI are
Evolution: scripts → pipelines → LLM apps → agents
Tokens, context, and prompting
Retrieval basics
Safety and governance fundamentals

Session 2 — Connecting Agents to Real Systems

Live Build / Lab

Connect an agent to a database
Enable tool calling
Query real data and return structured outputs

Concepts

Agent frameworks (LangChain, AutoGen, OpenAI/Azure agents)
Tool calling architecture
External integrations
Structured outputs

Session 3 — Agent Design for Automation & Monitoring

Live Build / Lab

Break down failures into agent tasks
Design workflow logic
Monitor outputs and detect anomalies

Concepts

Agent lifecycle and task decomposition
Workflow design
Automation patterns
Replacing brittle logic

Session 4 — Intelligent Search & Retrieval Agents (RAG)

Live Build / Lab

Chunk data and generate embeddings
Store vectors and query them
Build a metadata/search agent

Concepts

RAG systems
Vector databases
Semantic retrieval
Context injection

Session 5 — Capstone Phase 1: Architecture Design Lab

Live Build / Lab

Define problem statement
Design architecture diagram
Map agent roles and workflows
Estimate cost and infrastructure

Concepts

Architecture thinking
Scoping and complexity control
MVP vs enterprise design
Risk planning

Session 6 — Multi-Agent Systems & Collaboration

Live Build / Lab

Build Planner, Executor, Critic system
Run workflows with feedback loops
Validate and improve outputs

Concepts

Multi-agent orchestration
Collaboration patterns
Feedback loops
Coordination strategies

Session 7 — AI-Driven Quality & Insights

Live Build / Lab

Build validation and insight agents
Auto-detect rules and anomalies
Generate insights from data

Concepts

Observability
Root-cause analysis
Query generation
Insight summarization

Session 8 — Production Deployment & Governance

Live Build / Lab

Deploy agent workflows
Add logging and monitoring
Implement approvals and access control

Concepts

Guardrails and evaluation
Cost control and observability
Responsible AI and compliance
Human-in-the-loop systems

Session 9 — Capstone Phase 2: Build & Optimization

Live Build / Lab

Build capstone system
Debug and optimize
Add observability and evaluation

Focus Areas

Performance optimization
Cost control
Failure handling
Architecture validation

Session 10 — Career Positioning for Agentic AI Roles

Live Build / Lab

Create AI-focused resume
Optimize LinkedIn profile
Position projects for portfolio

Concepts

Recruiter evaluation
Resume storytelling
Positioning for AI roles

Session 11 — System Design Interview Masterclass

Live Build / Lab

Design a full AI system live
Simulate interview scenarios

Concepts

Scaling AI systems
Multi-agent architectures
Trade-offs (cost vs latency, accuracy vs performance)
Observability and failure handling

Session 12 — Demo Day & Hiring Simulation

Live Format

Present capstone project
Defend architecture decisions
Receive structured feedback

Includes

Interview-style questioning
Feedback on scalability and clarity

Capstone Project Examples

Intelligent Customer Support Chatbot
Build chatbot with intent detection, multi-turn conversations
Integrate APIs and databases
Deploy and optimize performance
Resume & LinkedIn Optimizer
Parse resumes and profiles
Generate improvement suggestions
Build UI and deploy system
GitHub Portfolio Builder
Extract project data
Generate summaries and documentation
Build visualization dashboard
AI-Powered IT Support Automation System
Triage tickets using AI agents
Integrate database and monitoring
Add RAG for knowledge retrieval
Build multi-agent system (Planner, Executor, Critic)
Add QA, BI agents, and governance
Deploy with approvals and audit logs
Final demo with full system execution
AI-Powered Procurement & Vendor Intelligence Agent
Parse procurement requests
Match vendors using databases
Monitor contracts and SLAs
Use RAG for vendor intelligence
Build negotiation agents
Add compliance and approval workflows
Final demo with full procurement cycle

What You Get

Lifetime access to recordings
Architecture and prompt templates
Resume & LinkedIn toolkit
Interview preparation framework
Private community and support
Portfolio and GitHub guidance
Career mentorship

Who Should Join

Engineers and developers
Technical professionals
Architects and system designers
Managers and leaders exploring AI systems
Anyone interested in building real-world AI systems

FAQ

What are the prerequisites?

No prior AI/ML experience required

Recommended:

Basic programming understanding
Familiarity with APIs or workflows
Interest in system design

Is programming required?

Helpful but not mandatory

With coding: build full systems

Without coding:

Design architectures
Use AI tools
Understand workflows

How does this help different roles?

Developers

Build AI-powered applications
Integrate LLMs into systems
Move toward AI-native systems

Architects

Design agentic architectures
Learn orchestration patterns
Handle system trade-offs

Managers / Leaders

Understand AI adoption
Identify automation opportunities
Make informed decisions

Non-technical roles

Understand AI systems
Design workflows
Build conceptual solutions

Which cloud platform is used?

Platform-agnostic (AWS, Azure, GCP, local)
Examples may include OpenAI, Azure OpenAI, open-source tools

What tools are used?

LLMs: OpenAI, Llama, Mistral
Embeddings & vector DBs
LangChain / LangGraph
Python, FastAPI, APIs
Optional: Streamlit, BI tools

Will I build real projects?

Yes

Capstone project
Multi-agent system
Real integrations

How is this different from other courses?

Focus on agentic systems, not just prompting
Production-ready architectures
Multi-agent workflows
Evaluation and governance

Will this help in career growth?

Yes

Portfolio project
System design skills
Resume & LinkedIn optimization
Interview preparation

Target Roles

AI Engineer
GenAI Engineer
Agentic AI Engineer
ML Engineer
Applied AI Engineer
AI Architect

Key Highlight:

This is not a theory-heavy program. You will build real systems, understand how they work in production, and gain practical skills that directly translate to real-world applications.

Ankita Gulati

Testimonials