Agentic AI for Engineers Cohort — 7 Weeks

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: 7 Weeks
• 14 Live Sessions (2 hours each, 2 sessions per week)
• Format: Live training, hands-on builds, architecture labs, capstone
• Outcome: Portfolio-ready agentic AI system

Course Outline (Labs First → Then Concepts)

Week 1 — Foundations & Your First AI Agent

Session 1 — Build Your First AI Agent

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

Week 2 — Automation, Monitoring & Retrieval

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

Week 3 — Architecture Design & Multi-Agent Systems

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

Week 4 — Quality, Insights & Production Deployment

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

Week 5 — Capstone Build & Optimization

Session 9 — Capstone Phase 2: Build

Live Build / Lab

• Build the capstone system end to end
• Wire up agents, tools, and data sources
• Run initial test scenarios

Focus Areas

• Architecture validation
• Failure handling
• Integration testing

Session 10 — Capstone Phase 2: Optimization

Live Build / Lab

• Debug and optimize the capstone
• Add observability and evaluation
• Tune for performance and cost

Focus Areas

• Performance optimization
• Cost control
• Evaluation metrics
• Production readiness

Week 6 — Career Positioning & System Design

Session 11 — 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 12 — 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

Week 7 — Demo Day & Hiring Simulation

Session 13 — Mock Demo & Feedback

Live Format

• Run a full rehearsal of your capstone presentation
• Receive peer and mentor feedback
• Refine architecture narrative and demo flow

Session 14 — 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

1. Intelligent Customer Support Chatbot

• Build chatbot with intent detection, multi-turn conversations
• Integrate APIs and databases
• Deploy and optimize performance

2. Resume & LinkedIn Optimizer

• Parse resumes and profiles
• Generate improvement suggestions
• Build UI and deploy system

3. GitHub Portfolio Builder

• Extract project data
• Generate summaries and documentation
• Build visualization dashboard

4. 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

5. 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?

1. Developers

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

2. Architects

• Design agentic architectures
• Learn orchestration patterns
• Handle system trade-offs

3. Managers / Leaders

• Understand AI adoption
• Identify automation opportunities
• Make informed decisions

4. 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, and 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, and 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.