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.