AI AI SkillForge

Advanced AI Engineering Bootcamp

Advanced AI Engineering - Build Production AI Agents

By the end of this course, students know how to build AI Employees that can think, use tools, collaborate with other agents, remember previous interactions, automate business workflows, and be deployed in production.

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Target Audience

  • Software Engineers
  • Software Architects
  • QA Automation Engineers
  • DevOps Engineers
  • Tech Leads
  • AI Engineers
  • Startup Founders

Course Outcome

  • Build AI Agents from scratch
  • Design Multi-Agent Systems
  • Use MCP for tool interoperability
  • Implement Function Calling
  • Integrate external APIs and enterprise tools
  • Build long-term memory systems
  • Automate complete business workflows
  • Deploy production-ready AI applications

Technology stack

Production-focused tools used across agents, workflows, deployment, and monitoring.

ProgrammingPython
IDEVS Code
FrameworkLangGraph
FrameworkCrewAI
FrameworkAutoGen
MCPModel Context Protocol
LLMOpenAI, Gemini, Claude, Local Models
MemoryRedis
Vector DBChromaDB, Qdrant
BackendFastAPI
QueueCelery, Redis Queue
DatabasePostgreSQL
UIStreamlit, React
Workflown8n
DeploymentDocker
MonitoringLangfuse, OpenTelemetry
Version ControlGitHub

Modules

10 sessions, 4 hours each.

Module 1 - 4 Hours

AI Engineering Foundations

Evolution from chatbots to AI Employees, assistant vs agent, agent vs workflow engine, agent components, lifecycle, architectures, enterprise AI architecture, and AI engineering principles.

Demo: Build a simple autonomous AI Agent Lab: Create your first Agent Assignment: Create an Email Assistant Agent
Module 2 - 4 Hours

AI Agents

Agent planning, reasoning, reflection, self-correction, goal-based agents, reactive agents, deliberative agents, and autonomous decision making.

Agent Loop: Observe -> Think -> Plan -> Act -> Learn Lab: Build a Travel Agent Assignment: Expense Approval Agent
Module 3 - 4 Hours

Function Calling

Why function calling matters, JSON Schema, tool selection, structured outputs, validation, error handling, retry strategies, and security.

Hands-on: Connect agents to weather API, calculator, database, email, and calendar Project: Restaurant Booking Agent
Module 4 - 4 Hours

Tool Integration

Connect AI to SQL, REST APIs, GraphQL, Gmail, Slack, Teams, WhatsApp, Google Drive, Excel, PDFs, and GitHub.

Lab: Agent that reads Gmail, creates calendar events, updates Excel, and sends WhatsApp messages Project: Office Productivity Agent
Module 5 - 4 Hours

MCP - Model Context Protocol

Why MCP exists, MCP architecture, clients, servers, resources, tools, prompts, security, authentication, and enterprise use cases.

Hands-on: Build an MCP server that exposes database, files, calculator, and company knowledge Project: Enterprise Knowledge MCP Server
Module 6 - 4 Hours

Memory Systems

Short-term, long-term, episodic, semantic, and working memory, plus conversation memory, knowledge memory, business memory, user preferences, compression, and retrieval.

Technologies: Redis, vector database, SQLite Project: Personal AI Assistant that remembers users
Module 7 - 4 Hours

Multi-Agent Systems

Why multiple agents, coordinator, worker, reviewer, planner, research agents, communication, delegation, conflict resolution, and parallel execution.

Lab: Build a research team with writer, reviewer, and editor agents Project: Blog Generation Company
Module 8 - 4 Hours

Workflow Automation

Business workflow design, state machines, approval workflows, long running tasks, event-driven architecture, background jobs, scheduling, retries, notifications, and observability.

Tools: LangGraph, n8n, FastAPI, Redis Lab: Invoice Approval Workflow Project: Employee Onboarding Workflow
Module 9 - 4 Hours

Production AI Engineering

Authentication, authorization, rate limiting, caching, streaming, monitoring, logging, evaluation, guardrails, cost optimization, scaling, and deployment.

Lab: Deploy an agent using Docker and cloud infrastructure Project: Production AI Service
Module 10 - 4 Hours

Capstone Project

Students build one enterprise-grade AI Employee for HR, procurement, hospital operations, legal, finance, customer support, sales, or research workflows.

Outcome: Production-ready agent system with source code, diagrams, MCP server, integrations, memory, workflow, monitoring, security, and demo presentation

Capstone options

Enterprise AI Employees students can build.

AI HR Recruiter AI Procurement Operator AI Hospital Operator AI Legal Assistant AI Finance Operator AI Customer Support Operator AI Sales Operator AI Research Operator

Final deliverables

What every student submits at the end.

Production-ready source code GitHub repository Architecture diagram Agent interaction sequence diagram MCP server implementation Tool integration documentation Memory architecture design Workflow diagram API documentation Docker deployment files Monitoring dashboard setup Unit and integration tests Security checklist Cost estimation report 10-15 minute demo video Final presentation

Teaching pattern

Every 4-hour session moves from business problem to production-ready practice.

  1. 0-20 min: Business problem and real-world use case
  2. 20-60 min: Core concepts with architecture diagrams
  3. 60-120 min: Live coding from scratch
  4. 120-135 min: Break
  5. 135-195 min: Guided hands-on lab
  6. 195-225 min: Production best practices, debugging, and optimization
  7. 225-240 min: Assignment, Q&A, and recap

How this course stands out

From chatbot builders to AI Employee engineers.

  1. Multiple autonomous agents
  2. Multi-agent collaboration
  3. MCP servers and clients
  4. Function calling and tool orchestration
  5. Persistent memory systems
  6. Workflow automation pipelines
  7. Production deployment, monitoring, and security