AI Agent Orchestration: From Prototypes to Production

Master enterprise-grade multi-agent systems with monitoring, security, cost optimization, and scaling for 1000+ concurrent requests

$1.41

ROI per $1 Invested

$3,999

Course Price

150

Students (Year 1)

27-60%

Cost Savings Potential

Course Summary

Duration

15 sessions × 90-120 minutes each (22.5-30 hours total)

Format

Live cohort-based + hands-on labs + production deployment

Prerequisites

Python (advanced), distributed systems, cloud infrastructure knowledge

Completion

Certificate + Production-deployed multi-agent system

Technology Stack
Python LangGraph CrewAI AutoGen AWS/Azure/GCP Kubernetes Prometheus/Grafana

What You'll Master

  • Design scalable multi-agent system architectures (centralized, decentralized, hierarchical)
  • Choose the right framework (LangGraph, CrewAI, AutoGen) for production use cases
  • Test non-deterministic LLM systems with confidence using LLM-as-judge patterns
  • Deploy agent systems to production with CI/CD automation and blue-green deployments
  • Monitor systems with comprehensive observability (logs, metrics, traces)
  • Secure agents against prompt injection and other attacks (56% exploit rate reduction)
  • Optimize costs by 27-60% through caching, model selection, and architecture design
  • Scale to handle 1000+ concurrent requests with autoscaling and load balancing

Complete 15-Session Curriculum

Module 1: Foundations (Sessions 1-3)

Framework mastery and architectural design patterns

Learning Objectives
  • Understand multi-agent AI market landscape and enterprise adoption trends
  • Differentiate between single-agent and multi-agent architectures
  • Identify real-world use cases and ROI drivers ($1.41 per $1 invested)
  • Build your first simple multi-agent system
Key Topics
  • Klarna case study: $40M profit improvement with AI agents
  • Agent roles and specialization, communication patterns
  • Sequential vs parallel agent execution
  • Production vs prototype considerations
Hands-on Exercise

Content Creation System: Build a 2-agent system (researcher + writer) that collaborates to create content. Deploy locally and observe agent interactions.

Deliverable

Working 2-agent content creation system with state management

Module 1: Foundations (1-3)
  • Session 2: Framework Deep Dive - LangGraph, CrewAI, AutoGen
  • Session 3: Architecture Patterns for Multi-Agent Systems
Module 2: Production Engineering (4-9)
  • Session 4: State Management & Persistence
  • Session 5: Agent Communication & Tool Integration
  • Session 6: Testing & QA for Non-Deterministic Systems
  • Session 7: CI/CD Pipelines for AI Agent Systems
  • Session 8: Monitoring, Logging, & Observability
  • Session 9: Security - Prompt Injection & Defense
Module 3: Enterprise Scale (10-14)
  • Session 10: Cost Management & FinOps for AI Agents
  • Session 11: Enterprise Integration - APIs, Databases, Legacy
  • Session 12: Performance Optimization & Scaling
  • Session 13: Production Deployment Strategies
  • Session 14: Production Operations & Incident Management
Module 4: Capstone (15)
  • Session 15: Deploy Production Multi-Agent System

Capstone: Enterprise-Grade Multi-Agent System

What You'll Build

Deploy a production-ready multi-agent system demonstrating enterprise-level engineering with monitoring, security, cost optimization, and scalability.

Example: Customer Onboarding System
  • Multi-Agent Architecture: Intake, verification, provisioning, notification agents
  • Production Infrastructure: AWS/GCP with auto-scaling, load balancing
  • Monitoring: Prometheus, Grafana, distributed tracing
  • Performance: <500ms latency, 1000+ req/min, 99.9% uptime
Technologies Integrated

Python, LangGraph/CrewAI, AWS/Azure, Docker, Kubernetes, Redis, PostgreSQL, Prometheus, Grafana, Claude 3.5 Sonnet

Portfolio Value

Demonstrate production-grade engineering skills for Senior AI Engineer or Solutions Architect roles at companies like Klarna, Salesforce, or major consulting firms.

Your Instructor: Joshua Burdick

Production Engineering Credentials
  • 14 Years: Full-stack development and production engineering
  • Epic Games & Warner Bros: Built systems handling millions of concurrent users
  • Scale Expertise: Deployed distributed systems to AWS, Azure, GCP at enterprise scale

Joshua has spent 14+ years building, deploying, and operating production systems at massive scale. At Epic Games, he worked on infrastructure that serves millions of Fortnite players globally.

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