Master enterprise-grade multi-agent systems with monitoring, security, cost optimization, and scaling for 1000+ concurrent requests
ROI per $1 Invested
Course Price
Students (Year 1)
Cost Savings Potential
15 sessions × 90-120 minutes each (22.5-30 hours total)
Live cohort-based + hands-on labs + production deployment
Python (advanced), distributed systems, cloud infrastructure knowledge
Certificate + Production-deployed multi-agent system
Framework mastery and architectural design patterns
Content Creation System: Build a 2-agent system (researcher + writer) that collaborates to create content. Deploy locally and observe agent interactions.
Working 2-agent content creation system with state management
Customer Support System: Build classifier → specialist → response generator in all three frameworks. Compare code complexity and execution patterns.
3 implementations of same system + comparison report
Customer Onboarding System: Design multi-agent system for automated customer onboarding (10+ agents). Create architecture diagram, identify bottlenecks, plan scaling strategy.
Complete architecture design with ADR and diagrams
State management, testing, CI/CD, monitoring, security, and cost optimization
Implement stateful agents with persistent memory. Choose appropriate state storage (Redis, PostgreSQL, vector DBs). Handle state conflicts and race conditions. Design state recovery and rollback mechanisms.
Exercise: Build stateful customer service agent that remembers conversation history across sessions with state persistence, crash recovery, and audit logging.
Implement agent-to-agent communication protocols. Build tool integrations (APIs, databases, external services). Handle asynchronous communication. Design robust error handling with circuit breakers.
Exercise: Create e-commerce agent system integrating payment API, inventory DB, shipping API, email service, and analytics. Handle all failures gracefully.
Test non-deterministic LLM-based agents effectively. Implement LLM-as-judge evaluation patterns. Build regression test suites. Measure agent performance and quality metrics.
Exercise: Create comprehensive test suite with unit tests, integration tests, end-to-end tests, and LLM-based quality evaluation. Achieve 80%+ coverage.
Build CI/CD pipelines for agent deployments. Implement blue-green and canary strategies. Automate testing in deployment pipeline. Roll back failed deployments safely.
Exercise: Create GitHub Actions workflow: run tests → build Docker → deploy staging → smoke tests → production canary deployment.
Implement comprehensive monitoring. Set up distributed tracing across agents. Build real-time Grafana dashboards. Configure alerting and on-call procedures.
Exercise: Instrument multi-agent system with: request latency, token usage, error rates, cost per request. Create Grafana dashboard and PagerDuty alerts.
Understand prompt injection attack vectors (56% exploit rate). Implement layered defense strategies. Validate and sanitize inputs. Design security guardrails.
Exercise: Security audit: identify 10+ vulnerabilities in sample system, implement fixes, validate with penetration testing. Achieve <10% exploit success rate.
Cost management, enterprise systems, performance, deployment, and operations
Track and forecast LLM API costs. Implement optimization strategies (27-60% savings). Build cost dashboards and budget alerts. Design cost-aware agent architectures.
Exercise: Audit system costs, track API calls, implement caching to reduce by 40%+, set up budget alerts at $100/day threshold.
Integrate agents with enterprise systems (Salesforce, SAP, legacy DBs). Handle authentication and authorization at scale. Design data transformation pipelines. Ensure compliance (GDPR, HIPAA, SOC2).
Exercise: Build agent that reads from Salesforce, enriches with external API, writes to PostgreSQL, sends Slack notifications. Handle auth, errors, retries.
Profile and optimize agent performance (80% latency reduction). Implement horizontal scaling. Optimize token usage and API calls. Handle 1000+ concurrent requests.
Exercise: Profile system, identify top 3 bottlenecks, implement optimizations (parallel calls, caching, faster models), reduce latency by 50%+. Load test at 1000 req/min.
Deploy multi-agent systems to AWS/Azure/GCP. Implement auto-scaling and load balancing. Design disaster recovery and backup strategies. Handle multi-region deployments.
Exercise: Deploy to AWS: use ECS or Lambda, configure auto-scaling (2-10 instances), set up ALB, implement health checks and auto-recovery.
Design on-call and incident response procedures. Conduct blameless postmortems. Implement chaos engineering for resilience. Build production readiness checklists.
Exercise: Create production runbooks for common failures, on-call procedures, escalation paths. Conduct chaos experiment (kill random service).
Student presentations (15 min each): system architecture overview, live demo, monitoring dashboard walkthrough, production readiness checklist, lessons learned, Q&A.
Functionality (30%), Production Readiness (25%), Architecture (20%), Performance (10%), Presentation (10%), Documentation (5%)
Deploy a production-ready multi-agent system demonstrating enterprise-level engineering with monitoring, security, cost optimization, and scalability.
Python, LangGraph/CrewAI, AWS/Azure, Docker, Kubernetes, Redis, PostgreSQL, Prometheus, Grafana, OpenTelemetry, Claude 3.5 Sonnet
Demonstrate production-grade engineering skills valued by enterprise employers. Showcase ability to design, deploy, monitor, and operate AI systems at scale. Perfect for landing Senior AI Engineer or Solutions Architect roles at companies like Klarna, Salesforce, or major consulting firms.
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. He's dealt with every challenge you'll face in this course: scaling issues, security vulnerabilities, cost overruns, production outages, and incident management. You'll learn battle-tested patterns from someone who's been in the trenches of production engineering at the highest levels.
Expected Completion Rate
Avg Time to Deploy Capstone
ROI per $1 Invested
# Production Multi-Agent System with Observability (Session 8 Example)
from langgraph.graph import StateGraph
from opentelemetry import trace
from prometheus_client import Counter, Histogram
import structlog
logger = structlog.get_logger()
tracer = trace.get_tracer(__name__)
# Metrics
agent_calls = Counter('agent_calls_total', 'Total agent calls', ['agent_name'])
agent_latency = Histogram('agent_latency_seconds', 'Agent latency')
class MultiAgentSystem:
def __init__(self):
self.graph = StateGraph()
self.setup_agents()
@tracer.start_as_current_span("research_agent")
def research_agent(self, state):
"""Research agent with observability"""
agent_calls.labels(agent_name='research').inc()
with agent_latency.time():
logger.info("research_agent.started", state=state)
# Agent logic here
result = self.llm.invoke(state)
logger.info("research_agent.completed", tokens=result.usage)
return result
# Deploy with monitoring
if __name__ == "__main__":
system = MultiAgentSystem()
# Expose Prometheus metrics on :8000/metrics
# Send traces to Jaeger
# Log to structured JSON