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Course Outline
Foundations of Production-Ready Agentic Systems
- Agentic architectures: loops, tools, memory, and orchestration layers
- Agent lifecycle: development, deployment, and continuous operation
- Challenges in managing agents at production scale
Infrastructure and Deployment Strategies
- Deploying agents within containerized and cloud-based environments
- Scaling patterns: horizontal vs. vertical scaling, concurrency, and throttling
- Multi-agent orchestration and workload distribution
Monitoring and Observability
- Critical metrics: latency, success rates, memory consumption, and agent call depth
- Tracing agent activities and visualizing call graphs
- Implementing observability with Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Compliance
- Centralized logging and structured event aggregation
- Ensuring compliance and auditability in agentic workflows
- Developing audit trails and replay mechanisms for effective debugging
Performance Tuning and Resource Optimization
- Minimizing inference overhead and refining agent orchestration cycles
- Utilizing model caching and lightweight embeddings for accelerated retrieval
- Conducting load testing and stress scenarios for AI pipelines
Cost Control and Governance
- Identifying agent cost drivers: API calls, memory, compute resources, and external integrations
- Monitoring agent-level expenses and establishing chargeback models
- Implementing automation policies to curb agent sprawl and idle resource usage
CI/CD and Rollout Strategies for Agents
- Integrating agent pipelines into CI/CD workflows
- Testing, versioning, and rollback strategies for iterative agent updates
- Progressive rollouts and secure deployment mechanisms
Failure Recovery and Reliability Engineering
- Designing for fault tolerance and graceful degradation
- Applying retry, timeout, and circuit breaker patterns for agent stability
- Incident response and post-mortem frameworks for AI operations
Capstone Project
- Build and deploy an agentic AI system with comprehensive monitoring and cost tracking
- Simulate load, assess performance, and refine resource utilization
- Present the final architecture and monitoring dashboard to peers
Summary and Next Steps
Requirements
- A robust understanding of MLOps and production-grade machine learning systems
- Practical experience with containerized deployments (Docker/Kubernetes)
- Working knowledge of cloud cost optimization and observability tooling
Target Audience
- MLOps Engineers
- Site Reliability Engineers (SREs)
- Engineering Managers overseeing AI infrastructure
21 Hours
Testimonials (3)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
Good mixvof knowledge and practice
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Agentic AI for Enterprise Applications
The mix of theory and practice and of high level and low level perspectives
