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Course Outline
Production Foundations of Agentic Systems
- Agentic architectures: loops, tools, memory, and orchestration layers
- Agent lifecycle: from development and deployment to continuous operation
- Key challenges in managing agents at production scale
Infrastructure and Deployment Strategies
- Deploying agents within containerized and cloud environments
- Scaling approaches: 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
- Creating audit trails and replay mechanisms for effective debugging
Performance Tuning and Resource Efficiency
- Minimizing inference overhead and refining agent orchestration cycles
- Utilizing model caching and lightweight embeddings for enhanced retrieval speed
- Conducting load testing and stress analysis for AI pipelines
Cost Governance and Control
- Identifying cost drivers: API calls, memory, compute, and external integrations
- Monitoring agent-specific costs and establishing chargeback models
- Implementing automation policies to curb agent sprawl and idle resource usage
CI/CD and Rollout Tactics for Agents
- Incorporating agent pipelines into CI/CD frameworks
- Testing, versioning, and rollback strategies for iterative agent enhancements
- Progressive rollouts and secure deployment methods
Failure Recovery and Reliability Engineering
- Architecting 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
- Construct and deploy an agentic AI system with comprehensive monitoring and cost tracking
- Simulate load, assess performance, and refine resource consumption
- Present the final architecture and monitoring dashboard to peers
Recap and Future Steps
Requirements
- Solid grasp of MLOps and production-grade machine learning systems
- Proficiency in containerized deployments (Docker/Kubernetes)
- Knowledge of cloud cost optimization and observability tools
Intended Audience
- MLOps Engineers
- Site Reliability Engineers (SREs)
- Engineering Managers responsible for 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
