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Course Outline
Foundations of Agentic Systems in Production
- Agentic architectures: loops, tools, memory, and orchestration layers
- Agent lifecycle: development, deployment, and continuous operation
- Challenges associated with production-scale agent management
Infrastructure and Deployment Models
- Deploying agents within containerized and cloud environments
- Scaling patterns: horizontal versus vertical scaling, concurrency, and throttling
- Multi-agent orchestration and workload balancing
Monitoring and Observability
- Key metrics: latency, success rate, memory usage, and agent call depth
- Tracing agent activity and call graphs
- Implementing observability with Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Compliance
- Centralized logging and structured event collection
- Ensuring compliance and auditability in agentic workflows
- Designing audit trails and replay mechanisms for debugging purposes
Performance Tuning and Resource Optimization
- Minimizing inference overhead and optimizing 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
- Analyzing agent cost drivers: API calls, memory, compute, and external integrations
- Tracking agent-level costs and establishing chargeback models
- Implementing automation policies to prevent agent sprawl and idle resource consumption
CI/CD and Rollout Strategies for Agents
- Integrating agent pipelines into CI/CD systems
- Defining testing, versioning, and rollback strategies for iterative agent updates
- Executing progressive rollouts and safe deployment mechanisms
Failure Recovery and Reliability Engineering
- Designing for fault tolerance and graceful degradation
- Applying retry, timeout, and circuit breaker patterns to ensure agent reliability
- Establishing incident response and post-mortem frameworks for AI operations
Capstone Project
- Construct and deploy an agentic AI system featuring full monitoring and cost tracking
- Simulate load, assess performance, and optimize resource utilization
- Present the final architecture and monitoring dashboard to peers
Summary and Next Steps
Requirements
- A solid grasp of MLOps and production machine learning systems
- Practical experience with containerized deployments (Docker/Kubernetes)
- Knowledge of cloud cost optimization and observability tools
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
