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Course Outline
Core Principles of Agentic Systems in Production
- Agentic structures: loops, tools, memory, and orchestration layers
- Agent lifecycle: from development and deployment to ongoing operation
- Challenges associated with managing agents at production scale
Infrastructure and Deployment Frameworks
- Deploying agents across containerized and cloud-based environments
- Scaling methodologies: horizontal vs. vertical scaling, concurrency, and throttling
- Multi-agent orchestration and workload distribution
Monitoring and Observability Strategies
- Critical metrics: latency, success rates, memory consumption, and agent call depth
- Tracing agent activities and mapping call graphs
- Implementing observability using Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Compliance Standards
- Centralized logging and structured event aggregation
- Ensuring compliance and auditability within agentic workflows
- Creating audit trails and replay mechanisms for effective debugging
Performance Tuning and Resource Efficiency
- Minimizing inference overhead and refining agent orchestration cycles
- Employing model caching and lightweight embeddings to accelerate retrieval
- Conducting load testing and stress simulations for AI pipelines
Cost Governance and Control
- Analyzing agent cost factors: API usage, memory, compute resources, and external integrations
- Monitoring agent-specific costs and establishing chargeback models
- Implementing automation policies to curb agent sprawl and unused resource consumption
CI/CD and Rollout Methodologies for Agents
- Integrating agent pipelines into CI/CD ecosystems
- Testing, versioning, and rollback tactics for iterative agent improvements
- Executing progressive rollouts and secure deployment mechanisms
Failure Recovery and Reliability Engineering
- Architecting for fault tolerance and graceful degradation
- Applying retry, timeout, and circuit breaker patterns for agent stability
- Managing incident response and post-mortem analysis for AI operations
Capstone Project
- Construct and launch an agentic AI system equipped with comprehensive monitoring and cost tracking
- Simulate load, evaluate performance, and refine resource utilization
- Share the final architecture and monitoring dashboard with peers
Conclusions and Future Directions
Requirements
- Deep knowledge of MLOps and production-grade machine learning systems
- Practical experience with containerized deployments (Docker/Kubernetes)
- Proficiency with cloud cost optimization and observability tools
Target 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
