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Course Outline
Core Concepts of Agentic Systems in Live Environments
- Architectural components: loops, tooling, memory structures, and orchestration layers
- Agent lifecycle: phases of development, deployment, and continuous operation
- Key challenges in managing agents at production scale
Infrastructure and Deployment Frameworks
- Implementing agents within containerized and cloud-native settings
- Scaling strategies: horizontal vs. vertical expansion, concurrency management, and throttling
- Coordinating multi-agent interactions and workload distribution
Monitoring and Observability Practices
- Essential metrics: latency, success rates, memory consumption, and agent invocation depth
- Tracking agent activities and visualizing call graphs
- Enhancing observability with tools like Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Compliance Management
- Aggregating logs and collecting structured event data
- Ensuring compliance and auditability within agentic workflows
- Creating audit trails and replay capabilities for effective debugging
Performance Optimization and Resource Efficiency
- Minimizing inference overhead and refining agent orchestration cycles
- Utilizing model caching and lightweight embeddings to accelerate retrieval
- Conducting load and stress testing for AI pipelines
Cost Governance and Strategic Control
- Analyzing cost drivers: API usage, memory, compute resources, and third-party integrations
- Monitoring individual agent costs and applying chargeback models
- Enforcing automation policies to curb agent sprawl and eliminate idle resource usage
CI/CD and Agent Rollout Methodologies
- Embedding agent workflows into CI/CD pipelines
- Strategies for testing, versioning, and rolling back iterative agent updates
- Implementing progressive rollouts and secure deployment mechanisms
Resilience Engineering and Failure Recovery
- Building for fault tolerance and managing graceful degradation
- Applying retry, timeout, and circuit breaker patterns to ensure agent stability
- Establishing incident response and post-mortem processes for AI operations
Capstone Project
- Constructing and launching an agentic AI system with comprehensive monitoring and cost tracking
- Simulating load conditions, assessing performance, and refining resource consumption
- Presenting the final architecture and monitoring dashboards to colleagues
Conclusion and Future Directions
Requirements
- A solid grasp of MLOps and production-grade machine learning systems
- Hands-on experience with containerized environments (Docker/Kubernetes)
- Knowledge of cloud cost management and observability platforms
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
