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Course Outline
Foundations of Agentic Systems in Production
- Core agentic architectures, including control loops, tool integration, memory management, and orchestration layers
- Agent lifecycle management: from development and deployment to sustained operational execution
- Operational challenges associated with managing agents at production scale for government applications
Infrastructure and Deployment Models
- Implementation of agents within containerized ecosystems and cloud environments
- Scaling strategies: horizontal versus vertical scaling, concurrency management, and throttling protocols
- Orchestration of multi-agent systems and workload distribution optimization
Monitoring and Observability
- Essential performance indicators: latency, success rates, memory consumption, and agent call depth
- End-to-end tracing of agent activities and dependency graphs
- Implementation of observability frameworks utilizing Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Compliance
- Centralized logging infrastructure and structured event data collection
- Ensuring compliance and auditability within agentic workflow processes
- Construction of immutable audit trails and replay capabilities for diagnostic purposes
Performance Tuning and Resource Optimization
- Minimization of inference overhead and optimization of agent orchestration cycles
- Utilization of model caching and lightweight embedding techniques to accelerate data retrieval
- Execution of load testing and stress simulation scenarios for AI pipeline validation
Cost Control and Governance
- Analysis of cost drivers: API expenditures, memory utilization, compute resources, and external integration fees
- Tracking agent-level expenses and establishing internal chargeback mechanisms
- Implementation of automation policies to mitigate unauthorized agent proliferation and idle resource consumption
CI/CD and Rollout Strategies for Agents
- Integration of agent deployment pipelines into continuous integration and continuous delivery (CI/CD) frameworks
- Methodologies for testing, version control, and rollback procedures during iterative updates
- Deployment of progressive rollout techniques and secure release mechanisms
Failure Recovery and Reliability Engineering
- Architecture design for fault tolerance and graceful system degradation
- Application of retry, timeout, and circuit breaker patterns to enhance agent reliability
- Incident response protocols and post-incident analysis frameworks specific to AI operations
Capstone Project
- Development and deployment of an agentic AI system equipped with comprehensive monitoring and cost tracking capabilities for government use
- Simulation of load conditions, performance measurement, and resource usage optimization
- Presentation of final system architecture and operational dashboards to stakeholders
Summary and Next Steps
Requirements
- Proficiency in MLOps methodologies and the operational management of production-grade machine learning environments, ensuring standards for government applications are met.
- Practical experience deploying systems using containerization technologies, including Docker and Kubernetes.
- Knowledge of strategies for optimizing cloud expenditures and implementing comprehensive observability solutions.
Target Audience
- MLOps engineers
- Site Reliability Engineers (SREs)
- Engineering leadership responsible for AI infrastructure oversight
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
