Course Outline
Foundations of Agentic Systems in Production for Government
- Agentic architectures: loops, tools, memory, and orchestration layers
- Lifecycle of agents: development, deployment, and continuous operation
- Challenges of production-scale agent management for government
Infrastructure and Deployment Models for Government
- Deploying agents in containerized and cloud environments for government
- Scaling patterns: horizontal vs vertical scaling, concurrency, and throttling for government operations
- Multi-agent orchestration and workload balancing for government systems
Monitoring and Observability for Government
- Key metrics: latency, success rate, memory usage, and agent call depth for government oversight
- Tracing agent activity and call graphs for government audit purposes
- Instrumenting observability using Prometheus, OpenTelemetry, and Grafana in a government context
Logging, Auditing, and Compliance for Government
- Centralized logging and structured event collection for government agencies
- Compliance and auditability in agentic workflows for government operations
- Designing audit trails and replay mechanisms for debugging in a government environment
Performance Tuning and Resource Optimization for Government
- Reducing inference overhead and optimizing agent orchestration cycles for government efficiency
- Model caching and lightweight embeddings for faster retrieval in government systems
- Load testing and stress scenarios for AI pipelines in a government context
Cost Control and Governance for Government
- Understanding agent cost drivers: API calls, memory, compute, and external integrations for government budgets
- Tracking agent-level costs and implementing chargeback models for government financial management
- Automation policies to prevent agent sprawl and idle resource consumption in government operations
CI/CD and Rollout Strategies for Agents in Government
- Integrating agent pipelines into CI/CD systems for government workflows
- Testing, versioning, and rollback strategies for iterative agent updates in a government setting
- Progressive rollouts and safe deployment mechanisms for government applications
Failure Recovery and Reliability Engineering for Government
- Designing for fault tolerance and graceful degradation in government systems
- Retry, timeout, and circuit breaker patterns for agent reliability in a government context
- Incident response and post-mortem frameworks for AI operations in government agencies
Capstone Project for Government
- Build and deploy an agentic AI system with full monitoring and cost tracking for government use
- Simulate load, measure performance, and optimize resource usage in a government environment
- Present final architecture and monitoring dashboard to peers in a government setting
Summary and Next Steps for Government
Requirements
- A strong understanding of MLOps and production machine learning systems for government applications.
- Experience with containerized deployments using Docker and Kubernetes.
- Familiarity with cloud cost optimization and observability tools to enhance efficiency and accountability in public sector workflows.
Audience
- MLOps engineers for government agencies.
- Site Reliability Engineers (SREs) for government operations.
- Engineering managers overseeing AI infrastructure for government use.
Testimonials (3)
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
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Autonomous Decision-Making with Agentic AI
practical exercises
