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Course Outline
Foundations of Agentic Systems in Production
- Agentic architectures: loops, tools, memory, and orchestration layers
- The lifecycle of agents: development, deployment, and ongoing operation
- Challenges associated with managing agents at production scale
Infrastructure and Deployment Models
- Deploying agents within containerized and cloud environments
- Scaling strategies: horizontal versus vertical scaling, concurrency, and throttling
- Orchestrating multi-agent systems and balancing workloads
Monitoring and Observability
- Essential metrics: latency, success rates, memory usage, and agent call depth
- Tracing agent activities and visualizing call graphs
- Instrumenting observability using Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Compliance
- Implementing centralized logging and structured event collection
- Ensuring compliance and auditability within agentic workflows
- Designing audit trails and replay mechanisms to aid debugging
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
- Identifying agent cost drivers: API calls, memory, compute resources, and external integrations
- Monitoring agent-specific costs and implementing chargeback models
- Establishing automation policies to prevent agent sprawl and reduce idle resource consumption
CI/CD and Rollout Strategies for Agents
- Integrating agent pipelines into CI/CD systems
- Testing, versioning, and rollback strategies for iterative agent updates
- Implementing progressive rollouts and secure deployment mechanisms
Failure Recovery and Reliability Engineering
- Designing for fault tolerance and graceful degradation
- Applying retry, timeout, and circuit breaker patterns to enhance agent reliability
- Establishing incident response and post-mortem frameworks for AI operations
Capstone Project
- Building and deploying an agentic AI system with comprehensive monitoring and cost tracking
- Simulating loads, assessing performance, and optimizing resource utilization
- Presenting 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 using Docker and 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
