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Course Outline
Foundations of Agentic Systems in Production
- Agentic architectures: encompassing loops, tools, memory, and orchestration layers
- The lifecycle of agents: covering development, deployment, and continuous operation
- Key challenges in managing agents at production scale
Infrastructure and Deployment Models
- Deploying agents across containerized and cloud environments
- Scaling patterns: comparing horizontal and vertical scaling, along with concurrency and throttling
- Orchestration of multiple agents and workload balancing
Monitoring and Observability
- Essential metrics: tracking latency, success rate, memory usage, and agent call depth
- Tracing agent activity and mapping call graphs
- Implementing observability instrumentation using Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Compliance
- Centralized logging and structured event collection
- Ensuring compliance and auditability within agentic workflows
- Creating audit trails and replay mechanisms for effective debugging
Performance Tuning and Resource Optimization
- Minimizing inference overhead and refining 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 establishing chargeback models
- Implementing automation policies to curb agent sprawl and idle resource consumption
CI/CD and Rollout Strategies for Agents
- Embedding agent pipelines into CI/CD systems
- Applying testing, versioning, and rollback strategies for iterative agent updates
- Executing progressive rollouts and safe deployment mechanisms
Failure Recovery and Reliability Engineering
- Building for fault tolerance and graceful degradation
- Employing retry, timeout, and circuit breaker patterns to ensure agent reliability
- Managing incident response and post-mortem frameworks for AI operations
Capstone Project
- Developing and deploying an agentic AI system with comprehensive monitoring and cost tracking
- Simulating load, assessing performance, and refining resource usage
- Presenting the final architecture and monitoring dashboard to peers
Summary and Next Steps
Requirements
- A solid grasp of MLOps and production machine learning systems
- Hands-on experience with containerized deployments (Docker/Kubernetes)
- Familiarity 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
