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Course Outline
Foundations of Agentic Systems in Production
- Agentic architecture components: loops, tools, memory, and orchestration layers
- The agent lifecycle: from development and deployment to continuous operation
- Key challenges in managing agents at production scale
Infrastructure and Deployment Strategies
- Deploying agents within containerized and cloud-based environments
- Scaling methodologies: horizontal versus vertical scaling, concurrency, and throttling
- Orchestration of multi-agent systems and workload distribution
Monitoring and Observability
- Essential metrics: latency, success rates, memory consumption, and agent call depth
- Tracing agent activities and visualizing call graphs
- Implementing observability through Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Compliance
- Centralized logging and structured event aggregation
- Ensuring compliance and auditability within agentic workflows
- Creating audit trails and replay mechanisms to facilitate debugging
Performance Tuning and Resource Optimization
- Minimizing inference overhead and refining agent orchestration cycles
- Utilizing model caching and lightweight embeddings for enhanced retrieval speeds
- Conducting load testing and stress scenario analysis for AI pipelines
Cost Governance and Control
- Analyzing cost drivers for agents: API calls, memory, compute resources, and external integrations
- Monitoring agent-specific expenses and applying chargeback models
- Implementing automated policies to curb agent sprawl and eliminate idle resource usage
CI/CD and Rollout Tactics for Agents
- Embedding agent pipelines into CI/CD ecosystems
- Strategies for testing, versioning, and rolling back iterative agent updates
- Executing progressive rollouts and secure deployment mechanisms
Failure Recovery and Reliability Engineering
- Architecting for fault tolerance and graceful degradation
- Applying retry, timeout, and circuit breaker patterns to ensure agent reliability
- Establishing incident response and post-mortem frameworks for AI operations
Capstone Project
- Constructing and deploying an agentic AI system with comprehensive monitoring and cost tracking
- Simulating load, assessing performance, and optimizing resource consumption
- Presenting the final architecture and monitoring dashboard to peers
Recap and Future Directions
Requirements
- A robust grasp of MLOps and production machine learning systems
- Hands-on experience with containerized deployments (Docker/Kubernetes)
- Proficiency with 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
