Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Agentic Systems in Production
- Agentic architectures: covering loops, tools, memory, and orchestration layers
- The agent lifecycle: spanning development, deployment, and continuous operation
- Key challenges in managing production-scale agents
Infrastructure and Deployment Models
- Deploying agents across containerized and cloud-based environments
- Scaling strategies: comparing horizontal vs vertical scaling, concurrency, and throttling
- Orchestrating multi-agent systems and balancing workloads
Monitoring and Observability
- Critical metrics: tracking latency, success rates, memory usage, and agent call depth
- Tracing agent activity and mapping call graphs
- Implementing observability using Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Compliance
- Implementing centralized logging and structured event collection
- 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 to accelerate retrieval
- Conducting load testing and stress simulations for AI pipelines
Cost Control and Governance
- Analyzing agent cost drivers: API calls, memory, compute resources, and external integrations
- Monitoring agent-level expenditures and implementing chargeback models
- Establishing automation policies to prevent agent sprawl and reduce idle resource consumption
CI/CD and Rollout Strategies for Agents
- Embedding agent pipelines into CI/CD workflows
- Executing testing, versioning, and rollback strategies for iterative agent updates
- Implementing progressive rollouts and secure deployment mechanisms
Failure Recovery and Reliability Engineering
- Designing systems 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
- Developing and deploying an agentic AI system equipped with comprehensive monitoring and cost tracking
- Simulating load conditions to measure performance and optimize resource usage
- Presenting the final architecture and monitoring dashboards to peers
Summary and Next Steps
Requirements
- A solid grasp of MLOps and production machine learning systems
- Hands-on experience with containerized deployments using Docker/Kubernetes
- Proficiency with cloud cost optimization and observability tooling
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
