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.
Duration 21 hours
Course Outline
Foundations of Agentic Systems in Production
- Agentic architectures: loops, tools, memory, and orchestration layers
- Agent lifecycle management: development, deployment, and continuous operation
- Challenges associated with managing agents at production scale
Infrastructure and Deployment Models
- Deploying agents within containerized and cloud-native environments
- Scaling strategies: horizontal versus vertical scaling, concurrency management, and throttling
- Orchestrating multi-agent systems and balancing workloads
Monitoring and Observability
- Core metrics: latency, success rates, memory consumption, and agent call depth
- Tracing agent activities and visualizing call graphs
- Instrumenting observability stacks using Prometheus, OpenTelemetry, and Grafana
Logging, Auditing, and Compliance
- Implementing centralized logging and structured event collection
- Ensuring compliance and auditability within agentic workflows
- Designing comprehensive audit trails and replay mechanisms for 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 scenario analysis for AI pipelines
Cost Control and Governance
- Analyzing agent cost drivers: API calls, memory, compute, and external integrations
- Monitoring agent-specific costs and implementing chargeback models
- Establishing automation policies to prevent agent sprawl and eliminate idle resource usage
CI/CD and Rollout Strategies for Agents
- Integrating agent pipelines into existing CI/CD ecosystems
- Applying testing, versioning, and rollback strategies for iterative agent updates
- Executing progressive rollouts and ensuring safe deployment mechanisms
Failure Recovery and Reliability Engineering
- Engineering for fault tolerance and graceful degradation
- Implementing retry, timeout, and circuit breaker patterns for agent reliability
- Establishing incident response and post-mortem frameworks for AI operations
Capstone Project
- Construct and deploy an agentic AI system with comprehensive monitoring and cost tracking
- Simulate load conditions, measure performance, and optimize resource utilization
- Present the final architecture and monitoring dashboard to peers
Summary and Next Steps
Requirements
- Proficient knowledge of MLOps and production-grade machine learning systems
- Hands-on experience with containerized deployments (Docker/Kubernetes)
- Working familiarity with cloud cost optimization and observability tooling
Target Audience
- MLOps engineers
- Site Reliability Engineers (SREs)
- Engineering managers responsible for AI infrastructure
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
