Course Outline
Foundations of Agentic Systems in Production
- Agentic architectures: 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 Models
- Deploying agents across containerized and cloud environments.
- Scaling strategies: comparing horizontal and vertical scaling, along with concurrency and throttling mechanisms.
- Orchestration of multi-agent systems and workload balancing.
Monitoring and Observability
- Essential metrics: latency, success rates, memory usage, and agent call depth.
- Tracing agent activities and mapping call graphs.
- Implementing observability instrumentation with Prometheus, OpenTelemetry, and Grafana.
Logging, Auditing, and Compliance
- Establishing centralized logging and structured event collection.
- Ensuring compliance and auditability within agentic workflows.
- Designing audit trails and replay mechanisms to facilitate debugging.
Performance Tuning and Resource Optimization
- Minimizing inference overhead and refining agent orchestration cycles.
- Leveraging model caching and lightweight embeddings for accelerated retrieval.
- Conducting load testing and stress scenarios for AI pipelines.
Cost Control and Governance
- Analyzing agent cost drivers: API calls, memory consumption, compute resources, and external integrations.
- Tracking costs at the agent level and implementing chargeback models.
- Enforcing automation policies to curb agent sprawl and prevent idle resource usage.
CI/CD and Rollout Strategies for Agents
- Embedding agent pipelines into CI/CD systems.
- Developing testing, versioning, and rollback strategies for iterative agent updates.
- Executing progressive rollouts and safe deployment mechanisms.
Failure Recovery and Reliability Engineering
- Architecting for fault tolerance and graceful degradation.
- Applying retry, timeout, and circuit breaker patterns to enhance agent reliability.
- Implementing 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 loads, measuring 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-grade machine learning systems.
- Practical experience with containerized deployments, such as Docker and Kubernetes.
- Working knowledge of cloud cost optimization and observability tools.
Target Audience
- MLOps Engineers
- Site Reliability Engineers (SREs)
- Engineering Managers responsible for AI infrastructure
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3900 € + VAT*
Contact us for an exact quote and to hear our latest promotions
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
