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Course Outline
Foundations of Agentic Systems in Production
- Agentic architectures: loops, tools, memory, and orchestration layers.
- The lifecycle of agents: development, deployment, and continuous operation.
- Challenges associated with managing agents at production scale.
Infrastructure and Deployment Models
- Deploying agents within containerized and cloud environments.
- Scaling patterns: horizontal vs. vertical scaling, concurrency, and throttling.
- Multi-agent orchestration and workload balancing.
Monitoring and Observability
- Key metrics: latency, success rate, memory usage, and agent call depth.
- Tracing agent activity and call graphs.
- Instrumenting observability using Prometheus, OpenTelemetry, and Grafana.
Logging, Auditing, and Compliance
- Centralized logging and structured event collection.
- Compliance and auditability within agentic workflows.
- Designing audit trails and replay mechanisms for debugging.
Performance Tuning and Resource Optimization
- Reducing inference overhead and optimizing agent orchestration cycles.
- Model caching and lightweight embeddings for faster retrieval.
- Load testing and stress scenarios for AI pipelines.
Cost Control and Governance
- Understanding cost drivers for agents: API calls, memory, compute, and external integrations.
- Tracking agent-level costs and implementing chargeback models.
- Automation policies to prevent agent sprawl and idle resource consumption.
CI/CD and Rollout Strategies for Agents
- Integrating agent pipelines into CI/CD systems.
- Testing, versioning, and rollback strategies for iterative agent updates.
- Progressive rollouts and safe deployment mechanisms.
Failure Recovery and Reliability Engineering
- Designing for fault tolerance and graceful degradation.
- Retry, timeout, and circuit breaker patterns for agent reliability.
- Incident response and post-mortem frameworks for AI operations.
Capstone Project
- Build and deploy an agentic AI system with comprehensive monitoring and cost tracking.
- Simulate load, measure performance, and optimize resource usage.
- Present the final architecture and monitoring dashboard to peers.
Summary and Next Steps
Requirements
- Solid understanding of MLOps and production machine learning systems.
- Experience with containerized deployments (Docker/Kubernetes).
- Familiarity 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
