A tailored course, built for your situation
Architecting AI Systems: From LLMs to Agentic Workflows
A 12-module mastery path for builders scaling intelligent systems
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Building AI systems that don’t scale, break in production, or fail under real-world complexity?
The situation this course is for
Most AI practitioners start strong but stall when moving from prototypes to production. Models behave differently under load, workflows collapse without proper orchestration, and multi-cloud deployments introduce hidden failure points. The gap isn't knowledge, it's structured implementation. Without a clear path from concept to resilient system, even advanced builders waste cycles on avoidable rework.
Who this is for
Technical leaders designing AI platforms, LLM applications, or agent-based systems, especially those transitioning from algorithmic work to full-stack architecture.
Who this is not for
This is not for beginners, passive learners, or those only interested in theoretical AI. It’s not for teams seeking vendor solutions or off-the-shelf tools.
What you walk away with
- Design production-grade AI systems with confidence
- Implement reliable agentic workflows across environments
- Architect robust multi-cloud AI deployments
- Apply ontologies to improve model reasoning and traceability
- Ship faster using proven templates and structured playbooks
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI System Design
Establish core principles for building maintainable, scalable AI systems. Cover architectural patterns, failure modes, and the shift from model-centric to system-centric thinking. Introduce key tradeoffs in latency, cost, and reliability.
12 chapters in this module
- System vs model thinking
- Defining AI components
- Latency cost tradeoffs
- Failure mode analysis
- Scalability patterns
- Designing for observability
- Choosing cloud strategy
- Data flow modeling
- State management basics
- Security by design
- Versioning workflows
- Architecture decision records
Module 2. LLM Integration Patterns
Master practical integration of large language models into applications. Explore prompt chaining, context window management, and reliability techniques. Learn how to avoid common pitfalls in token usage and response parsing.
12 chapters in this module
- Prompt chaining methods
- Context window limits
- Response parsing rules
- Token cost control
- Fallback strategies
- Model routing logic
- Input sanitization
- Output validation
- Rate limit handling
- Caching responses
- Embedding pipelines
- Fine-tuning triggers
Module 3. Agentic Workflow Orchestration
Design reliable agent teams that collaborate under uncertainty. Cover task decomposition, handoff protocols, and error recovery. Implement workflows that adapt to changing conditions without manual intervention.
12 chapters in this module
- Agent role definition
- Task decomposition
- Handoff protocols
- Error recovery paths
- State persistence
- Dynamic routing
- Agent memory
- Tool selection
- Permission layers
- Audit trails
- Load balancing
- Timeout management
Module 4. Ontology-Driven AI
Use structured knowledge to improve AI reasoning and consistency. Implement domain-specific ontologies that guide model behavior and improve traceability across decisions.
12 chapters in this module
- Knowledge graph basics
- Entity relationship maps
- Schema alignment
- Inference constraints
- Context grounding
- Semantic validation
- Concept hierarchies
- Rule injection
- Dynamic taxonomies
- Query expansion
- Feedback loops
- Versioned schemas
Module 5. Multi-Cloud Deployment Strategies
Deploy AI systems across cloud providers without vendor lock-in. Learn configuration patterns, cost optimization, and failover mechanisms that ensure uptime and flexibility.
12 chapters in this module
- Provider-agnostic design
- Cost benchmarking
- Failover triggers
- Cross-cloud networking
- Credential isolation
- Region selection
- Data residency rules
- Egress cost control
- Load distribution
- Monitoring parity
- Deployment pipelines
- Rollback protocols
Module 6. Production Readiness
Transition from prototype to production with confidence. Cover testing strategies, canary releases, and performance baselines. Ensure systems behave as expected under real-world load.
12 chapters in this module
- Canary release patterns
- Performance baselines
- Load testing
- Error budgeting
- Chaos engineering
- Logging standards
- Alert thresholds
- Incident playbooks
- Security scanning
- Compliance checks
- Rollback readiness
- Post-mortem culture
Module 7. Observability & Monitoring
Implement deep visibility into AI systems. Track model drift, latency spikes, and workflow failures. Build dashboards that reveal root causes, not just symptoms.
12 chapters in this module
- Metric selection
- Log correlation
- Trace propagation
- Drift detection
- Anomaly thresholds
- Dashboard design
- Alert fatigue reduction
- Root cause mapping
- Sampling strategies
- Cost-aware monitoring
- User impact scoring
- Automated diagnostics
Module 8. Security & Compliance
Secure AI systems against data leaks, prompt injections, and unauthorized access. Implement controls that meet compliance standards without sacrificing agility.
12 chapters in this module
- Input validation
- Prompt injection defense
- Data masking
- Access controls
- Audit logging
- Model watermarking
- Redaction rules
- Policy enforcement
- Third-party risk
- Penetration testing
- Data retention
- Incident response
Module 9. Scaling AI Infrastructure
Grow AI systems efficiently as demand increases. Optimize resource usage, manage distributed workloads, and avoid bottlenecks in data pipelines and inference layers.
12 chapters in this module
- Auto-scaling rules
- Inference optimization
- Batch processing
- Queue management
- Resource pooling
- Cold start reduction
- Distributed caching
- Model sharding
- Data pipeline tuning
- GPU utilization
- Memory optimization
- Concurrency control
Module 10. Human-AI Collaboration
Design interfaces where humans and AI work together effectively. Structure handoffs, clarify responsibilities, and build trust through transparency.
12 chapters in this module
- Role clarity
- Handoff design
- Trust signals
- Explainability layers
- Feedback mechanisms
- Escalation paths
- Bias detection
- User control
- Confidence display
- Error communication
- Training alignment
- Performance feedback
Module 11. Continuous Improvement
Implement feedback loops that improve AI systems over time. Use real-world data to refine models, update workflows, and adapt to changing conditions.
12 chapters in this module
- Feedback collection
- Model retraining
- A/B testing
- Performance decay
- User behavior analysis
- Error pattern tracking
- Version comparison
- Automated tuning
- Data drift alerts
- Human review loops
- Update scheduling
- Backward compatibility
Module 12. Future-Proofing AI Systems
Anticipate shifts in AI capabilities and infrastructure. Design systems that evolve with new models, tools, and requirements without costly rewrites.
12 chapters in this module
- Modular design
- Abstraction layers
- API versioning
- Model interchange
- Skill portability
- Framework agnosticism
- Upgrade paths
- Dependency management
- Ecosystem monitoring
- Roadmap alignment
- Tech debt tracking
- Innovation scouting
How this maps to your situation
- Scaling AI platforms across clouds
- Orchestrating reliable agentic workflows
- Applying ontologies to improve reasoning
- Moving from prototypes to production
Before vs. after
Before
Working on AI systems that feel fragile, hard to debug, or stuck in prototype phase.
After
Shipping resilient, scalable AI platforms with confidence, backed by structured design and proven patterns.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3 hours per module, designed to be completed at your pace over 12 weeks.
If nothing changes
Without a structured approach, even advanced builders waste months on rework, debugging, and patching systems that should have been designed for scale from the start.
How this compares to the alternatives
Unlike generic AI courses, this program focuses on real-world system architecture, not just theory. It includes implementation playbooks you won’t find in MOOCs or vendor documentation.
Frequently asked
Who is this course for?
Technical leaders building AI platforms, LLM applications, or agent-based systems who need production-grade patterns.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there video content?
No, the course is entirely text-based with downloadable templates and a hand-built implementation playbook.
$199 one-time. Approximately 3 hours per module, designed to be completed at your pace over 12 weeks..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours