A tailored course, built for your situation
Agentic Machine Learning Engineering: From Concept to Production
A 12-module system to architect, deploy, and scale autonomous AI agents with precision and real-world impact
$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 agentic AI systems that fail in production or underperform at scale
The situation this course is for
You're leading the design of intelligent, autonomous systems, but translating advanced concepts into reliable, secure, and scalable deployments remains inconsistent. Common frameworks lack depth in real-world integration, leaving gaps in testing, monitoring, and lifecycle management. The cost of rework is high, and stakeholder trust erodes when agents don’t behave as expected outside controlled environments.
Who this is for
Principal AI Architect or Senior Machine Learning Engineer leading the design and deployment of agentic systems in production environments
Who this is not for
Entry-level data scientists, researchers focused only on theory, or teams not yet deploying autonomous agents beyond proof-of-concept
What you walk away with
- Architect resilient, modular agentic systems using battle-tested design patterns
- Deploy AI agents with secure, auditable data handling and clear decision tracing
- Scale agent workflows across complex data pipelines without degradation
- Implement feedback loops for continuous agent learning and performance tuning
- Deliver measurable business impact through structured agent lifecycle management
The 12 modules (with all 144 chapters)
Module 1. Foundations of Agentic AI
Establish core principles of autonomy, goal decomposition, and agent-environment interaction. Define success metrics aligned with business outcomes.
12 chapters in this module
- Agent definition
- Autonomy spectrum
- Goal structures
- Task decomposition
- Environment modeling
- Action spaces
- Reward design
- State representation
- Agent types
- Use case filtering
- Risk assessment
- Ethical boundaries
Module 2. Agent Architecture Patterns
Compare and select from proven architectural blueprints including reflex, deliberative, hybrid, and hierarchical designs for production use.
12 chapters in this module
- Reactive agents
- Model-based agents
- Planner integration
- Hierarchical control
- Modular components
- State machines
- Behavior trees
- Neural agents
- Memory architectures
- Knowledge graphs
- API design
- Event loops
Module 3. Tooling and Infrastructure
Set up robust environments for agent development, testing, and deployment using containerization, orchestration, and monitoring tools.
12 chapters in this module
- Container setup
- Orchestration tools
- Agent monitoring
- Logging standards
- Version control
- CI/CD pipelines
- Testing frameworks
- Simulation environments
- Resource scaling
- Security layers
- Access controls
- Audit trails
Module 4. Data Flow and Context Management
Design efficient, secure data pipelines that feed agents with relevant, timely context while minimizing latency and drift.
12 chapters in this module
- Context windows
- Data ingestion
- Normalization methods
- Feature stores
- Metadata tagging
- Latency reduction
- Caching strategies
- Streaming pipelines
- Data contracts
- Schema evolution
- Drift detection
- Context pruning
Module 5. Secure Agent Design
Integrate data masking, access controls, and privacy-preserving techniques into agent workflows from the start.
12 chapters in this module
- Data masking
- Role-based access
- Input sanitization
- Output filtering
- Audit logging
- Encryption layers
- Policy enforcement
- Consent checks
- Anonymization
- Compliance mapping
- Risk scoring
- Incident response
Module 6. Agent Communication Protocols
Enable reliable, structured interaction between agents and systems using standardized messaging, handshakes, and error handling.
12 chapters in this module
- Message formats
- Handshake design
- Error codes
- Retries and backoff
- Event broadcasting
- Pub-sub models
- Agent discovery
- Heartbeats
- Load balancing
- Federated queries
- Cross-agent coordination
- Conflict resolution
Module 7. Decision Logic and Reasoning
Implement advanced reasoning layers including rule engines, probabilistic inference, and chain-of-thought execution.
12 chapters in this module
- Rule engines
- Probabilistic models
- Chain-of-thought
- Constraint solving
- Uncertainty handling
- Fallback logic
- Confidence scoring
- Belief updating
- Hypothesis testing
- Multi-path evaluation
- Decision trees
- Justification trails
Module 8. Learning and Adaptation
Build agents that learn from feedback, adapt to new data, and improve performance without human intervention.
12 chapters in this module
- Feedback loops
- Reinforcement learning
- Reward shaping
- Experience replay
- Policy updates
- Drift adaptation
- Self-correction
- Performance metrics
- A/B testing
- Learning triggers
- Model retraining
- Version rollback
Module 9. Testing and Validation
Apply rigorous testing protocols to validate agent behavior across edge cases, failure modes, and security boundaries.
12 chapters in this module
- Unit testing
- Integration tests
- Fuzz testing
- Boundary checks
- Security scanning
- Behavior validation
- Scenario replay
- Stress testing
- Failure injection
- Golden datasets
- Validation gates
- Test automation
Module 10. Deployment and Orchestration
Deploy agents into production with zero-downtime strategies, canary releases, and real-time observability.
12 chapters in this module
- Canary rollout
- Blue-green deployment
- Rollback protocols
- Health checks
- Scaling policies
- Orchestration tools
- Agent registration
- Dependency management
- Version control
- Traffic routing
- Observability stack
- Incident alerts
Module 11. Monitoring and Observability
Track agent performance, decision quality, and system health with purpose-built dashboards and alerting systems.
12 chapters in this module
- Performance metrics
- Decision logging
- Anomaly detection
- Latency tracking
- Error rate monitoring
- Audit trails
- Dashboard design
- Alert thresholds
- Root cause analysis
- User feedback
- System health
- Automated diagnostics
Module 12. Scaling and Lifecycle Management
Manage the full lifecycle of agents across environments, versions, and organizational units with governance and compliance.
12 chapters in this module
- Lifecycle stages
- Version governance
- Deprecation policies
- Cross-team handoff
- Documentation standards
- Compliance audits
- Scalability patterns
- Multi-tenant design
- Resource optimization
- Cost monitoring
- Agent retirement
- Knowledge transfer
How this maps to your situation
- Leading agentic AI initiatives in production environments
- Scaling autonomous systems across business units
- Ensuring secure, compliant agent deployments
- Reducing rework and improving time-to-value
Before vs. after
Before
Designing agentic systems without a structured framework, leading to inconsistent results and deployment delays
After
Confidently architecting, deploying, and scaling autonomous agents with clear design patterns and measurable impact
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 for engineers working part-time on implementation.
If nothing changes
Without a proven framework, teams risk building brittle, non-scalable agents that fail under real-world conditions, eroding trust and increasing technical debt.
How this compares to the alternatives
Unlike generic AI courses, this program focuses exclusively on agentic systems with production-grade depth, implementation templates, and real-world validation strategies.
Frequently asked
Who is this course for?
Senior machine learning engineers, AI architects, and technical leads building autonomous agent systems for production deployment.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, 30-day money-back guarantee if the course doesn't meet expectations.
$199 one-time. Approximately 3 hours per module, designed for engineers working part-time on implementation..
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