Engineering Deterministic AI Orchestration for Probabilistic LLMs
R4 · ORCHESTRATE13 minUpon completing this course, you will master the engineering practices required to build deterministic AI orchestration systems around probabilistic LLMs. You will learn to design robust platforms that ensure AI runs are repeatable, replayable, auditable, and governed, transforming agents into disposable executors.
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Syllabus
1. The Determinism Problem (and what 'deterministic' honestly means here)
- Understanding LLM Non-Determinism
- Engineering Determinism
2. The Core Architecture: Thin Agent, Fat Platform, Thin Skill
- Deconstructing the Architecture
- Why This Architecture Works
3. Five Rules the Platform Enforces
- Core Enforcement Principles
- State, Context, and Roles
4. Enforcement Outside the Model (The Load-Bearing Idea)
- Code-Based Enforcement
- Validating Enforcement Mechanisms
5. Fixed Phases with Exit Gates
- Structuring Workflows with Phases
- Orchestrating Phase Transitions
6. What the Platform Gives You (The Four Primitives)
- Platform Primitives for Reliability
- Orchestrating with Platform Primitives
7. Design Exercise
- Designing a Deterministic Workflow