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Now in Labs: Agentic AI Track

Engineering Labs for Real-World Systems.

In-depth, interactive curricula covering the hard boundaries of modern software: deterministic control loops, schema validation barriers, storage engines, and failure isolation.

Featured Curriculum

Production Agentic AI

Full Syllabus →
Live Interactive Track
~14 Hours of Study

Agentic AI

Deterministic control loops, tool interfaces, and failure isolation for autonomous workflows

A structured engineering curriculum covering the transition from simple prompt-response wrappers to deterministic execution loops, schema validation barriers, bounded recursion, and offline trajectory evaluations.

Key Architectural Invariants:
✓ Design deterministic Directed Acyclic Graph (DAG) state machines for multi-step agent workflows
✓ Enforce strict JSON Schema validation boundaries on LLM tool invocations with additionalProperties: false
✓ Implement circuit breaker governors to prevent runaway recursion and token-spend depletion
✓ Build replayable offline evaluation harnesses to detect non-deterministic agent decision degradation
Upcoming Track ~18 Hours

Data Engineering

High-throughput ingestion, physical storage engines, and transaction isolation

Upcoming track focusing on physical page layouts, LSM-Trees vs B-Trees, Write Amplification Factors, and transactional anomalies under high write load.

In Authoring
Upcoming Track ~16 Hours

AI Engineering

Inference runtime optimization, vector index mechanics, and model evaluation

Upcoming track on building production AI infrastructure: KV-cache management, quantization trade-offs, and embedding retrieval mechanics.

In Authoring
Writing

Engineering Notes

All Notes →