Pick the work you want to do.
Each path is an ordered route through the curriculum. Visual stories open here; the remaining lessons link to the original.
Agent Skills Engineering
Build, route, secure, evaluate, package, and verify Agent Skills in real hosts.
Agent Systems Engineering
Engineer tool-using agent loops with explicit context, memory, orchestration, safety, evaluation, and production control.
AI Data Systems
Build reliable data, feature, embedding, retrieval, evaluation, and observability pipelines for AI systems.
Developer Experience and Education
Build credible integrations, examples, and reusable agent packages, then turn developer friction into clearer tools and teaching.
AI Evaluation and Reliability
Measure model and agent behavior, expose failure modes, instrument the runtime, and build release and incident controls around evidence.
LLM Product Engineering
Turn language-model capability into evaluated, safe, cost-aware product behavior that can survive production traffic.
Building and Deploying AI Applications
Build an AI feature from prompt and structured output through retrieval, evaluation, production serving, observability, and release.
Customer AI Deployment
Discover a customer workflow, reduce its riskiest assumptions, and carry a useful AI system through measurement and rollout.
Model Context Protocol (MCP)
Build, secure, verify, and operate stateless MCP systems from JSON-RPC envelopes through conformance release gates.
Product Judgment and Delivery
Turn evidence into outcomes, assumptions, testable slices, executable specifications, measurement plans, and owned feedback loops.
Software Engineering Fundamentals
Build the repository, debugging, testing, interface, security, release, and operational foundations AI systems depend on.
Agent-Assisted Engineering
Frame, plan, execute, delegate, verify, review, and improve coding-agent work inside real repositories.