Learning paths

Build, route, secure, evaluate, package, and verify Agent Skills in real hosts.

Engineer tool-using agent loops with explicit context, memory, orchestration, safety, evaluation, and production control.

Build reliable data, feature, embedding, retrieval, evaluation, and observability pipelines for AI systems.

Build credible integrations, examples, and reusable agent packages, then turn developer friction into clearer tools and teaching.

Measure model and agent behavior, expose failure modes, instrument the runtime, and build release and incident controls around evidence.

Turn language-model capability into evaluated, safe, cost-aware product behavior that can survive production traffic.

Build an AI feature from prompt and structured output through retrieval, evaluation, production serving, observability, and release.

Discover a customer workflow, reduce its riskiest assumptions, and carry a useful AI system through measurement and rollout.

Build, secure, verify, and operate stateless MCP systems from JSON-RPC envelopes through conformance release gates.

Turn evidence into outcomes, assumptions, testable slices, executable specifications, measurement plans, and owned feedback loops.

Build the repository, debugging, testing, interface, security, release, and operational foundations AI systems depend on.

Frame, plan, execute, delegate, verify, review, and improve coding-agent work inside real repositories.