AI Builder
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
- Validate user value and ground model behavior in reliable context
- Build tool-using agents and ship production full-stack AI experiences
- Measure agents, observe runtime behavior and secure production execution
Benchmark: OpenAI Codex/Cloud Agents + Anthropic product/managed-agent engineering signals (Sep 2026) Mastery ladder: 2 job-market competencies per level; 2 direct VIDEO bridges per competency (12 total).
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Validate user value and ground model behavior in reliable context
Problem discovery, use-case framing and AI product iteration
Coverage: Product Discovery, AI UX & Customer Research
Prompt, context, retrieval and grounding design
Coverage: Prompt Engineering, Context Engineering & RAG
Build tool-using agents and ship production full-stack AI experiences
Agent loops, tool calling, MCP and orchestration
Coverage: AI Agents, Tools, MCP & Workflow Orchestration
Full-stack UX, APIs, async/streaming and deployment
Coverage: Full-Stack AI Apps, APIs & Production Delivery
Measure agents, observe runtime behavior and secure production execution
Agent evals, tracing, observability and quality gates
Coverage: AI Evals, Monitoring, Tracing & Reliability
Identity, secure runtime, sandboxing and adversarial testing
Coverage: Agent Security, Governance, Sandboxing & Red Teaming
Role-Readiness Proof / Capstone
Ship an AI-native product from validated user problem to production: prompt/RAG grounding, tool-calling + MCP agent workflow, full-stack UI/API with streaming, deployment, interactive evals, tracing/observability, identity/secrets/sandbox controls, red-team evidence and measurable reliability/adoption/cost outcomes.
Proof Rubric
- Explain the concept clearly
- apply it in a realistic scenario
- compare tradeoffs
- show tests, metrics or decision evidence
- quantify customer/business/technical value or risk reduction