{"company":"LockedIn Labs","site":"https://training.lockedinlabs.ai","generated":"2026-10-21T04:42:03.579Z","agent":{"brief":"https://training.lockedinlabs.ai/agents.md","negotiated":"Any public route returns the same brief when requested with Accept: text/markdown."},"levels":[{"id":"L0","name":"AI Curious","code":"L0 · CURIOUS","tagline":"See the machine clearly.","summary":"Strip away the magic. Build accurate mental models of what modern AI systems are, what they are not, and where they genuinely bite.","outcomes":["Explain LLMs, agents, and tools without hand-waving","Judge AI claims with a practitioner's skepticism","Map the AI native tool landscape by job-to-be-done"],"gate":"Planned: an unscripted foundations review, active after vetted examiner coverage."},{"id":"L1","name":"AI Capable","code":"L1 · CAPABLE","tagline":"Run your day on AI.","summary":"Turn AI from a novelty into your default way of working. 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Planned: named practitioner review, active after vetted reviewer coverage."},{"id":"L3","name":"AI Native Operator","code":"L3 · OPERATOR","tagline":"Build and run agentic systems.","summary":"The operator level. Compose multi-step agents, orchestrate fleets, evaluate behavior rigorously, and take systems from demo to dependable.","outcomes":["Design and ship multi-step agentic workflows","Evaluate agent behavior with structured evals","Operate agents in production with observability"],"gate":"Ship a verified agentic system: working, evaluated, and documented."},{"id":"L4","name":"AI Native Leader","code":"L4 · LEADER","tagline":"Transform the organization.","summary":"From operator to force multiplier. Strategy, governance, operating models, and the economics of AI transformation — for people who move companies, not just workflows.","outcomes":["Build an AI native operating model for a real org","Design governance that accelerates instead of blocking","Make the investment case with honest economics"],"gate":"Planned: defend a transformation plan in a live board-style review, active after vetted examiner coverage."}],"dimensions":[{"key":"literacy","label":"AI literacy & judgment"},{"key":"prompting","label":"Prompting & instruction design"},{"key":"decomposition","label":"Workflow decomposition"},{"key":"agents","label":"Agent direction & oversight"},{"key":"verification","label":"Verification & quality control"},{"key":"governance","label":"Security & governance awareness"}],"tracks":[{"slug":"foundations","name":"AI Native Foundations","code":"FND","audience":"Everyone — no technical background required","band":"L0 → L1","modules":[{"slug":"mental-models","title":"Thinking in 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control package: spec, case sets, graders, baseline/candidate runs, failure analysis, remediation, full rerun, and release decision."},{"slug":"secure-authority","title":"Bound authority and earn authorization","level":"L3","artifact":"Authorization package: threat model, authority matrix, control evidence, attack transcript, residual-risk register, and signed decision recommendation."},{"slug":"production-command","title":"Operate adoption, rollout, and incidents","level":"L4","artifact":"Production command record: readiness review, staged rollout, mission dashboard, adoption evidence, incident transcript, regression, and four-room briefings."},{"slug":"yield-ownership","title":"Yield ownership and compound field learning","level":"L4","artifact":"FDE mission portfolio: owner-accepted transfer package, teach-back/change/recovery evidence, field memo, and integrated live-defense index."}]},{"slug":"developers","name":"AI Native Developers","code":"DEV","audience":"Working software engineers","band":"L1 → L3","modules":[{"slug":"developer-daily-loop","title":"The AI-capable developer day","level":"L1","artifact":"One instrumented workday: the ledger, the test-first change, and the triaged review findings."},{"slug":"ai-native-ide","title":"The AI native dev environment","level":"L2","artifact":"AGENTS.md + agent-produced diff + failure analysis."},{"slug":"spec-driven-dev","title":"Spec-driven development with agents","level":"L2","artifact":"Executable spec + agent plan + green diff."},{"slug":"agentic-tdd","title":"Test-driven agentic coding","level":"L3","artifact":"TDD transcript with mutation-test evidence."},{"slug":"reviewing-machine-code","title":"Reviewing machine code","level":"L3","artifact":"Risk-tiered review of an agent PR with demanded changes."},{"slug":"ship-an-agent","title":"Ship an agent to production","level":"L3","artifact":"Working agent + consequence/coverage/variance release evidence + redacted event contract + measured cost sheet + exercised containment and rollback runbook."}]},{"slug":"agents","name":"Agentic Workflows & AI Agents","code":"AGI","audience":"Builders, operators, and technical leads","band":"L2 → L3","modules":[{"slug":"agent-anatomy","title":"Anatomy of an agent","level":"L2","artifact":"Minimal agent config + annotated execution trace."},{"slug":"tools-and-protocols","title":"Tools, MCP, and protocols","level":"L2","artifact":"Tool contract v1→v2 with documented agent failure."},{"slug":"orchestration","title":"Orchestrating multi-step agents","level":"L3","artifact":"Three-agent orchestration + run evidence from both gates."},{"slug":"agent-evals","title":"Evaluating agent behavior","level":"L3","artifact":"Three-layer eval suite + adversarial fix."},{"slug":"agent-fleet","title":"Design an agent fleet","level":"L3","artifact":"Fleet architecture + working two-agent slice with evals."}]},{"slug":"sdlc","name":"AI Native SDLC & Control Plane","code":"SDL","audience":"Platform, security, and delivery engineers in regulated orgs","band":"L2 → L4","modules":[{"slug":"ai-native-sdlc","title":"The AI native SDLC","level":"L2","artifact":"Eight-phase delivery map for a real agent workstream + gate definitions + accountability labels."},{"slug":"risk-tiering","title":"Risk tiers and the control matrix","level":"L2","artifact":"Risk-tier control matrix for five real systems + the design change classification forced."},{"slug":"verifier-layer","title":"The verifier layer","level":"L3","artifact":"Six-class verifier suite wired into a pipeline + signed results for one real change + an induced failure."},{"slug":"agent-observability","title":"Agent observability","level":"L3","artifact":"Instrumented agent + one full trace + three runtime detection rules + a forensic reconstruction."},{"slug":"control-plane","title":"The enterprise AI control plane","level":"L3","artifact":"Control-plane design + a registered agent with scoped delegated authority + a policy denial + the orphaned-owner demonstration."},{"slug":"authorize-to-operate","title":"Authorization and the evidence chain","level":"L4","artifact":"Authorization package for a governed agent deployment: evidence chain, red-team and independent verification results, residual risk register, expiry, and recertification plan."}]},{"slug":"executives","name":"Executives & Leaders","code":"EXE","audience":"CEOs, CIOs, CMOs, and transformation leaders","band":"L1 → L4","modules":[{"slug":"executive-ai-day","title":"The AI-capable executive day","level":"L1","artifact":"One instrumented week: the ledger, the stress-tested decision memo pair, and your written data boundary."},{"slug":"landscape","title":"The AI native landscape","level":"L2","artifact":"Scored vendor pitch + two forcing questions."},{"slug":"ai-roi","title":"Where AI actually pays","level":"L2","artifact":"Scored use-case portfolio + top-three defense."},{"slug":"agentic-delivery","title":"Directing agentic delivery","level":"L3","artifact":"Delegation contract for one agentic workflow, with rung defense."},{"slug":"operating-model","title":"The AI native operating model","level":"L4","artifact":"Human-agent team redesign with roles and metrics."},{"slug":"governance","title":"Governance that ships","level":"L4","artifact":"Three-tier governance policy + classified examples."},{"slug":"transformation-plan","title":"Leading the transformation","level":"L4","artifact":"Board-ready transformation plan + live defense."}]},{"slug":"healthcare","name":"Healthcare & Regulated Industries","code":"HLT","audience":"Clinical, operational, and IT leaders in regulated orgs","band":"L2 → L4","modules":[{"slug":"regulated-basics","title":"AI in regulated environments","level":"L2","artifact":"Constraint map + AI insertion point."},{"slug":"phi-guardrails","title":"PHI, privacy, and guardrails","level":"L2","artifact":"Guardrail design + worked record flow."},{"slug":"clinical-workflows","title":"Agentic 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review."},{"slug":"cyclic-graphs-with-langgraph4j","title":"Cyclic graphs with LangGraph4j","level":"L3","artifact":"A LangGraph4j loop, durable file adapter, human interrupt and fault trace: live crash-resume preserves authority and budgets, uncertain reads stay explicit, and a named human defends the limits."},{"slug":"choosing-the-java-ai-stack","title":"Choosing the Java AI stack","level":"L3","artifact":"An ADR, two finalist trials, measurement appendix and switching runbook: enforce hard constraints, preserve uncertainty and defend the recommendation to a named assessor under a changed client constraint."}]}],"certifications":[{"code":"AINO-F","name":"AI Native Foundations","level":"L1","track":"foundations","issuance":"issuable","issuable":true},{"code":"AINO-D","name":"Certified AI Native Developer","level":"L3","track":"developers","issuance":"issuable","issuable":true},{"code":"AINO-A","name":"Certified Agentic 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