Role-Driven Mastery System

Master the skills the job market is actually hiring for.

Follow a job-market-audited Role Path, move through a discipline end-to-end with a Career Pathway, or pursue a university-benchmarked Academic Certificate — each with a Basic → Intermediate → Advanced ladder, direct video learning bridges and a capstone proof-of-work project.

Agent Mastery — OpenAI

Not Started0 / 9 complete

Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.

9 lessons

Search Live Openings

We don’t have a direct job-board feed matched to this one yet, so here are real, live US job searches for “Agent Mastery — OpenAI” instead.

Search on LinkedIn →Search on Indeed →
Remote only:LinkedIn →Indeed →

Build from model/tool primitives into managed durable agents: choose the right runtime boundary, connect MCP/tools and skills, orchestrate specialists, harden execution and prove the system with traces/evals.

Learn the shared agent architecture once, then build it independently across Claude, Microsoft Copilot, Google Cloud/GCP, OpenAI and AWS — from tools/MCP and identity to evaluation, security, deployment and a cross-platform production portfolio.

Benchmark: Official OpenAI Agents API / Agents SDK documentation (current Sep 2026)

Mastery Ladder

FOUNDATIONS

Choose the Agent Runtime

Agents API: managed Codex harness, durable sessions and OpenAI-managed orchestration; Responses API: own the loop while using model/tool primitives; Agents SDK / Codex SDK: code-first orchestration when you operate the runtime.

INTERMEDIATE

Tools + State + Orchestration

Function tools, hosted tools, remote MCP servers, tool search and reusable skills; sessions/conversations, context management, sandboxes and artifact-producing execution; agents-as-tools, handoffs/subagents, human-in-the-loop approvals and structured outputs.

ADVANCED

Evaluate + Operate

Guardrails, sandbox boundaries, least privilege and safe consequential actions; tracing, eval suites, regression gates, latency/cost and failure diagnosis; durable sessions, recovery/rollback, production deployment and adoption evidence.

Official Learning / Build Resources

Agents overview / runtime choices

Agents API overview

Agents API quickstart

Using tools + MCP

Agents SDK guide

Hands-On Build Lab — Build Sequence

  • 01 – Agent choice: Create one working agent with a clearly bounded job, typed inputs/outputs and an explicit runtime choice (Agents API, Responses loop, or SDK) with rationale.
  • 02 – Ground + tools: Add grounded context and at least two tools; connect one remote MCP server or build a local/remote MCP tool integration.
  • 03 – State + artifacts: Add durable state/session behavior and one sandbox/file/code workflow where the agent produces or modifies an artifact.
  • 04 – Orchestration: Add a manager/specialist pattern using subagents, agents-as-tools or handoffs; keep authorization checks outside model judgment.
  • 05 – Guardrails: Implement input/output/tool guardrails plus human approval before one consequential write action; test prompt-injection and malformed-tool inputs.
  • 06 – Evaluate + trace: Create a representative eval set, capture traces, diagnose at least three failures, and demonstrate measurable improvement without hiding regressions.
  • 07 – Deploy: Deploy/stage the agent; document SLOs, cost/latency, rollback, incident handling, cleanup and a user/business success metric.

Reference Links

Agents API quickstart

Agents SDK - tracing

Agents SDK - guardrails

Agents SDK - handoffs

Pass / Proof Rubric

  • Working behavior, not a scripted demo: correct tool use
  • robust state
  • safe action boundaries
  • useful orchestration
  • reproducible evals
  • trace-based debugging
  • prompt-injection/tool abuse resistance
  • recovery from partial failure
  • quantified quality/latency/cost
  • and a runbook another engineer can operate