AI Coding Agents
Codex + Claude Code: AI Coding Agents Masterclass for Adults & Professionals
Stop typing every line. Learn to direct the two most powerful AI coding agents and ship more, faster, safely — in 24 classes.
Ready to Master Codex & Claude Code Masterclass for Adults & Professionals — AI Coding Agents (24 Classes)?
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Program Overview
This is the definitive professional course on the two agentic coding tools reshaping software work in 2026: OpenAI Codex and Anthropic Claude Code. Across 24 in-depth, lab-driven classes you'll go from first install to automating real engineering workflows — building features, refactoring legacy code, fixing flaky tests, reviewing PRs at scale, and orchestrating parallel agents. Whether you're a developer leveling up, a career switcher entering tech, or a professional who wants to 10x routine work, you'll learn both tools at the depth a job demands. You learn the current stack: Claude Code v2.1.x on Anthropic's Opus 4.7 (with effort levels up to xhigh and 1M-token context), and the OpenAI Codex CLI/Cloud on GPT-5.3-Codex and GPT-5.5. Every class is hands-on with real commands, config, and a deliverable.
What Makes This Program Different
- Two agents, professionally — graduate fluent in BOTH Codex and Claude Code, and able to choose the right one per task and team.
- Workplace-grade labs — refactor legacy modules, fix CI, automate releases, review PRs at scale; not toy examples.
- Goes all the way to the SDKs — build your own automations and internal tooling with the Claude Agent SDK and Codex SDK.
- Living Syllabus — continuously updated as OpenAI Codex and Anthropic Claude Code ship new features, models, and capabilities. You always learn the current product.
- Security & governance built in — permission rules, OS sandboxes, secrets handling, and prompt-injection defense for real teams.
- Outcome-focused — finish with a production-grade capstone, reusable automations, and a portfolio of AI-assisted engineering work.
Your Learning Journey
0
Phase 1 — Foundations of AI Coding Agents (Classes 1-5): the agentic landscape, install + configure Codex and Claude Code, approval/permission models, your first real task.
1
Phase 2 — Daily Engineering Workflow (Classes 6-11): slash commands, plan & goal mode, diffs & multi-file edits, tests & git, project memory (CLAUDE.md / AGENTS.md), checkpoints & resume.
2
Phase 3 — Extending & Connecting (Classes 12-17): MCP, skills/commands & Codex plugins/profiles, subagents, hooks, model & effort selection, multimodal inputs.
3
Phase 4 — Cloud, Collaboration & Scale (Classes 18-21): Codex Cloud parallel tasks, Claude Code web/desktop/mobile/background, GitHub code review & CI, worktrees/batch/agent teams.
4
Phase 5 — Build, Secure & Ship (Classes 22-24): Claude Agent SDK & Codex SDK automation, security & sandboxing for teams, and a production capstone with certification.
Career Progression
1
AI-Assisted Software Engineer / Full-Stack Developer
2
Developer Productivity / Platform Engineer building agent workflows
3
Career switcher entering tech with a modern, AI-native skill set
4
Tech lead introducing safe agentic coding practices to a team
5
Automation engineer building internal tools on the Agent/Codex SDKs
Detailed Course Curriculum
Explore the complete week-by-week breakdown of what you'll learn in this comprehensive program.
📚 Topics Covered
- Agentic coding defined: an autonomous loop that reads context, plans, edits, runs commands, verifies, and iterates
- OpenAI Codex as a product family: the CLI, Codex Cloud, the IDE extension, the ChatGPT/desktop/mobile apps, and the Codex SDK
- Anthropic Claude Code surfaces: terminal, VS Code/JetBrains, desktop, web (claude.ai/code), mobile, Slack, and CI
- Where each excels: Codex for cloud parallelism + GitHub-native review + ChatGPT ecosystem; Claude Code for deep terminal control, subagents, hooks, and skills
- The 2026 model backends: Claude Code on Opus 4.7; Codex on GPT-5.3-Codex and GPT-5.5
- How agentic tools change team workflows: review-centric, smaller PRs, faster iteration
- Building a mental model for evaluating any new agentic tool
🚀 Projects
- Write a one-page internal brief: 'Codex vs Claude Code — where we'd use each' for a hypothetical team
💪 Practice
Map three tasks from your own work to the tool you'd choose and justify it.
🎯 Assessment
Produce a clear comparison matrix of Codex and Claude Code across 6 dimensions.
📚 Topics Covered
- Install: macOS/Linux curl -fsSL https://claude.ai/install.sh | bash; Windows irm https://claude.ai/install.ps1 | iex; Homebrew/WinGet alternatives
- Authenticate: claude auth login (subscription) or ANTHROPIC_API_KEY; claude auth status; claude setup-token for CI
- Enterprise providers: Amazon Bedrock, Google Vertex AI, Azure AI Foundry via env flags (CLAUDE_CODE_USE_BEDROCK, etc.)
- First session and the REPL; the slash-command menu; /status, /usage
- Settings hierarchy: ~/.claude/settings.json (user), .claude/settings.json (project), .claude/settings.local.json (gitignored)
- Choosing your model with /model (defaults to Opus 4.7 on Max/Team Premium, Sonnet on others)
- IDE integration: install the VS Code/JetBrains extension and open Claude Code in your editor
🚀 Projects
- Install Claude Code, configure a project .claude/settings.json, and run your first task in a real repo
💪 Practice
Set up the VS Code extension and review an edit as an inline diff.
🎯 Assessment
Show a configured project with settings.json and a successful first task.
📚 Topics Covered
- Install: npm install -g @openai/codex (Node 18+), or brew install --cask codex; note the @openai/ scope matters
- Authenticate: Sign in with ChatGPT (Plus/Pro/Business/Edu/Enterprise) or an API key
- Health check: codex doctor verifies runtime, auth, terminal, and network
- The interactive TUI: model, service tier, token usage, and approval mode shown inline
- Config file: ~/.codex/config.toml; switch settings per project with profiles (codex --profile <name>)
- Model & reasoning selection with /model (low → xhigh); 2026 defaults GPT-5.3-Codex / GPT-5.5
- Windows options: native (PowerShell + Windows Sandbox) or WSL2
🚀 Projects
- Install Codex, create a ~/.codex/config.toml with a custom profile, and complete a task in workspace-write mode
💪 Practice
Run codex doctor, fix any warnings, and switch reasoning level mid-task.
🎯 Assessment
Submit your config.toml and a screenshot of a successful Codex session.
📚 Topics Covered
- Why approvals are the safety backbone of agentic coding
- Claude Code permission modes (Shift+Tab to cycle): default, acceptEdits, plan (read-only), auto (classifier-approved); set with --permission-mode, manage via /permissions
- Permission rule syntax: allow/ask/deny patterns like Bash(git push *), Edit(*.env)
- Codex sandbox modes: read-only, workspace-write (default), danger-full-access; approval policies untrusted/on-request/on-failure/never/granular
- Codex /approvals to switch live; the --dangerously-bypass-approvals-and-sandbox flag and why it's a last resort
- Designing a team-safe default posture (start restrictive, widen deliberately)
- Reading and reasoning about an approval request before granting it
🚀 Projects
- Author a restrictive permission/approval policy for both tools and prove a blocked action is denied
💪 Practice
Add a deny rule for force-push and destructive commands in both tools.
🎯 Assessment
Document a recommended default permission posture for a small dev team.
📚 Topics Covered
- Pick a real bug or small feature in a sample repo and scope it precisely
- Claude Code: plan → diff → approve → verify; iterate with targeted feedback
- Codex: same task in workspace-write; compare approach, edits, and explanations
- Reading multi-file diffs critically and catching over-broad changes
- Driving iteration: 'narrow the change to module X', 'add a guard for null input'
- Verifying behavior: have the agent run the app/tests and confirm the fix
- Capturing what worked: notes you'll fold into project memory next phase
🚀 Projects
- Resolve the same issue with each agent and write a short comparison of edit quality and effort
💪 Practice
Introduce a regression and have each agent locate and fix it.
🎯 Assessment
Submit two diffs (one per agent) solving the same task, with a critique.
📚 Topics Covered
- Claude Code: /help, /clear, /compact (compress context), /context (inspect what's loaded), /model, /diff, /review
- Codex: /model, /approvals, /review, /status, /plan, /goal, and the @ picker for files/dirs/skills
- Managing context windows deliberately — when to /compact vs /clear vs start fresh
- @-mentioning files and MCP resources to target the agent precisely
- Monitoring cost and limits: /usage (Claude) and inline token usage (Codex)
- Recovering a derailed session: ask the agent to restate its current plan
- Building muscle memory in each TUI for speed
🚀 Projects
- Create a personal/team reference of the 12 most-used commands across both tools with examples
💪 Practice
Run a long session, then reclaim context with /compact and compare token usage before/after.
🎯 Assessment
Demonstrate precise @-mention targeting and a clean context reset.
📚 Topics Covered
- Claude Code Plan Mode (/plan): read-only research and a proposed plan before any edit
- Goal Mode (/goal): the agent works until a defined completion condition is met
- Codex /plan and /goal (Goal mode stable and on by default in 2026)
- Writing checkable goals and acceptance criteria the agent can self-verify against
- Editing the agent's plan to constrain scope and approach before approval
- Plan-then-execute as a default for any multi-file or risky change
- Combining plan mode with restrictive permissions for safe exploration
🚀 Projects
- Use Plan Mode to design a non-trivial change (e.g., add pagination to an API) and execute the approved plan
💪 Practice
Convert a vague ticket into a precise, checkable goal statement.
🎯 Assessment
Submit a plan you edited before approval and the resulting implementation.
📚 Topics Covered
- How agents propose precise edits and render diffs across multiple files
- Reviewing a feature that spans several files coherently
- Accept / reject / request-changes on individual hunks
- Claude Code /diff and the VS Code/JetBrains inline review experience
- Keeping PRs small and reviewable; resisting scope creep from the agent
- Asking for rationale: 'why this approach over X?'
- Detecting and reverting unintended edits
🚀 Projects
- Have an agent refactor a legacy function and review every hunk, rejecting anything out of scope
💪 Practice
Force the agent to split one large change into two smaller, reviewable steps.
🎯 Assessment
Critique a multi-file diff and identify any risky or unnecessary change.
📚 Topics Covered
- Delegating command execution: build, run, lint, and test through the agent
- Test-driven loops: have the agent write a failing test, implement, and make it pass
- Git with the agent: staged diffs, clear commit messages, branch hygiene
- Claude Code Bash tool (sandboxable) and Codex commands inside the workspace boundary
- Approving commands with judgment — reading the exact command first
- Fixing flaky/failing tests iteratively until green
- Frequent, small commits as a safety net
🚀 Projects
- Implement a feature TDD-style with the agent: failing test → implementation → green → commit
💪 Practice
Have the agent bisect and fix a failing CI step locally.
🎯 Assessment
Show a clean commit history of an agent-assisted TDD cycle.
📚 Topics Covered
- Claude Code: /init generates CLAUDE.md; it auto-loads each session
- Memory hierarchy: managed/policy, user (~/.claude/CLAUDE.md), project (./CLAUDE.md), local (CLAUDE.local.md); imports with @path
- Auto-memory (2026): Claude saves learnings to a project memory folder; manage with /memory
- Codex AGENTS.md: repo root + nested per-package + personal ~/.codex/AGENTS.md (closest file wins)
- A 'Review guidelines' section in AGENTS.md to standardize Codex code review
- What belongs in memory: run/build/test commands, conventions, architectural rules, do/don't
- Keeping memory lean (~200 lines) and version-controlled for the team
🚀 Projects
- Write production-quality CLAUDE.md and AGENTS.md for a real repo, including review guidelines
💪 Practice
Add a convention and confirm both agents follow it without re-prompting.
🎯 Assessment
Submit team-ready memory files and explain three rules' impact.
📚 Topics Covered
- Claude Code automatic checkpointing before edits; /rewind (or Esc-Esc) to restore code, conversation, or both
- Key limitation: checkpoints track file-tool edits, not shell rm/mv — your git commits remain the real safety net
- Resuming: claude --continue and claude --resume (by id/name); --fork-session; --from-pr
- Branching a conversation (/branch) to explore alternatives
- Codex session resume and local conversation history search
- Combining git + checkpoints for confident experimentation
- Session organization across multiple concurrent tasks
🚀 Projects
- Run a risky refactor, reject the outcome, and recover cleanly via /rewind plus git
💪 Practice
Resume a prior session by name and continue the task.
🎯 Assessment
Demonstrate a recovery workflow combining rewind and git.
📚 Topics Covered
- Model Context Protocol: a standard for exposing tools/data/resources to agents
- Claude Code: claude mcp add --transport http <name> <url> (also sse/stdio); scopes local/project/user; manage with /mcp, claude mcp list
- MCP resources (@server:protocol://path) and MCP prompts as slash commands
- Codex: configure servers under [mcp_servers.<name>] in config.toml (stdio command/args/env or HTTP url), per-tool approval modes
- Practical integrations: issue trackers, databases, docs, design tools, internal APIs
- Tool Search / deferred tool loading to keep context lean with many servers
- Security: trust boundaries, OAuth, and prompt-injection risk from MCP content
🚀 Projects
- Connect a real MCP server (e.g., a database or docs server) to both agents and complete a task using it
💪 Practice
Add a project-scoped .mcp.json so teammates inherit the same server.
🎯 Assessment
Show one server connected per tool and a task that used its tools.
📚 Topics Covered
- Claude Code Skills (Agent Skills standard): reusable, auto-invokable instructions; .claude/commands/*.md and .claude/skills/*/SKILL.md both create /commands
- Frontmatter control: who can invoke, allowed/disallowed tools, subagent execution; /skills to manage, /reload-skills
- Designing a skill: a /deploy or /release command encapsulating your workflow
- Codex profiles for per-project model/sandbox settings and the plugin marketplace for sharing setups
- When to encode a workflow as a skill/plugin vs ad-hoc prompting
- Distributing skills/plugins to a team for consistency
- Versioning and reviewing shared automations
🚀 Projects
- Build a Claude Code skill that automates a repeatable workflow and a Codex profile for a project
💪 Practice
Iterate a skill twice based on real usage feedback.
🎯 Assessment
Submit a working skill/command and document what it standardizes.
📚 Topics Covered
- Subagents run in their own context with their own tools and permissions
- Built-ins: Explore (fast read-only search), Plan (read-only planning), General-purpose
- Define your own: .claude/agents/*.md with name, description, tools, model, permissionMode
- Least-privilege agents: a reviewer that can only read; a test-writer scoped to tests
- Manage and generate with /agents; subagent memory for cross-session learning
- Dispatching parallel subagents for independent subtasks
- Why subagents protect the main context and improve reliability
🚀 Projects
- Create code-reviewer and test-writer subagents and use them on a real change
💪 Practice
Compare Explore subagent results vs the main agent for a codebase question.
🎯 Assessment
Submit two subagent definitions and sample outputs.
📚 Topics Covered
- Hooks run deterministic code at lifecycle events — automation and guardrails
- Key events: PreToolUse, PostToolUse, SessionStart, UserPromptSubmit, Stop, PreCompact and more
- Config in settings JSON: matcher + handler (command/http/prompt/agent), exit-code 2 to block
- Practical hooks: auto-format/lint on edit, run tests after changes, block dangerous commands, inject context
- Placeholders like ${CLAUDE_PROJECT_DIR} and viewing hooks with /hooks
- Treating hook output as feedback the agent must respond to
- Governance: enforcing team standards automatically via hooks
🚀 Projects
- Implement a PostToolUse format+lint hook and a PreToolUse guard that blocks force-push
💪 Practice
Add a SessionStart hook that prints project setup status.
🎯 Assessment
Submit two working hooks and explain their events and effects.
📚 Topics Covered
- Claude Code models: Opus 4.7 (flagship), Sonnet (balanced), Haiku (fast/cheap); /model, aliases, opusplan
- Effort levels low/medium/high/xhigh (set via /effort, --effort) and the ultrathink keyword
- 1M-token context with Opus 4.7 for large codebases (opus[1m])
- Codex models: GPT-5.3-Codex and GPT-5.5; reasoning levels low→xhigh; long-task compaction across context windows
- Cost/performance trade-offs: cheap+fast for routine edits, top model+high effort for hard design
- Subagent model overrides for cost control at scale
- Measuring whether more effort actually improved the result
🚀 Projects
- Benchmark a hard refactor at two effort levels and two models; document quality vs cost/latency
💪 Practice
Pin different models for main vs subagents and observe the effect.
🎯 Assessment
Produce a model+effort selection guide for five task types.
📚 Topics Covered
- Why visuals help: convey designs, dashboards, and bugs faster than prose
- Drag-and-drop screenshots/images into the prompt as context
- Codex Appshots (macOS): capture frontmost app windows for context
- UI-from-image: have the agent implement a layout from a design screenshot
- Debugging from a screenshot of an error or broken render
- Mixing text + image instructions for precise outcomes
- Current limits: image input supported; no audio/video
🚀 Projects
- Recreate a real UI from a design screenshot and reconcile it against the spec
💪 Practice
Diagnose a rendering bug from a screenshot alone.
🎯 Assessment
Show an image-driven task with a correct, verified result.
📚 Topics Covered
- Codex Cloud: asynchronous agents working in isolated, sandboxed environments
- Get started at chatgpt.com/codex and connect GitHub
- Queue many tasks in parallel; each returns as its own pull request
- Configure cloud environments: repo, setup steps, tools, and internet-access controls
- Reviewing cloud-generated PRs before merge; iterating via comments
- When cloud wins: long-running jobs, parallel chores, repos not on your laptop
- Governance for cloud agents in a team setting
🚀 Projects
- Delegate three parallel Codex Cloud tasks and review/merge the resulting PRs
💪 Practice
Configure a cloud environment's setup steps so the agent can build/test the repo.
🎯 Assessment
Show parallel cloud tasks and explain each PR's scope and quality.
📚 Topics Covered
- claude.ai/code on the web for long-running and parallel tasks, even on repos you don't have locally
- Desktop app: visual diff review and multiple side-by-side sessions
- Background agents: detach with /background, manage with claude agents / attach / logs
- Remote control & mobile: steer and approve from your phone
- Teleporting a web/remote session into the terminal to finish locally
- Choosing the right surface per task and handing off between them
- Slack/GitHub triggers that start sessions
🚀 Projects
- Start a task on the web, hand it to the desktop/terminal, and finish it; run one agent in the background
💪 Practice
Manage a background agent's lifecycle from the shell.
🎯 Assessment
Demonstrate a task moving across two surfaces and a managed background agent.
📚 Topics Covered
- Connect GitHub apps so both agents can open and review PRs
- Codex review: @codex review (P0/P1 findings) or enable automatic reviews on every PR; scoped reviews like @codex review for security regressions
- Codex task mentions: @codex fix the CI failures pushes fixes when authorized
- Claude Code /code-review (effort levels), --comment for inline GitHub comments, --fix to apply findings
- CI automation: openai/codex-action and Claude Code GitHub Actions / GitLab CI
- Defining team review guidelines (AGENTS.md) honored during review
- Acting on AI review feedback with rigor, not blind acceptance
🚀 Projects
- Set up automatic AI code review on a repo and run a PR through both Codex and Claude Code review
💪 Practice
Tune review guidelines and observe how findings change.
🎯 Assessment
Show a reviewed PR with AI findings you triaged and resolved.
📚 Topics Covered
- Parallelization strategy: decompose large work into independent units
- Git worktrees for isolation: claude -w <name> creates an isolated workspace
- Claude Code /batch: split a large change into 5-30 units, one background subagent per worktree, each tests + opens a PR
- Agent teams: multiple coordinating sessions (--teammate-mode)
- Codex Cloud parallelism for the same outcome via many PRs
- Reviewing and integrating parallel outputs without conflicts
- Knowing when serial is safer than parallel
🚀 Projects
- Use /batch (or parallel Codex Cloud tasks) to roll a cross-cutting change across a codebase and review each PR
💪 Practice
Run two agents in separate worktrees on independent tasks simultaneously.
🎯 Assessment
Document a parallelized change: the split, the worktrees/PRs, and the review.
📚 Topics Covered
- Headless automation: claude -p "task" (output-format text/json/stream-json) and codex exec (with --output-schema for structured JSON)
- Claude Agent SDK: npm i @anthropic-ai/claude-agent-sdk / pip install claude-agent-sdk; query() with tools, hooks, agents, permissionMode
- Codex SDK: @openai/codex-sdk (TS) and openai-codex (Python): startThread, run, resumeThread
- Piping and composition: git diff main | claude -p "review for security issues"
- Building an internal automation: triage, summarize, or auto-fix in CI
- Budget/turn controls (--max-turns, --max-budget-usd) for safe unattended runs
- Note: from June 15 2026 Agent SDK / claude -p usage draws on a separate credit on subscription plans
🚀 Projects
- Build a CI automation (e.g., auto-summarize PR diffs or auto-label issues) using claude -p or codex exec with structured output
💪 Practice
Write a script that pipes logs into an agent and parses its JSON response.
🎯 Assessment
Submit a working SDK/headless automation with structured output.
📚 Topics Covered
- Permission rules (allow/ask/deny) evaluated before any tool runs; pattern syntax for fine control
- Claude Code Bash sandbox (/sandbox): Seatbelt (macOS) / bubblewrap (Linux/WSL2); filesystem & network isolation, allow/deny domains
- Codex sandboxes: Seatbelt / bubblewrap / Windows Sandbox; read-only vs workspace-write vs danger-full-access
- Secrets hygiene: denyRead for ~/.ssh, ~/.aws; never commit credentials; env scrubbing
- Prompt injection & untrusted content: risks from web fetches and MCP servers, and mitigations
- Managed/enterprise settings: forced login org, allowed models, disabling hooks, managed domain allowlists
- Accountability: you own the code the agent writes — review and test it
🚀 Projects
- Define and test a hardened security configuration (permissions + sandbox + secret denials) for a team repo
💪 Practice
Demonstrate a sandbox blocking network/file access outside the allowed scope.
🎯 Assessment
Deliver a team 'safe agentic coding' policy document with concrete settings.
📚 Topics Covered
- Scope a real deliverable: a feature in an existing app, or a small full-stack app + an automation
- Plan with Plan/Goal mode; author CLAUDE.md and AGENTS.md with review guidelines
- Build with both agents, using subagents/hooks/MCP where they add value
- Quality gate: tests, AI code review (with fixes), and a clean PR history
- Ship it: deploy the app and/or land the automation in CI
- Document your workflow: what each agent did, decisions, and safeguards
- Present and earn the Modern Age Coders AI Coding Agents Professional certificate
🚀 Projects
- Deliver a deployed, tested, AI-reviewed capstone (app and/or SDK automation) with a documented agentic workflow
💪 Practice
Present a 5-minute walkthrough of how you directed the agents end-to-end.
🎯 Assessment
Final capstone: deployed/merged work + repo + write-up; pass earns the professional certificate.
Projects You'll Build
Build a professional portfolio with 50+ projects real-world projects.
No project information available
Weekly Learning Structure
No weekly structure information available
Certification & Recognition
No certification information available
Technologies & Skills You'll Master
Comprehensive coverage of the entire modern web development stack.
No skills information available
Support & Resources
No support information available
Career Outcomes & Opportunities
Transform your career with industry-ready skills and job placement support.
Prerequisites
Device
A laptop/desktop (Windows, Mac, or Linux) with internet and admin rights to install CLIs
Coding Experience
Helpful but not required — beginners and career switchers are welcome; we start from install and ramp quickly
Accounts
A GitHub account, plus access to Claude (Pro/Max) and ChatGPT (Plus/Pro) plans, or API keys; we cover setup and provider options
Tools
Comfort with a terminal develops during the course; basic familiarity with git is a plus
Who Is This Course For?
Working_professionals
Engineers and analysts who want to ship more with AI agents and automate routine work
Career_switchers
People entering tech who want a modern, AI-native skill set that employers increasingly expect
Developers
Developers leveling up from autocomplete to true agentic workflows (MCP, subagents, SDKs)
Tech_leads
Leads introducing safe, governed agentic coding practices to a team
Founders_freelancers
Founders and freelancers who need to build and ship quickly with a small team
Career Paths After Completion
AI-Assisted Software Engineer / Full-Stack Developer
Developer Productivity / Platform Engineer (agent workflows, internal tooling)
Automation Engineer building on the Claude Agent SDK / Codex SDK
Tech Lead / Engineering Manager standardizing agentic practices
Freelance / Founder shipping AI-built products faster
Salary Expectations
After 3 Months
₹6-12 LPA in India / $70k-110k globally (developer using agents to boost output)
After 6 Months
₹12-22 LPA / $110k-150k (AI-assisted engineer shipping features independently)
After 12 Months
₹22-40+ LPA / $150k-220k+ (senior/lead leveraging agents and SDK automation at scale)
Note
Ranges vary by region, role, and prior experience; directing AI coding agents is rapidly becoming a premium, in-demand skill.
Course Guarantees
Living Syllabus
Living Syllabus guarantee — continuously updated as OpenAI Codex and Anthropic Claude Code release new features, models, and capabilities. Enrolled students always learn the current product, at no extra cost
Hands On
Every class is lab-driven with real commands, config, and a deliverable
Production Focus
Workplace-grade labs and a production capstone, not toy demos
Sdk Depth
Goes all the way to the Claude Agent SDK and Codex SDK for real automation
Mentorship
Guidance from mentors who use these agents in production daily
Certificate
Recognized professional completion certificate
Frequently Asked Questions
Common Questions About Codex & Claude Code Masterclass for Adults & Professionals — AI Coding Agents (24 Classes)
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Proven Results
Our Impact in Numbers
Every student ships a production-grade capstone (deployed app and/or merged automation)
Production Shipped
Students integrate AI code review into a GitHub/CI workflow
Review At Scale
Students build at least one SDK/headless automation
Automation Built
Students produce a team-ready safe-agentic-coding policy
Governance Ready
Why Choose Us
Why Choose Codex & Claude Code Masterclass for Adults & Professionals — AI Coding Agents (24 Classes)?
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