Agentic Execution Lab: learn Codex and Claude by building real cases.
80% of AI projects fail. The technology isn't the problem: it gets grafted onto processes designed for people. Here we pick a real process, build a usable workflow, and the team learns the agentic mindset: delegate, verify, orchestrate.
Bring me a real case[Real case]
We work on a company problem.
Reports, analysis, content, code, automation, competitor research, operations, or e-commerce: no classroom examples.
[Plan -> Build -> Review]
Codex, Claude, and Cowork as operating teammates.
The team learns to provide context, split work, delegate long-running tasks, review outputs, and decide when to intervene.
[Mindset]
The result is a new way of working.
Not just tools: the team keeps playbooks, agent instructions, verification checklists, working files, and criteria to repeat the method.
Case selection
We select a real process with useful output: a workflow, report, page, automation, or pipeline.
Work decomposition
We separate what humans must do, what agents can do, where verification is needed, and which standards define a good output.
Operating session
We use OpenAI Codex, Claude Code, or Claude Cowork on the real case, explaining the reasoning while the work progresses.
Review and correction
We build the quality-control checklist: typical errors, limits, stop criteria, iterations, and final validation.
Reusable handoff
The team receives assets, prompts, instructions, workflows, and criteria to repeat the method on similar cases.
Case selection
We select a real process with useful output: a workflow, report, page, automation, or pipeline.
Work decomposition
We separate what humans must do, what agents can do, where verification is needed, and which standards define a good output.
Operating session
We use OpenAI Codex, Claude Code, or Claude Cowork on the real case, explaining the reasoning while the work progresses.
Review and correction
We build the quality-control checklist: typical errors, limits, stop criteria, iterations, and final validation.
Reusable handoff
The team receives assets, prompts, instructions, workflows, and criteria to repeat the method on similar cases.
Teams that do not want generic AI training
You want to learn by doing, with cases that leave concrete value inside the company.
Founders and operators
You want to understand how to use AI agents to produce real work, not just drafts or ideas.
Technical, marketing, and operations teams
You want to adopt Codex, Claude Code, or Cowork with method, verification, and shared standards.
Want to know where to start?
Send context, website, and objective. I'll reply with the most sensible first step.