Before their first class, this graduate built their Keystone™ — the context layer that captures how they work, decide, and communicate — in a 1:1 session with a COOPilots.io instructor.
From that foundation, across live cohort sessions, they built real working AI systems for their own work.
This credential means they didn't just learn about AI — they built something real, in their own voice, on their own work, and left with the skills for building more.
They learned how AI actually works
The four ingredients: Desk, Keystone, Instructions, Tools
CLAUDE.md as the table of contents
Keystone IQ + EQ — frameworks, SOPs, voice, values
Tokens, context windows, and context rot
Determinism vs. Non-determinism
Where to safely use LLMs in real work
The 5-ingredient prompt: Ask, Constraints, Context, Output, Permissions
20+ things AI can do without a single integration
7 common pitfalls, and how to contain each one
Claude models and MCP vs. CLI vs. middleware tradeoffs
Foundations
Build
They designed real work
Their own Keystone™ — the context layer that makes AI act like them
They identified 3-5 real work use cases for AI before class even began
Workflow design: natural language → process → tools → dependencies
Picking the right tool: skill vs. agent
Advanced builds: loops, workflows, agent teams, challenger agents
Plan Mode and the discipline of 95% clarification
Shipped a committed use case
Scale
They learned how to scale it
Five lenses of impact: Capacity, Capability, Quality, Revenue, Cost
How to baseline, measure, and pressure-test ROI
Governance and liability awareness across the current AI regulatory patchwork
Five rules for building safely at scale
A 30/60/90 plan to turn a project into a practice
Questions? Contact Caitlin Ferguson at caitlin@coopilots.io