AI employee guides

A practical operating model for giving AI employees real jobs, context, tools, recurring work, and review without losing human control.

A seven-day plan for setting up company context, hiring the first AI employee, connecting one tool, and completing a measurable work loop.

A practical comparison of chat assistants, workflow automation, and AI employees with roles, context, tools, tasks, files, schedules, and approval rules.

The trust architecture behind Yodu: one Docker container per workspace, command-queue-only access, keys you can rotate but never read, and an approval gate on every irreversible action.

Connect Claude Code to Yodu's Platform MCP, verify the workspace boundary, and use 53 curated tools without exposing stored secret values.

Use Yodu's workspace-scoped REST API for task intake, memory synchronization, reporting, channels, schedules, tools, and runtime visibility.

A visual tour of the workspace where founders talk to AI employees, manage tasks, inspect files, configure access, and review recurring work.

A launch note on deleting draft-only, approval-required, autonomous, and revoked: three of the four tiers gated nothing, so tool access became one honest switch.

A practical guide to connecting business apps, deciding which employees can see each tool with one switch, and letting the approval gate hold risky actions.

How to configure your own model access, add a remote MCP server, verify health, and grant private capabilities conservatively.

A practical method for turning repeated corrections into reusable AI employee skills with clear inputs, outputs, quality checks, and approval boundaries.

A practical review system for tasks, evidence, files, approvals, schedules, access, and outcomes produced by an AI team.

A scorecard for accepted work, cycle time, rework, blocked time, model cost, and recurring-job usefulness without inflating vanity metrics.

A safer engineering workflow for connecting GitHub, scoping the task, keeping branch protection and CI intact, and preserving human merge control.

A source-backed workflow for creating useful content that Google, Bing, ChatGPT search, and AI answer engines can discover, understand, and cite.

How to scope a remote MCP server, protect authorization, test health, review exposed tools, and roll access out to one AI employee at a time.