AI Workflows & Teams
Organize processes, materials and responsibilities so work can be repeated and shared.
Make a one-off method repeatable and transferable
Recommended reading
Should AI Work Live in a Project, Folder, or Conversation?
Give projects, files and conversations distinct roles, with an established place for important results.
All articles
- Team Adoption & SOPsHow Should Teams Assign Responsibility for AI Work?Use a simplified RACI matrix to assign data, process, access, approval, incident, and version responsibilities with explicit escalation paths.
- Team Adoption & SOPsHow to Design a Small, Measurable, Stoppable AI PilotChoose one low-risk use case, a small group, and a short period. Compare quality, time, revision, and failures, then decide go, revise, or stop.
- Workflow & AutomationA Repeatable AI Image Workflow: From Brief to Reviewed PromptTurn an AI image request into a repeatable workflow with a clear brief, human review gate, stop conditions, and reusable records—without promising unattended automation.
- Agents & CollaborationAI Agent Definition of Done: How to Define CompletionReliable agents need observable completion criteria. Define tasks, outcomes, failure cases, and human calibration before treating an AI workflow as dependable.
- Agents & CollaborationWhat Is an Agent Harness? Lessons from Claude’s Public Engineering GuidesClaude's public engineering guides show how an agent harness defines context, tools, stop conditions, permissions, and traceability.
- Agents & CollaborationAI Agent Evaluation: Why “Done” Is Not CompletionAn agent response, an artifact, a verified result, and a real-world outcome are different things. Use a four-layer definition of completion for AI agent evaluation.
- Agents & CollaborationHuman-in-the-Loop AI Agents: Where Humans Should StayHuman-in-the-loop does not mean approving every action. Use reversibility, permission, and consequence to place people at the decisions that matter.