AI Collaboration on hanneseichblatt.de

Case Study Paperclip: The Mission Pattern in Practice

Sun, 26 Apr 2026 00:00:00 +0000

This post builds on Structuring Collaboration, which defined four AI collaboration modes: Lookup, Workshop, Companion, and Mission.

Mission is the hardest pattern to get right. You hand over a task definition and success criteria, and a trusted agent colleague works autonomously — checking back at decision points, not at every step.

Most people understand Mission in theory. The difficulty is operational: how do you actually structure delegation so the agent knows what it’s working on, when it’s done, and when to stop and ask?

Towards an AI Collaboration Skill Tree

Sun, 26 Apr 2026 00:00:00 +0000

Previous posts established the four collaboration patterns and mapped how organizations onboard agent colleagues. What’s missing is the individual’s instrument: a map of where you are, what’s locked, what to unlock next.

RPG players know the skill tree. You pick a path, invest points, unlock gates, specialize. Prerequisites are encoded—no fireball until you’ve mastered spark. The tree tells you what to learn, in what order, and what becomes possible. AI adoption needs the same structure.

Structuring Collaboration: AI Adoption as Agentic Onboarding

Thu, 23 Apr 2026 00:00:00 +0000

You’re leading a medium-sized tech organization – 200-500 people across development, operations, analytics, and professional services. Your organization has decided to adopt AI capabilities strategically, not in a panic.

I think we need to reframe this: AI adoption is agentic colleague onboarding. You’re integrating a new kind of colleague into the way your teams work. The real question isn’t “which AI tools should we buy?” but “how do we adapt our collaboration practices to work with them?”

Four Modes of AI Collaboration

Sun, 12 Apr 2026 00:00:00 +0000

Most people I know use AI the same way: type a question, get an answer, move on. That works fine. But it’s one of four fundamentally different engagement patterns, and treating them as interchangeable is part of why AI-assisted work so often feels like it underdelivers.

The Four Modes

The patterns are best understood along two axes: how much context the agent keeps, and how much autonomy you give it.

Lookup is stateless. You bring a fully specified question; the agent answers it; nothing carries over. It’s the right tool for exactly what it sounds like: looking things up. Most people live here.