Phase 9: Team Workflows & AI-Native Process · 45 min · Claude Code · MCP · Python

Knowledge Management for AI-Native Teams

A new hire who is productive with the codebase but useless with the team's AI agent hasn't really onboarded — the agent's context is now part of what 'knowing the codebase' means.

Real-world application: Treating a team's shared context (rules files, skills, institutional knowledge exposed to agents) as a maintained asset with its own audit and onboarding process shows you understand context engineering as team infrastructure, not a personal productivity trick.

What you will learn

Explain what shared context (rules files, skills, MCP-exposed knowledge) means as team infrastructure rather than individual habit
Run a context audit: identify what a new team member needs to be productive with the team's AI agent on day one
Distinguish institutional knowledge that belongs in a shared, agent-readable artifact from knowledge that should stay in a person's head or a wiki
Design an onboarding pattern for AI-native teams that includes agent context, not just codebase and process knowledge

Introduction

Knowledge Management for AI-Native Teams

Objective

Given the team's current real practices and the current contents of the shared agent rules file, identify missing required practices (gaps) and stale entries (practices no longer followed).

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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