# Collaborative Session Examples This directory contains realistic, end-to-end session transcripts showing how the Game Studio Agent Architecture works in practice. Each example demonstrates the **collaborative workflow** where agents ask questions, present options, and wait for user approval rather than autonomously generating content. --- ## 📚 **Available Examples** ### [Session: Designing the Crafting System](session-design-crafting-system.md) **Type:** Design **Agent:** game-designer **Duration:** ~45 minutes (12 turns) **Complexity:** Medium **Scenario:** Solo dev needs to design a crafting system that serves Pillar 2 ("Emergent Discovery Through Experimentation"). The agent guides them through question/answer, presents 3 design options with game theory analysis, incorporates user modifications, and iteratively drafts the GDD with approval at each step. **Key Collaborative Moments:** - Agent asks 5 clarifying questions upfront - Presents 3 distinct options with pros/cons + MDA alignment - User modifies recommended option, agent incorporates immediately - Edge case flagged proactively ("what if non-recipe combo?") - Each GDD section shown for approval before moving to next - Explicit "May I write to [file]?" before creating file **Learn:** - How design agents ask about goals, constraints, references - How to present options using game design theory (MDA, SDT, Bartle) - How to iterate on drafts section-by-section - When to delegate to specialists (systems-designer, economy-designer) --- ### [Session: Implementing Combat Damage Calculation](session-implement-combat-damage.md) **Type:** Implementation **Agent:** gameplay-programmer **Duration:** ~30 minutes (10 turns) **Complexity:** Low-Medium **Scenario:** User has a complete design doc and wants the damage calculation implemented. Agent reads the spec, identifies 7 ambiguities/gaps, asks clarifying questions, proposes architecture for approval, implements with rule enforcement, and proactively writes tests. **Key Collaborative Moments:** - Agent reads design doc first, identifies 7 spec ambiguities - Architecture proposed with code samples BEFORE implementation - User requests type safety, agent refines and re-proposes - Rules catch issues (hardcoded values), agent fixes transparently - Tests written proactively following verification-driven development - Agent offers options for next steps rather than assuming **Learn:** - How implementation agents clarify specs before coding - How to propose architecture with code samples for approval - How rules enforce standards automatically - How to handle spec gaps (ask, don't assume) - Verification-driven development (tests prove it works) --- ### [Session: Scope Crisis - Strategic Decision Making](session-scope-crisis-decision.md) **Type:** Strategic Decision **Agent:** creative-director **Duration:** ~25 minutes (8 turns) **Complexity:** High **Scenario:** Solo dev faces crisis: Alpha milestone in 2 weeks, crafting system needs 3 weeks, investor demo is make-or-break. Creative director gathers context, frames the decision, presents 3 strategic options with honest trade-off analysis, makes recommendation but defers to user, then documents decision with ADR and demo script. **Key Collaborative Moments:** - Agent reads context docs before proposing solutions - Asks 5 questions to understand decision constraints - Frames decision properly (what's at stake, evaluation criteria) - Presents 3 options with risk analysis and historical precedent - Makes strong recommendation but explicitly: "this is your call" - Documents decision + provides demo script to support user **Learn:** - How leadership agents frame strategic decisions - How to present options with trade-off analysis - How to use game dev precedent and theory in recommendations - How to document decisions (ADRs) - How to cascade decisions to affected departments --- ## 🎯 **What These Examples Demonstrate** All examples follow the **collaborative workflow pattern:** ``` Question → Options → Decision → Draft → Approval ``` ### ✅ **Collaborative Behaviors Shown:** 1. **Agents Ask Before Assuming** - Design agents ask about goals, constraints, references - Implementation agents clarify spec ambiguities - Leadership agents gather full context before recommending 2. **Agents Present Options, Not Dictates** - 2-4 options with pros/cons - Reasoning based on theory, precedent, project pillars - Recommendation made, but user decides 3. **Agents Show Work Before Finalizing** - Design drafts shown section-by-section - Architecture proposals shown before implementation - Strategic analysis presented before decisions 4. **Agents Get Approval Before Writing Files** - Explicit "May I write to [file]?" before using Write/Edit tools - Multi-file changes list all affected files first - User says "Yes" before any file is created 5. **Agents Iterate on Feedback** - User modifications incorporated immediately - No defensiveness when user changes recommendations - Celebrate when user improves agent's suggestion --- ## 📖 **How to Use These Examples** ### For New Users: Read these examples BEFORE your first session. They show realistic expectations for how agents work: - Agents are consultants, not autonomous executors - You make all creative/strategic decisions - Agents provide expert guidance and options ### For Understanding Specific Workflows: - **Designing a system?** → Read session-design-crafting-system.md - **Implementing code?** → Read session-implement-combat-damage.md - **Making strategic decisions?** → Read session-scope-crisis-decision.md ### For Training: If you're teaching someone to use this system, walk through one example turn-by-turn to show: - What good questions look like - How to evaluate presented options - When to approve vs. request changes - How to maintain creative control while leveraging AI expertise --- ## 🔍 **Common Patterns Across All Examples** ### Turn 1-2: **Understand Before Acting** - Agent reads context (design docs, specs, constraints) - Agent asks clarifying questions - No assumptions or guesses ### Turn 3-5: **Present Options with Reasoning** - 2-4 distinct approaches - Pros/cons for each - Theory/precedent supporting the analysis - Recommendation made, decision deferred to user ### Turn 6-8: **Iterate on Drafts** - Show work incrementally - Incorporate feedback immediately - Flag edge cases or ambiguities proactively ### Turn 9-10: **Approval and Completion** - "May I write to [file]?" - User: "Yes" - Agent writes files - Agent offers next steps (tests, review, integration) --- ## 🚀 **Try It Yourself** After reading these examples, try this exercise: 1. Pick one of your game systems (combat, inventory, progression, etc.) 2. Ask the relevant agent to design or implement it 3. Notice if the agent: - ✅ Asks clarifying questions upfront - ✅ Presents options with reasoning - ✅ Shows drafts before finalizing - ✅ Requests approval before writing files If the agent skips any of these, remind it: > "Please follow the collaborative protocol from docs/COLLABORATIVE-DESIGN-PRINCIPLE.md" --- ## 📝 **Additional Resources** - **Full Principle Documentation:** [docs/COLLABORATIVE-DESIGN-PRINCIPLE.md](../COLLABORATIVE-DESIGN-PRINCIPLE.md) - **Workflow Guide:** [docs/WORKFLOW-GUIDE.md](../WORKFLOW-GUIDE.md) - **Agent Roster:** [.claude/docs/agent-roster.md](../../.claude/docs/agent-roster.md) - **CLAUDE.md (Collaboration Protocol):** [CLAUDE.md](../../CLAUDE.md#collaboration-protocol)