# Research Topic: Concurrent Session Architecture Limitations in Claude Code Governance

**Status**: Discovered Design Constraint
**Priority**: Medium
**Classification**: Single-Tenant Architecture Limitation
**First Identified**: October 2025 (Phase 4)
**Related To**: Session state management, framework health metrics, test isolation
**Scope**: Concurrent Claude Code sessions

---

## Executive Summary

A significant architectural constraint was discovered during production testing: **the Tractatus framework assumes single-session, single-instance operation**. When multiple Claude Code instances govern the same codebase concurrently, several failure modes emerge:

1. **Contaminated health metrics** (token usage, message counts, pressure scores blend across sessions)
2. **Race conditions in instruction storage** (concurrent writes to `.claude/instruction-history.json`)
3. **Test isolation failures** (concurrent test runs conflict on shared database)
4. **Session state corruption** (last-write-wins on `.claude/session-state.json`)
5. **Inaccurate checkpoint triggers** (blended token counts fire alerts at wrong thresholds)

**This is a design constraint, not a bug.** The framework was architected for single-developer, single-session workflows—a valid design choice for Phase 1 prototyping. However, this reveals an important limitation for enterprise deployment where multiple developers might use AI governance concurrently on shared codebases.

**Discovery method**: Dogfooding during production testing when two concurrent sessions were inadvertently run, producing MongoDB duplicate key errors and invalid health metrics.

**Good news**: This is addressable through multi-tenant architecture patterns (session-specific state files, database-backed state, file locking). However, these capabilities are not yet implemented.

---

## 1. The Problem

### 1.1 Architectural Assumption: Single Session

**Framework Design** (Phase 1-4):
```
Assumption: ONE Claude Code instance governs codebase at a time
Architecture: Shared state files in .claude/ directory
State persistence: File-based JSON (no locking)
Session identification: Static session ID, manually updated
```

**Why This Was Reasonable**:
- Phase 1 prototype (research demonstration)
- Solo developer workflow (original use case)
- Simplified implementation (no concurrency complexity)
- Faster development (avoid distributed systems problems)

**Where It Breaks**:
- Multiple developers using AI governance concurrently
- Production testing while development continues
- Automated CI/CD with AI agents
- Parallel task execution

### 1.2 Discovered During Production Testing

**Scenario**: Two Claude Code sessions running concurrently on same codebase

**Session A**: Production test suite execution (`npm test`)
**Session B**: Development work on elevator pitch documentation

**Observed Failure**: MongoDB duplicate key errors
```
MongoServerError: E11000 duplicate key error collection:
tractatus_prod.documents index: slug_1 dup key:
{ slug: "test-document-integration" }
```

**Root Cause**: Both sessions running test suites simultaneously, both attempting to create test documents with identical slugs, test cleanup race conditions preventing proper teardown.

**Contamination Indicator**: Session health metrics became meaningless—token counts, message counts, and pressure scores blended from both conversations, making framework health assessment unreliable.

---

## 2. Technical Analysis

### 2.1 Shared State Files

**Files Affected**:
```
.claude/instruction-history.json      (18 instructions, ~355 lines)
.claude/session-state.json            (Framework activity tracking)
.claude/token-checkpoints.json        (Milestone monitoring)
```

**Problem: No File Locking**

```javascript
// Simplified pseudo-code showing vulnerability
function addInstruction(newInstruction) {
  // Session A reads file
  const history = JSON.parse(fs.readFileSync('instruction-history.json'));

  // Session B reads file (same state)
  const history = JSON.parse(fs.readFileSync('instruction-history.json'));

  // Session A adds instruction, writes back
  history.push(instructionA);
  fs.writeFileSync('instruction-history.json', JSON.stringify(history));

  // Session B adds instruction, writes back (overwrites A's change!)
  history.push(instructionB);
  fs.writeFileSync('instruction-history.json', JSON.stringify(history));

  // Result: instructionA is LOST (classic write conflict)
}
```

**Impact**: Last-write-wins behavior, instruction additions can be silently lost.

**Frequency**: Low under normal use (instruction additions are infrequent), but probabilistically guaranteed under concurrent operation.

### 2.2 Session State Contamination

**Session State Structure** (`.claude/session-state.json`):
```json
{
  "session_id": "2025-10-07-001",
  "created_at": "2025-10-07T12:00:00Z",
  "token_budget": 200000,
  "messages": 42,
  "framework_activity": {
    "pressure_checks": 3,
    "instructions_added": 2,
    "validations_run": 15,
    "boundary_enforcements": 1
  }
}
```

**Concurrent Session Behavior**:
- Session A: 42 messages, 85,000 tokens
- Session B: 18 messages, 32,000 tokens
- **Blended state**: 60 messages, 117,000 tokens (meaningless)

**Pressure Score Contamination**:
```
Session A calculates: 85,000 / 200,000 = 42.5% (ELEVATED)
Session B reads blended: 117,000 / 200,000 = 58.5% (HIGH)
Session B incorrectly triggers handoff recommendation!
```

**Impact**: Framework health metrics become unreliable, checkpoint triggers fire at incorrect thresholds, context pressure monitoring fails to serve its purpose.

### 2.3 Test Isolation Failures

**Test Suite Design**:
```javascript
// tests/integration/api.documents.test.js
beforeEach(async () => {
  // Create test document
  await db.collection('documents').insertOne({
    slug: 'test-document-integration',  // Static slug
    title: 'Test Document',
    // ...
  });
});

afterEach(async () => {
  // Clean up test document
  await db.collection('documents').deleteOne({
    slug: 'test-document-integration'
  });
});
```

**Concurrent Session Behavior**:
```
Time    Session A                        Session B
----    ---------                        ---------
T0      Insert test-document-integration
T1                                       Insert test-document-integration
                                         (FAIL: E11000 duplicate key)
T2      Run tests...
T3                                       Delete test-document-integration
T4      Expect document exists
        (FAIL: document deleted by B!)
```

**Impact**: Test failures not related to actual bugs, unreliable CI/CD, false negatives in quality checks.

**Observed**: 29 tests failing on production with concurrent sessions vs. 1 failing locally (single session).

### 2.4 Session Identity Confusion

**Current Implementation**:
```javascript
// scripts/session-init.js
const SESSION_ID = '2025-10-07-001';  // Static, manually updated
```

**Problem**: Both concurrent sessions share same session ID

**Impact**:
- Framework logs ambiguous (can't attribute actions to sessions)
- Instruction history shows mixed provenance
- Debugging concurrent issues impossible
- Audit trail unreliable

---

## 3. Framework Health Metrics Impact

### 3.1 Metrics Compromised by Concurrency

**Token Usage Tracking**:
- ❌ **Contaminated**: Sum of both sessions
- ❌ **Checkpoint triggers**: Fire at wrong thresholds
- ❌ **Budget management**: Neither session knows true usage
- **Reliability**: 0% (completely unreliable)

**Message Count Tracking**:
- ❌ **Contaminated**: Combined message counts
- ❌ **Session length assessment**: Meaningless
- ❌ **Complexity scoring**: Blended contexts
- **Reliability**: 0% (completely unreliable)

**Context Pressure Score**:
- ❌ **Contaminated**: Weighted average of unrelated contexts
- ❌ **Handoff triggers**: May fire prematurely or miss degradation
- ❌ **Session health assessment**: Unreliable
- **Reliability**: 0% (completely unreliable)

**Error Frequency**:
- ⚠️ **Partially contaminated**: Combined error counts
- ⚠️ **Error attribution**: Can't determine which session caused errors
- ⚠️ **Pattern detection**: Mixed signal obscures real patterns
- **Reliability**: 30% (error detection works, attribution doesn't)

**Task Complexity**:
- ⚠️ **Partially contaminated**: Sum of concurrent tasks
- ⚠️ **Complexity scoring**: Appears artificially high
- **Reliability**: 40% (detects high complexity, can't attribute)

### 3.2 Metrics Unaffected by Concurrency

**Test Suite Pass Rate**:
- ✅ **Database-backed**: Reflects actual system state
- ✅ **Objectively measurable**: Independent of session state
- **Reliability**: 100% (fully reliable)
- **Note**: Pass rate itself reliable, but concurrent test execution causes failures

**Framework Component Operational Status**:
- ✅ **Process-local verification**: Each session verifies independently
- ✅ **Component availability**: Reflects actual system capabilities
- **Reliability**: 100% (fully reliable)

**Instruction Database Content**:
- ⚠️ **Eventually consistent**: Despite write conflicts, instructions persist
- ⚠️ **Audit trail**: Provenance may be ambiguous
- **Reliability**: 85% (content reliable, provenance uncertain)

### 3.3 Real-World Impact Example

**Observed Scenario** (October 2025):

```
Session A (Production Testing):
- Messages: 8
- Tokens: 29,414
- Pressure: Should be 14.7% (NORMAL)
- Action: Continue testing

Session B (Development):
- Messages: 42
- Tokens: 85,000
- Pressure: Should be 42.5% (ELEVATED)
- Action: Monitor, prepare for potential handoff

Blended State (What Both Sessions See):
- Messages: 50
- Tokens: 114,414
- Pressure: 57.2% (HIGH)
- Action: RECOMMEND HANDOFF (incorrect for both!)
```

**Impact**: Session A incorrectly warned about context pressure, Session B unaware of actual elevated pressure. Framework health monitoring counterproductive instead of helpful.

---

## 4. Why This Wasn't Caught Earlier

### 4.1 Development Workflow Patterns

**Phase 1-3 Development** (Solo workflow):
- Single developer
- Sequential sessions
- One task at a time
- Natural session boundaries

**Result**: Architectural assumption validated by usage pattern (no concurrent sessions in practice).

### 4.2 Test Suite Design

**Current Testing**:
- Unit tests (isolated, no state conflicts)
- Integration tests (assume exclusive database access)
- No concurrency testing
- No multi-session scenarios

**Gap**: Tests validate framework components work, but don't validate architectural assumptions about deployment model.

### 4.3 Dogfooding Discovery

**How Discovered**:
- Production test suite running in one terminal
- Concurrent development session for documentation
- Both sessions accessing shared state files
- MongoDB duplicate key errors surfaced the conflict

**Lesson**: Real-world usage patterns reveal architectural constraints that design analysis might miss.

**Validation**: This is exactly what dogfooding is designed to catch—real-world failure modes that theoretical analysis overlooks.

---

## 5. Architectural Design Space

### 5.1 Current Architecture: Single-Tenant

**Design**:
```
Codebase
  └── .claude/
       ├── instruction-history.json  (shared)
       ├── session-state.json        (shared)
       └── token-checkpoints.json    (shared)

Claude Code Instance → Reads/Writes shared files
```

**Assumptions**:
- ONE instance active at a time
- Sequential access pattern
- File-based state sufficient
- Manual session ID management

**Strengths**:
- Simple implementation
- Fast development
- No distributed systems complexity
- Appropriate for Phase 1 prototype

**Weaknesses**:
- No concurrency support
- Race conditions on writes
- Contaminated metrics
- Test isolation failures

### 5.2 Alternative: Multi-Tenant Architecture

**Design**:
```
Codebase
  └── .claude/
       ├── instruction-history.json      (shared, READ-ONLY)
       └── sessions/
            ├── session-abc123/
            │    ├── state.json
            │    └── checkpoints.json
            └── session-xyz789/
                 ├── state.json
                 └── checkpoints.json

Claude Code Instance (Session ABC123)
  → Reads shared instruction-history.json
  → Writes session-specific state files
```

**Capabilities**:
- Multiple concurrent instances
- Session-isolated state
- Accurate per-session metrics
- Instruction history still shared (with locking)

**Implementation Requirements**:
1. Unique session ID generation (UUID)
2. Session-specific state directory
3. File locking for shared instruction writes
4. Session lifecycle management (cleanup old sessions)
5. Aggregated metrics (if needed)

**Complexity**: Moderate (2-3 weeks implementation)

### 5.3 Alternative: Database-Backed State

**Design**:
```
MongoDB Collections:
  - instructions         (shared, indexed)
  - sessions            (session metadata)
  - session_state       (session-specific state)
  - token_checkpoints   (session-specific milestones)

Claude Code Instance
  → Reads from MongoDB (supports concurrent reads)
  → Writes with transaction support (ACID guarantees)
```

**Capabilities**:
- True multi-tenant support
- Transactional consistency
- Query capabilities (aggregate metrics, audit trails)
- Horizontal scaling

**Implementation Requirements**:
1. Database schema design
2. Migration from file-based to DB-backed state
3. Transaction handling
4. Connection pooling
5. State synchronization

**Complexity**: High (4-6 weeks implementation)

### 5.4 Alternative: Distributed Lock Service

**Design**:
```
Shared State Files (existing)
  + File locking layer (flock, lockfile library)
  OR
  + Redis-based distributed locks

Claude Code Instance
  → Acquires lock before state operations
  → Releases lock after write
  → Handles lock timeouts and contention
```

**Capabilities**:
- Prevents write conflicts
- Maintains file-based state
- Minimal architectural change

**Implementation Requirements**:
1. Lock acquisition/release wrapper
2. Deadlock prevention
3. Lock timeout handling
4. Stale lock cleanup

**Complexity**: Low-Moderate (1-2 weeks implementation)

---

## 6. Impact Assessment

### 6.1 Who Is Affected?

**NOT Affected**:
- Solo developers using single Claude Code session
- Sequential development workflows
- Current Tractatus development (primary use case)
- Organizations with strict turn-taking on AI usage

**Affected**:
- Teams with multiple developers using AI governance concurrently
- Production environments with automated testing + development
- CI/CD pipelines with parallel AI-assisted jobs
- Organizations expecting true multi-user AI governance

**Severity by Scenario**:

| Scenario | Impact | Workaround Available? |
|----------|--------|----------------------|
| Solo developer | None | N/A (works as designed) |
| Team, coordinated usage | Low | Yes (take turns) |
| Concurrent dev + CI/CD | Medium | Yes (isolate test DB) |
| True multi-tenant need | High | No (requires architecture change) |

### 6.2 Current Tractatus Deployment

**Status**: Single-developer, single-session usage
**Impact**: None (architectural assumption matches usage pattern)
**Risk**: Low for current Phase 1-4 scope

**Future Risk**:
- Phase 5+: If multi-developer teams adopt framework
- Enterprise deployment: If concurrent AI governance expected
- Scale testing: If parallel sessions needed for research

### 6.3 Enterprise Deployment Implications

**Question**: Can Tractatus scale to enterprise teams (10-50 developers)?

**Current Answer**: Not without architectural changes

**Requirements for Enterprise**:
1. Multi-session support (multiple developers concurrently)
2. Session isolation (independent health metrics)
3. Shared instruction history (organizational learning)
4. Audit trails (who added which instruction, when)
5. Concurrent test execution (CI/CD pipelines)

**Gap**: Current architecture supports #3 partially, not #1, #2, #4, #5

---

## 7. Mitigation Strategies

### 7.1 Current Workarounds (No Code Changes)

**Workaround 1: Coordinated Usage**
- **Approach**: Only one developer uses Claude Code at a time
- **Implementation**: Team agreement, Slack status, mutex file
- **Pros**: Zero code changes, works immediately
- **Cons**: Doesn't scale, manual coordination overhead, limits parallel work

**Workaround 2: Isolated Test Databases**
- **Approach**: Development and testing use separate databases
- **Implementation**: Environment-specific DB names
- **Pros**: Prevents test collision, easy to implement
- **Cons**: Doesn't solve state contamination, partial solution only

**Workaround 3: Session Serialization**
- **Approach**: Stop all Claude Code sessions before starting new one
- **Implementation**: `pkill` Claude Code processes, verify before starting
- **Pros**: Guarantees single session, no conflicts
- **Cons**: Disruptive, prevents parallelism, manual process

### 7.2 Short-Term Solutions (Minimal Code)

**Solution 1: Session-Specific State Directories**
- **Approach**: Implement multi-tenant architecture (Section 5.2)
- **Effort**: 2-3 weeks development
- **Benefits**: Concurrent sessions, isolated metrics, no contamination
- **Risks**: State directory cleanup, session lifecycle management

**Solution 2: File Locking Layer**
- **Approach**: Add distributed locks (Section 5.4)
- **Effort**: 1-2 weeks development
- **Benefits**: Prevents write conflicts, preserves file-based architecture
- **Risks**: Lock contention, timeout handling, debugging complexity

### 7.3 Long-Term Solutions (Architectural)

**Solution 3: Database-Backed State**
- **Approach**: Migrate to MongoDB-backed state (Section 5.3)
- **Effort**: 4-6 weeks development
- **Benefits**: True multi-tenant, transactional, scalable, queryable
- **Risks**: Migration complexity, backward compatibility, DB dependency

**Solution 4: Hybrid Approach**
- **Approach**: Shared instruction history (DB), session state (files)
- **Effort**: 3-4 weeks development
- **Benefits**: Balances consistency needs with simplicity
- **Risks**: Two state management systems to maintain

---

## 8. Research Questions

### 8.1 Fundamental Questions

1. **What is the expected concurrency level for AI governance frameworks?**
   - Hypothesis: 2-5 concurrent sessions for small teams, 10-20 for enterprise
   - Method: User studies, enterprise deployment analysis
   - Timeframe: 6-9 months

2. **Does multi-session governance create new failure modes beyond state contamination?**
   - Hypothesis: Yes—instruction conflicts, inconsistent enforcement, coordination overhead
   - Method: Controlled experiments with concurrent sessions
   - Timeframe: 3-6 months

3. **What metrics need to be session-specific vs. aggregate?**
   - Hypothesis: Context pressure session-specific, instruction effectiveness aggregate
   - Method: Multi-session deployment, metric analysis
   - Timeframe: 6 months

### 8.2 Architectural Questions

4. **Is file-based state inherently incompatible with multi-tenant AI governance?**
   - Hypothesis: No, with proper locking mechanisms
   - Method: Implement file locking, test under load
   - Timeframe: 3 months

5. **What are the performance characteristics of DB-backed state vs. file-based?**
   - Hypothesis: DB-backed has higher latency but better consistency
   - Method: Benchmark tests, load testing
   - Timeframe: 2 months

6. **Can session isolation preserve organizational learning?**
   - Hypothesis: Yes, if instruction history shared but session state isolated
   - Method: Multi-tenant architecture implementation
   - Timeframe: 6 months

### 8.3 Practical Questions

7. **At what team size does single-session coordination become impractical?**
   - Hypothesis: 3-5 developers
   - Method: Team workflow studies
   - Timeframe: 6 months

8. **Do concurrent sessions require different governance rules?**
   - Hypothesis: Yes—coordination rules, conflict resolution, priority mechanisms
   - Method: Multi-session governance experiments
   - Timeframe: 9 months

---

## 9. Comparison to Related Systems

### 9.1 Git (Distributed Version Control)

**Concurrency Model**: Optimistic concurrency, merge conflict resolution
**State Management**: Distributed (each developer has full repo)
**Conflict Resolution**: Manual merge, automated for non-conflicting changes
**Lesson**: Even file-based systems can support concurrency with proper design

**Tractatus Difference**: Git merges are explicit, Tractatus state updates implicit
**Takeaway**: Could Tractatus adopt merge-based conflict resolution?

### 9.2 Database Systems

**Concurrency Model**: ACID transactions, row-level locking
**State Management**: Centralized, transactional
**Conflict Resolution**: Locks, isolation levels, optimistic concurrency
**Lesson**: Centralized state enables strong consistency guarantees

**Tractatus Difference**: File-based state lacks transactional guarantees
**Takeaway**: Database-backed state natural fit for multi-session needs

### 9.3 Collaborative Editing (Google Docs, VS Code Live Share)

**Concurrency Model**: Operational transformation, CRDTs (conflict-free replicated data types)
**State Management**: Real-time synchronization
**Conflict Resolution**: Automatic, character-level merging
**Lesson**: Real-time collaboration requires sophisticated conflict resolution

**Tractatus Difference**: Session state doesn't require character-level merging
**Takeaway**: Simpler conflict models (last-write-wins with versioning) might suffice

### 9.4 Kubernetes (Distributed System Orchestration)

**Concurrency Model**: Leader election, etcd for distributed state
**State Management**: Distributed consensus (Raft protocol)
**Conflict Resolution**: Strong consistency, leader serializes writes
**Lesson**: Distributed systems require consensus for correctness

**Tractatus Difference**: Framework doesn't need distributed consensus (codebase is single source of truth)
**Takeaway**: File locking or DB transactions sufficient, don't need Raft/Paxos

---

## 10. Honest Assessment

### 10.1 Is This a Fatal Flaw?

**No.** Single-tenant architecture is:
- A valid design choice for Phase 1 prototype
- Appropriate for solo developer workflows
- Simpler to implement and maintain
- Not unique to Tractatus (many tools assume single user)

**But**: It's a limitation for enterprise deployment and team usage.

### 10.2 When Does This Become Critical?

**Timeline**:
- **Now** (Phase 1-4): Not critical (solo developer workflow)
- **Phase 5-6** (6-12 months): May need multi-session if teams adopt
- **Enterprise deployment**: Critical requirement for organizational use
- **Research experiments**: Needed for scalability testing

**Conclusion**: We have 6-12 months before this becomes a blocking issue

### 10.3 Why Be Transparent About This?

**Reason 1: User Expectations**
Organizations evaluating Tractatus should know deployment constraints

**Reason 2: Research Contribution**
Other AI governance frameworks will face concurrency challenges

**Reason 3: Tractatus Values**
Honesty about limitations builds more trust than hiding them

**Reason 4: Design Trade-offs**
Single-tenant architecture enabled faster prototype development—valid trade-off for research phase

---

## 11. Recommendations

### 11.1 For Current Tractatus Users

**Immediate** (Next session):
- Use workaround: Stop concurrent sessions before production testing
- Isolate test databases (development vs. testing)
- Coordinate AI usage in team settings

**Short-term** (1-3 months):
- Implement session-specific state directories (Phase 5)
- Add unique session ID generation
- Test suite improvements (randomized slugs, better cleanup)

**Medium-term** (3-12 months):
- Evaluate need for multi-session support based on user adoption
- Research DB-backed state vs. file locking trade-offs
- Implement chosen multi-tenant architecture if needed

### 11.2 For Organizations Evaluating Tractatus

**Be aware**:
- Current architecture assumes single Claude Code session
- Concurrent sessions cause state contamination and test failures
- Workarounds available (coordinated usage, isolated databases)
- Multi-tenant architecture planned but not implemented

**Consider**:
- Is single-session coordination acceptable for your team size?
- Do you need concurrent AI governance? (most teams: no)
- Can you contribute to multi-session architecture development?

### 11.3 For AI Governance Researchers

**Research Opportunities**:
- Multi-session governance coordination protocols
- Session-specific vs. aggregate metrics
- Concurrent instruction addition conflict resolution
- Optimistic vs. pessimistic concurrency for AI state

**Collaborate on**:
- Multi-tenant architecture design patterns
- Concurrency testing methodologies
- Enterprise deployment case studies

---

## 12. Conclusion

The Tractatus framework's **single-tenant architecture** is a **design constraint, not a defect**. It was appropriate for Phase 1-4 prototype development but represents a limitation for enterprise deployment.

**Key Findings**:
- ✅ **Discovered through dogfooding**: Real-world usage revealed architectural assumption
- ✅ **Well-understood**: Root causes clear, mitigation strategies identified
- ✅ **Addressable**: Multiple architectural solutions available (multi-tenant, DB-backed, file locking)
- ❌ **Not yet implemented**: Current framework doesn't support concurrent sessions

**Current Status**:
- Works reliably for single-session workflows
- Contamination occurs with concurrent sessions
- Workarounds available (coordination, isolation)

**Future Direction**:
- Multi-tenant architecture (Phase 5-6, if user adoption requires)
- Research on concurrent AI governance coordination
- Evaluation of DB-backed vs. file-based state trade-offs

**Transparent Takeaway**: Tractatus is effective for solo developers and coordinated teams, has known concurrency limitations, has planned architectural solutions if enterprise adoption requires them.

**This is the value of dogfooding: discovering real constraints through actual use, not theoretical speculation.**

---

## 13. Appendix: Technical Discovery Details

### 13.1 Observed Error Sequence

**Production Test Execution** (October 9, 2025):

```bash
# Session A: Production testing
npm test
# 29 tests failing (duplicate key errors)

# Session B: Development work
# (concurrent documentation edits)

# Conflict manifestation:
MongoServerError: E11000 duplicate key error collection:
tractatus_prod.documents index: slug_1 dup key:
{ slug: "test-document-integration" }
```

**Analysis**:
- Both sessions running `npm test` simultaneously
- Test setup: Insert document with static slug
- Race condition: Both sessions attempt insert
- MongoDB constraint: Unique index on slug field
- Result: E11000 duplicate key error

**Lesson**: Concurrent test execution requires randomized identifiers or session-specific test data.

### 13.2 Session State Comparison

**Expected (Session A only)**:
```json
{
  "session_id": "2025-10-07-001",
  "messages": 8,
  "tokens_used": 29414,
  "pressure_score": 14.7,
  "status": "NORMAL"
}
```

**Observed (Concurrent A + B)**:
```json
{
  "session_id": "2025-10-07-001",
  "messages": 50,
  "tokens_used": 114414,
  "pressure_score": 57.2,
  "status": "HIGH"
}
```

**Impact**: Framework health assessment unreliable, checkpoint triggers fire incorrectly.

### 13.3 File Write Conflict Timeline

```
T0: Session A reads instruction-history.json (18 instructions)
T1: Session B reads instruction-history.json (18 instructions)
T2: Session A adds inst_019, writes file (19 instructions)
T3: Session B adds inst_020, writes file (19 instructions)
T4: File contains inst_020 only (inst_019 lost!)
```

**Probability**: Low under normal use, 100% guaranteed under heavy concurrent writes.

**Mitigation**: File locking or atomic operations required.

---

**Document Version**: 1.0
**Research Priority**: Medium
**Next Review**: Phase 5 planning (or when multi-session need identified)
**Status**: Open research topic, community contributions welcome
**Scope**: Claude Code concurrent session governance

---

**Related Resources**:
- [Rule Proliferation Research](./rule-proliferation-and-transactional-overhead.md)
- [Framework in Action Case Study](../case-studies/framework-in-action-oct-2025.md)
- `.claude/session-state.json` - Current state structure
- `scripts/session-init.js` - Session initialization

**Future Research**:
- Multi-tenant architecture design (Phase 5-6)
- Database-backed state migration (Phase 6-7)
- Concurrent session coordination protocols (Phase 7)
- Optimistic concurrency control for instruction history (Phase 6)

**Contributions**: See CONTRIBUTING.md (to be created in GitHub repository)

**Anonymization**: All identifying information (server IPs, personal names, organizational details) removed. Technical details preserved for research value.
