# Trace: Comprehensive Architecture Design
## Multi-View PM System with Advanced Verification & Versioning

**Document Version**: 1.0.0
**Date**: 2025-11-20
**Target**: Production-ready system for orchestrating 1000+ agents

---

## Executive Summary

This document synthesizes extensive research on versioning systems, natural language assertions, smart contract verification patterns, and distributed coordination to design a comprehensive architecture for **trace** - a multi-view project management system optimized for power users orchestrating complex agent workflows.

### Core Capabilities

1. **16-View Multi-Modal System** with 60+ link types across all PM aspects
2. **Git-Style Versioning** with time-travel, baselines, and semantic versioning
3. **PERT/WBS/DAG Planning** with critical path analysis and parallel execution
4. **Natural Language Assertions** for acceptance criteria verification
5. **Smart Contract-Style Validation** with cryptographic proofs
6. **NATS-Based Event Coordination** for distributed agent orchestration
7. **Holistic Quality Metrics** continuously validated
8. **Immutable Audit Trails** with Merkle trees and multi-sig approval

### Architecture Philosophy

- **Power-User First**: Maximum robustness and features for orchestrating 1000+ agents
- **No Compromises**: Enterprise features without enterprise compliance overhead
- **Verification-Driven**: Every change formally verified and cryptographically proven
- **Time-Aware**: Complete history with time-travel to any point
- **Agent-Optimized**: Designed for AI agent collaboration and orchestration

---

## 1. System Architecture Overview

### 1.1 Layered Architecture

```
┌─────────────────────────────────────────────────────────────────────┐
│                    CLI/TUI Layer (Typer + Rich + Textual)           │
│                   User Interface & Agent API Gateway                 │
└────────────────────────┬────────────────────────────────────────────┘
                         │
┌────────────────────────┴────────────────────────────────────────────┐
│                     Application Services Layer                       │
│  ┌──────────────┬──────────────┬──────────────┬──────────────────┐  │
│  │ Multi-View   │ Intelligent  │ PM Planning  │ Agent            │  │
│  │ Management   │ CRUD         │ (PERT/WBS)   │ Orchestration    │  │
│  └──────────────┴──────────────┴──────────────┴──────────────────┘  │
└────────────────────────┬────────────────────────────────────────────┘
                         │
┌────────────────────────┴────────────────────────────────────────────┐
│                    Verification & Quality Layer                      │
│  ┌──────────────┬──────────────┬──────────────┬──────────────────┐  │
│  │ NL           │ Smart        │ Formal       │ Quality          │  │
│  │ Assertions   │ Contracts    │ Verification │ Metrics          │  │
│  └──────────────┴──────────────┴──────────────┴──────────────────┘  │
└────────────────────────┬────────────────────────────────────────────┘
                         │
┌────────────────────────┴────────────────────────────────────────────┐
│                    Versioning & History Layer                        │
│  ┌──────────────┬──────────────┬──────────────┬──────────────────┐  │
│  │ Event        │ Snapshot     │ Semantic     │ Time-Travel      │  │
│  │ Sourcing     │ Management   │ Versioning   │ Engine           │  │
│  └──────────────┴──────────────┴──────────────┴──────────────────┘  │
└────────────────────────┬────────────────────────────────────────────┘
                         │
┌────────────────────────┴────────────────────────────────────────────┐
│                   Event Coordination Layer (NATS)                    │
│  ┌──────────────┬──────────────┬──────────────┬──────────────────┐  │
│  │ JetStream    │ KV Store     │ Pub/Sub      │ Request/Reply    │  │
│  │ (Persistence)│ (State)      │ (Events)     │ (Coordination)   │  │
│  └──────────────┴──────────────┴──────────────┴──────────────────┘  │
└────────────────────────┬────────────────────────────────────────────┘
                         │
┌────────────────────────┴────────────────────────────────────────────┐
│                     Storage & Persistence Layer                      │
│  ┌──────────────┬──────────────┬──────────────┬──────────────────┐  │
│  │ SQLite       │ Neo4j        │ File System  │ Merkle Store     │  │
│  │ (Primary)    │ (Graph Opt)  │ (Artifacts)  │ (Audit Trail)    │  │
│  └──────────────┴──────────────┴──────────────┴──────────────────┘  │
└─────────────────────────────────────────────────────────────────────┘
```

### 1.2 Technology Stack (Python 3.12)

#### Core Framework
- **FastMCP**: stdio MCP server foundation
- **Typer + Rich**: CLI interface with beautiful formatting
- **Textual**: TUI for interactive workflows
- **NATS**: Event coordination and distributed state

#### Versioning & History
- **pygit2**: Git-style versioning operations
- **jsonpatch**: Structured data diffing (RFC 6902)
- **zstandard**: Compression for deltas/snapshots
- **msgpack**: Efficient event serialization

#### Verification & Quality
- **IntentGuard**: Natural language assertion engine
- **Hypothesis**: Property-based testing
- **Deal**: Design by Contract
- **Z3-solver**: SMT solving for formal verification
- **Pydantic**: Runtime validation via refinement types

#### Database & Storage
- **SQLAlchemy**: ORM with multi-database support
- **SQLite**: Primary storage (start simple)
- **PostgreSQL**: Scale-up path
- **Neo4j**: Optional graph queries
- **orjson**: Fast JSON serialization

#### Analysis & Metrics
- **Transformers (BERT)**: NLP for requirement analysis
- **Ruff**: Fast linting
- **Radon/Lizard**: Complexity metrics
- **Bandit**: Security scanning

#### Cryptography & Proofs
- **cryptography**: Signing and verification
- **hashlib**: Merkle trees and content addressing

---

## 2. Multi-View System Architecture

### 2.1 The 16 Views

```python
class ViewType(Enum):
    """All 16 views in trace system"""
    FEATURE = "feature"           # User stories, epics
    CODE = "code"                 # Implementation modules
    WIREFRAME = "wireframe"       # UI mockups
    API = "api"                   # API specifications
    TEST = "test"                 # Test cases
    DATABASE = "database"         # Schema design
    DEPLOYMENT = "deployment"     # Infrastructure
    METRICS = "metrics"           # KPIs and measurements
    COMPLIANCE = "compliance"     # Requirements tracking
    TASK = "task"                 # WBS tasks
    DEPENDENCY = "dependency"     # Dependency graph
    RISK = "risk"                 # Risk management
    TIMELINE = "timeline"         # PERT/Gantt
    RESOURCE = "resource"         # People/agent allocation
    DECISION = "decision"         # Architecture decisions
    ARTIFACT = "artifact"         # Documents, diagrams
```

### 2.2 Universal Item Structure

Every item in any view shares this core structure:

```python
@dataclass
class UniversalItem:
    """Base structure for all items across all views"""
    id: UUID
    view_type: ViewType
    title: str
    description: str
    status: ItemStatus

    # Hierarchical structure
    parent_id: Optional[UUID]
    children: List[UUID]

    # Cross-view links (60+ types)
    links: List[Link]

    # Versioning metadata
    version: str  # Semantic version
    commit_id: str  # Git-style commit
    baseline_id: Optional[str]

    # Verification metadata
    acceptance_criteria: List[AcceptanceCriterion]
    verification_status: VerificationStatus
    verification_proofs: List[Dict[str, Any]]

    # Quality metrics
    coverage: Optional[float]
    complexity: Optional[float]
    security_score: Optional[float]

    # Audit trail
    created_at: datetime
    updated_at: datetime
    created_by: str
    merkle_proof: Optional[str]

    # Custom attributes per view
    attributes: Dict[str, Any]
```

### 2.3 Cross-View Link Types (60+)

```python
class LinkType(Enum):
    """60+ link types organized by category"""

    # Implementation Links (12 types)
    IMPLEMENTS = "implements"          # Feature → Code
    TESTED_BY = "tested_by"           # Code → Test
    DOCUMENTED_BY = "documented_by"   # Code → Artifact
    DEPLOYED_AS = "deployed_as"       # Code → Deployment
    USES_API = "uses_api"             # Code → API
    READS_FROM = "reads_from"         # Code → Database
    WRITES_TO = "writes_to"           # Code → Database
    CALLS = "calls"                   # Code → Code
    EXTENDS = "extends"               # Code → Code
    IMPLEMENTS_INTERFACE = "implements_interface"
    OVERRIDES = "overrides"
    DELEGATES_TO = "delegates_to"

    # Planning Links (10 types)
    DEPENDS_ON = "depends_on"         # Task → Task
    BLOCKS = "blocks"                 # Task → Task
    PRECEDES = "precedes"             # Task → Task (PERT)
    CHILD_OF = "child_of"             # Task → Task (WBS)
    ASSIGNED_TO = "assigned_to"       # Task → Resource
    CRITICAL_PATH = "critical_path"   # Task → Timeline
    MILESTONE = "milestone"           # Task → Timeline
    PARALLEL_WITH = "parallel_with"   # Task → Task
    SEQUENCE_OF = "sequence_of"       # Task → Task
    ALTERNATIVE_TO = "alternative_to" # Task → Task

    # Requirements Links (8 types)
    SATISFIES = "satisfies"           # Code → Feature
    TRACES_TO = "traces_to"           # Feature → Compliance
    VERIFIED_BY = "verified_by"       # Feature → Test
    CONSTRAINED_BY = "constrained_by" # Feature → Compliance
    DERIVED_FROM = "derived_from"     # Feature → Feature
    CONFLICTS_WITH = "conflicts_with" # Feature → Feature
    SUPERSEDES = "supersedes"         # Feature → Feature
    VARIANT_OF = "variant_of"         # Feature → Feature

    # Design Links (8 types)
    VISUALIZED_BY = "visualized_by"   # Feature → Wireframe
    SPECIFIED_BY = "specified_by"     # API → Artifact
    EXPOSES = "exposes"               # API → Code
    CONSUMED_BY = "consumed_by"       # API → Code
    SCHEMA = "schema"                 # API → Database
    ENDPOINT = "endpoint"             # API → Deployment
    AUTHENTICATION = "authentication" # API → Security
    RATE_LIMITED = "rate_limited"     # API → Policy

    # Monitoring Links (8 types)
    MEASURES = "measures"             # Metrics → Code
    TRIGGERS_ALERT = "triggers_alert" # Metrics → Decision
    MONITORS = "monitors"             # Metrics → Deployment
    TRACKS = "tracks"                 # Metrics → Feature
    BASELINE = "baseline"             # Metrics → Baseline
    EXCEEDS_THRESHOLD = "exceeds_threshold"
    BELOW_THRESHOLD = "below_threshold"
    TREND = "trend"

    # Risk Links (8 types)
    MITIGATES = "mitigates"           # Task → Risk
    INTRODUCES = "introduces"         # Feature → Risk
    ACCEPTS = "accepts"               # Decision → Risk
    TRANSFERS = "transfers"           # Decision → Risk
    AVOIDS = "avoids"                 # Decision → Risk
    ESCALATES = "escalates"           # Risk → Decision
    IMPACT_ON = "impact_on"          # Risk → Feature
    PROBABILITY = "probability"       # Risk → Assessment

    # Temporal Links (6 types)
    VERSION_OF = "version_of"         # Item → Item (history)
    BRANCHED_FROM = "branched_from"   # Item → Item (git-style)
    MERGED_INTO = "merged_into"       # Item → Item
    SNAPSHOT_AT = "snapshot_at"       # Item → Baseline
    REPLACES = "replaces"             # Item → Item
    EVOLVED_FROM = "evolved_from"     # Item → Item
```

### 2.4 Intelligent CRUD System

The intelligent CRUD system auto-generates scaffolding across all 16 views when creating items:

```python
class IntelligentCRUDEngine:
    """Auto-scaffolding engine for multi-view consistency"""

    def create_item(self, primary_view: ViewType, spec: Dict[str, Any]) -> UUID:
        """
        Create item in primary view and auto-scaffold across all relevant views

        Example: Create Feature "User Login"
        - Auto-creates: Code module skeleton
        - Auto-creates: Test cases from acceptance criteria
        - Auto-creates: API endpoints
        - Auto-creates: Database schema for users
        - Auto-creates: WBS tasks for implementation
        - Auto-creates: Deployment requirements
        - Auto-creates: Security compliance checks
        - Auto-creates: Metrics tracking
        """
        # Create primary item
        primary_item = self._create_primary(primary_view, spec)

        # Analyze and infer related items
        inferred_items = self._infer_related_items(primary_item)

        # Create in parallel across views
        created_items = self._parallel_create(inferred_items)

        # Establish links
        self._create_links(primary_item, created_items)

        # Verify consistency
        self._verify_cross_view_consistency(primary_item.id)

        # Emit event for agent coordination
        self.nats.publish(f"item.created.{primary_view}", {
            'item_id': str(primary_item.id),
            'inferred_items': [str(i.id) for i in created_items]
        })

        return primary_item.id

    def _infer_related_items(self, item: UniversalItem) -> List[UniversalItem]:
        """Use NLP + templates to infer related items"""
        inferences = []

        if item.view_type == ViewType.FEATURE:
            # Analyze feature description with BERT
            entities = self.nlp.extract_entities(item.description)

            # Infer code modules
            for entity in entities.get('components', []):
                inferences.append(UniversalItem(
                    view_type=ViewType.CODE,
                    title=f"{entity}.py",
                    description=f"Implementation of {entity}",
                    links=[Link(type=LinkType.IMPLEMENTS, target_id=item.id)]
                ))

            # Infer tests from acceptance criteria
            for criterion in item.acceptance_criteria:
                inferences.append(UniversalItem(
                    view_type=ViewType.TEST,
                    title=f"test_{item.title.lower().replace(' ', '_')}",
                    description=criterion.specification,
                    links=[Link(type=LinkType.VERIFIED_BY, target_id=item.id)]
                ))

            # Infer API endpoints
            if 'api' in item.description.lower():
                inferences.append(UniversalItem(
                    view_type=ViewType.API,
                    title=f"POST /api/{item.title.lower()}",
                    links=[Link(type=LinkType.EXPOSES, target_id=item.id)]
                ))

        return inferences
```

---

## 3. Git-Style Versioning Architecture

### 3.1 Hybrid Event Sourcing + Snapshots

```python
class VersioningEngine:
    """Git-inspired versioning with event sourcing"""

    def __init__(self):
        self.event_store = EventStore()          # Immutable event log
        self.snapshot_store = SnapshotStore()    # Performance snapshots
        self.commit_graph = CommitGraph()        # DAG of commits
        self.baseline_manager = BaselineManager() # Tagged versions

    def commit_changes(
        self,
        changes: List[Event],
        message: str,
        author: str
    ) -> Commit:
        """
        Create atomic commit across all 16 views

        Commit structure mirrors Git:
        - Content-addressable (SHA-256 hash)
        - References parent commit(s)
        - Contains trees for each view
        - Signed by author
        """
        # Validate cross-view consistency
        self._validate_consistency(changes)

        # Create trees for each affected view
        trees = self._create_view_trees(changes)

        # Create commit object
        commit = Commit(
            id=self._compute_commit_hash(trees, message, author),
            parent_commits=self._get_current_heads(),
            trees=trees,
            author=author,
            message=message,
            timestamp=datetime.utcnow(),
            signature=self._sign_commit(trees, message)
        )

        # Append events to immutable log
        for event in changes:
            event.commit_id = commit.id
            self.event_store.append(event)

        # Update commit graph
        self.commit_graph.add_commit(commit)

        # Create snapshot if threshold reached
        if self._should_snapshot():
            self._create_snapshot(commit.id)

        # Emit NATS event
        self.nats.publish("version.committed", {
            'commit_id': commit.id,
            'views_affected': list(trees.keys()),
            'change_count': len(changes)
        })

        return commit

@dataclass
class Commit:
    """Git-style commit object"""
    id: str                          # SHA-256 hash
    parent_commits: List[str]        # Parent commit IDs (DAG)
    trees: Dict[ViewType, str]       # view_type → tree_hash
    author: str
    message: str
    timestamp: datetime
    signature: str                   # Cryptographic signature
    metadata: Dict[str, Any] = field(default_factory=dict)

    def verify_integrity(self) -> bool:
        """Verify commit integrity via hash"""
        recomputed = self._compute_hash()
        return recomputed == self.id
```

### 3.2 Semantic Versioning Automation

```python
class SemanticVersioningEngine:
    """Automated version calculation from conventional commits"""

    def compute_next_version(
        self,
        current_version: str,
        commits_since_last_tag: List[Commit]
    ) -> str:
        """
        Auto-compute next version from commit messages

        Conventional commit format:
        - feat: New feature → MINOR bump
        - fix: Bug fix → PATCH bump
        - feat!: Breaking change → MAJOR bump
        - BREAKING CHANGE: in body → MAJOR bump
        """
        major, minor, patch = self._parse_version(current_version)

        has_breaking = any(
            c.message.startswith('feat!') or
            c.message.startswith('fix!') or
            'BREAKING CHANGE' in c.message
            for c in commits_since_last_tag
        )

        has_feature = any(
            c.message.startswith('feat:')
            for c in commits_since_last_tag
        )

        has_fix = any(
            c.message.startswith('fix:')
            for c in commits_since_last_tag
        )

        if has_breaking:
            return f"{major + 1}.0.0"
        elif has_feature:
            return f"{major}.{minor + 1}.0"
        elif has_fix:
            return f"{major}.{minor}.{patch + 1}"

        return current_version

    def create_release(self, version: str) -> Baseline:
        """Create tagged baseline for release"""
        current_commit = self.commit_graph.get_head()

        baseline = Baseline(
            id=uuid4(),
            name=f"v{version}",
            version=version,
            type=BaselineType.SEMANTIC,
            commit_id=current_commit.id,
            timestamp=datetime.utcnow(),
            protected=True  # Cannot be deleted
        )

        self.baseline_manager.create(baseline)

        return baseline
```

### 3.3 Time-Travel Engine

```python
class TimeTravelEngine:
    """Navigate to any point in history"""

    def travel_to_time(self, timestamp: datetime) -> ProjectState:
        """
        Reconstruct complete project state at any point in time

        Algorithm:
        1. Find nearest snapshot before timestamp
        2. Load snapshot state
        3. Replay events from snapshot to timestamp
        4. Return reconstructed state
        """
        # Find nearest snapshot
        snapshot = self.snapshot_store.find_before(timestamp)

        if not snapshot:
            # No snapshot - replay from beginning
            state = ProjectState.empty()
            events = self.event_store.get_all_before(timestamp)
        else:
            # Start from snapshot
            state = snapshot.load_state()
            events = self.event_store.get_between(
                snapshot.timestamp,
                timestamp
            )

        # Replay events
        for event in events:
            state = self._apply_event(state, event)

        return state

    def travel_to_baseline(self, baseline_name: str) -> ProjectState:
        """Jump to named baseline (e.g., 'v1.0.0')"""
        baseline = self.baseline_manager.get_by_name(baseline_name)
        commit = self.commit_graph.get_commit(baseline.commit_id)

        return self._reconstruct_state_at_commit(commit)

    def diff_versions(
        self,
        version_a: str,
        version_b: str
    ) -> VersionDiff:
        """
        Compute diff between two versions

        Returns structured diff across all 16 views
        """
        state_a = self.travel_to_baseline(version_a)
        state_b = self.travel_to_baseline(version_b)

        return VersionDiff(
            added_items=state_b.items - state_a.items,
            removed_items=state_a.items - state_b.items,
            modified_items=self._compute_modifications(state_a, state_b),
            link_changes=self._compute_link_changes(state_a, state_b)
        )
```

---

## 4. PM Planning Features (PERT/WBS/DAG)

### 4.1 Work Breakdown Structure (WBS)

```python
class WBSEngine:
    """Hierarchical work breakdown structure"""

    def create_wbs(self, root_feature: UUID) -> WBSTree:
        """
        Create WBS from feature with intelligent decomposition

        WBS Principles:
        - 100% rule: children sum to 100% of parent
        - 3-5 levels deep
        - Deliverable-focused (not activity-focused)
        """
        root = self.item_store.get(root_feature)

        # Decompose using NLP + templates
        decomposition = self._decompose_feature(root)

        # Create task hierarchy
        wbs_tree = WBSTree(root=root)

        for level_1 in decomposition:
            l1_task = self._create_task(level_1, parent=root.id)

            for level_2 in level_1.subtasks:
                l2_task = self._create_task(level_2, parent=l1_task.id)

                # Leaf tasks get estimates
                if not level_2.subtasks:
                    l2_task.estimate = self._estimate_effort(l2_task)

        # Verify 100% rule
        self._verify_100_percent_rule(wbs_tree)

        return wbs_tree

    def _decompose_feature(self, feature: UniversalItem) -> List[TaskDecomposition]:
        """
        Intelligent feature decomposition

        Uses NLP to extract:
        - User actions → tasks
        - System components → work packages
        - Acceptance criteria → verification tasks
        """
        decomposition = []

        # Extract user actions with BERT
        actions = self.nlp.extract_actions(feature.description)

        for action in actions:
            # Create work package for each action
            work_package = TaskDecomposition(
                title=f"Implement {action}",
                description=self._expand_action(action),
                subtasks=self._infer_subtasks(action)
            )
            decomposition.append(work_package)

        # Add testing work package
        decomposition.append(TaskDecomposition(
            title="Testing & Verification",
            subtasks=[
                Task(title="Unit tests"),
                Task(title="Integration tests"),
                Task(title="Acceptance tests"),
                Task(title="Verification proofs")
            ]
        ))

        return decomposition
```

### 4.2 PERT/CPM Critical Path Analysis

```python
class PERTEngine:
    """Program Evaluation and Review Technique with Critical Path Method"""

    def compute_critical_path(self, wbs_tree: WBSTree) -> CriticalPathResult:
        """
        Compute critical path using forward/backward pass

        Algorithm:
        1. Build task dependency DAG
        2. Forward pass: compute earliest start/finish
        3. Backward pass: compute latest start/finish
        4. Compute slack (float) for each task
        5. Critical path = tasks with zero slack
        """
        # Build DAG from WBS + dependencies
        dag = self._build_dependency_dag(wbs_tree)

        # Forward pass
        earliest = self._forward_pass(dag)

        # Backward pass
        latest = self._backward_pass(dag, earliest)

        # Compute slack
        slack = {
            task_id: latest[task_id] - earliest[task_id]
            for task_id in dag.nodes
        }

        # Critical path = zero slack
        critical_path = [
            task_id for task_id, s in slack.items()
            if s == 0
        ]

        return CriticalPathResult(
            critical_path=critical_path,
            earliest_times=earliest,
            latest_times=latest,
            slack=slack,
            total_duration=max(earliest.values())
        )

    def _forward_pass(self, dag: TaskDAG) -> Dict[UUID, float]:
        """
        Forward pass: compute earliest start times

        ES(task) = max(EF(predecessor) for all predecessors)
        EF(task) = ES(task) + duration(task)
        """
        earliest = {}

        # Topological sort
        for task_id in dag.topological_sort():
            task = dag.get_task(task_id)

            if not task.predecessors:
                # No predecessors - start at time 0
                earliest[task_id] = 0
            else:
                # Start after all predecessors finish
                earliest[task_id] = max(
                    earliest[pred_id] + dag.get_task(pred_id).duration
                    for pred_id in task.predecessors
                )

        return earliest

    def optimize_schedule(
        self,
        critical_path: List[UUID],
        constraints: ScheduleConstraints
    ) -> OptimizedSchedule:
        """
        Optimize schedule using critical path

        Strategies:
        - Parallelize non-critical tasks
        - Resource leveling
        - Compress critical path (crashing/fast-tracking)
        """
        schedule = OptimizedSchedule()

        # Parallelize non-critical tasks
        for cp_task_id in critical_path:
            parallel_candidates = self._find_parallel_candidates(
                cp_task_id,
                critical_path
            )

            for candidate in parallel_candidates:
                schedule.run_parallel(cp_task_id, candidate)

        # Resource leveling
        schedule = self._level_resources(schedule, constraints)

        return schedule
```

### 4.3 DAG Task Scheduling with NATS

```python
class DAGExecutionEngine:
    """Distributed DAG execution using NATS"""

    def __init__(self, nats_url: str):
        self.nats = NASTClient(nats_url)
        self.js = self.nats.jetstream()

        # JetStream streams
        self.task_stream = "TASKS"
        self.result_stream = "RESULTS"

    async def execute_dag(
        self,
        dag: TaskDAG,
        max_parallel: int = 10
    ) -> ExecutionResult:
        """
        Execute DAG tasks in parallel with dependency resolution

        NATS Architecture:
        - Task Queue: JetStream stream with pull consumers
        - State: KV store for task status
        - Coordination: Request/Reply for synchronization
        """
        # Initialize execution state
        state = await self._init_execution_state(dag)

        # Create worker consumers
        workers = [
            self._create_worker(f"worker-{i}")
            for i in range(max_parallel)
        ]

        # Schedule ready tasks
        ready_tasks = dag.get_ready_tasks()
        for task_id in ready_tasks:
            await self._schedule_task(task_id, state)

        # Monitor execution
        while not state.is_complete():
            # Wait for task completion
            result = await self._wait_for_completion()

            # Update state
            await state.mark_complete(result.task_id)

            # Check for newly ready tasks
            newly_ready = dag.get_newly_ready_tasks(result.task_id)

            for task_id in newly_ready:
                await self._schedule_task(task_id, state)

        return ExecutionResult(
            success=state.all_succeeded(),
            completed_tasks=state.completed,
            failed_tasks=state.failed,
            total_duration=state.elapsed_time()
        )

    async def _schedule_task(self, task_id: UUID, state: ExecutionState):
        """Publish task to NATS queue"""
        task = state.dag.get_task(task_id)

        # Publish to JetStream
        await self.js.publish(
            subject=f"{self.task_stream}.{task.type}",
            data=msgpack.packb({
                'task_id': str(task_id),
                'type': task.type,
                'params': task.params,
                'dependencies': [str(dep) for dep in task.dependencies]
            })
        )

        # Update KV store
        await self.js.kv.put(
            bucket="task_status",
            key=str(task_id),
            value=msgpack.packb({'status': 'scheduled'})
        )

    async def _create_worker(self, worker_id: str):
        """Create pull consumer for task execution"""
        consumer = await self.js.pull_subscribe(
            subject=f"{self.task_stream}.*",
            durable=worker_id
        )

        async def worker_loop():
            while True:
                try:
                    # Pull next task (blocking)
                    msgs = await consumer.fetch(batch=1, timeout=30)

                    for msg in msgs:
                        task_data = msgpack.unpackb(msg.data)

                        # Execute task
                        result = await self._execute_task(task_data)

                        # Publish result
                        await self.js.publish(
                            subject=f"{self.result_stream}.{task_data['task_id']}",
                            data=msgpack.packb(result)
                        )

                        # Ack message
                        await msg.ack()

                except TimeoutError:
                    continue

        # Start worker
        asyncio.create_task(worker_loop())
```

---

## 5. Natural Language Assertions & Verification

### 5.1 IntentGuard Integration

```python
class NLAssertionEngine:
    """Natural language assertion verification using IntentGuard"""

    def __init__(self):
        self.intent_guard = IntentGuard(
            model='qwen2.5-coder-1.5b',
            confidence_threshold=0.85
        )
        self.cache = LRUCache(maxsize=1000)

    def verify_acceptance_criterion(
        self,
        code: str,
        criterion: AcceptanceCriterion
    ) -> VerificationResult:
        """
        Verify code satisfies natural language acceptance criterion

        Uses multi-perspective voting:
        - 5 different prompt formulations
        - Majority voting
        - Confidence scoring
        """
        # Check cache
        cache_key = self._compute_cache_key(code, criterion)
        if cache_key in self.cache:
            return self.cache[cache_key]

        # Multi-perspective verification
        perspectives = [
            f"Does this code satisfy: {criterion.specification}?",
            f"Verify the property holds: {criterion.specification}",
            f"Check if invariant is maintained: {criterion.specification}",
            f"Confirm the following is true: {criterion.specification}",
            f"Validate behavior: {criterion.specification}"
        ]

        votes = []
        for perspective in perspectives:
            result = self.intent_guard.verify(
                code=code,
                property=perspective,
                temperature=0.2  # Low temperature for consistency
            )
            votes.append(result)

        # Aggregate with confidence
        aggregated = self._aggregate_results(votes)

        # Cache result
        self.cache[cache_key] = aggregated

        return aggregated

    def continuous_verification(self, watch_path: str):
        """
        Watch mode: continuously verify as code changes

        Integrates with file system watcher
        """
        observer = Observer()

        handler = NLVerificationHandler(self)
        observer.schedule(handler, watch_path, recursive=True)

        observer.start()

        try:
            while True:
                time.sleep(1)
        except KeyboardInterrupt:
            observer.stop()

        observer.join()

class NLVerificationHandler(FileSystemEventHandler):
    """File system event handler for continuous NL verification"""

    def on_modified(self, event):
        if event.src_path.endswith('.py'):
            # File changed - re-verify
            code = Path(event.src_path).read_text()

            # Extract NL assertions from code
            assertions = self.extract_nl_assertions(code)

            for assertion in assertions:
                result = self.engine.verify_acceptance_criterion(
                    code=code,
                    criterion=assertion
                )

                if not result.passes or result.confidence < 0.85:
                    self.notify_developer(event.src_path, assertion, result)
```

### 5.2 Formal Methods Integration (Hybrid Verification)

```python
class HybridVerificationEngine:
    """Combine NL assertions with formal methods"""

    def __init__(self):
        self.nl_verifier = NLAssertionEngine()
        self.z3_verifier = Z3SymbolicExecutor()
        self.hypothesis_runner = HypothesisRunner()

    def verify_comprehensive(
        self,
        code: str,
        acceptance_criteria: List[AcceptanceCriterion]
    ) -> ComprehensiveVerificationResult:
        """
        Multi-layer verification:
        1. NL verification (IntentGuard)
        2. Property-based testing (Hypothesis)
        3. Symbolic execution (Z3)
        4. Reconcile discrepancies
        """
        results = ComprehensiveVerificationResult()

        for criterion in acceptance_criteria:
            # Layer 1: NL verification
            nl_result = self.nl_verifier.verify_acceptance_criterion(
                code, criterion
            )
            results.nl_results.append(nl_result)

            # Layer 2: Property-based testing
            if criterion.testable:
                pb_result = self.hypothesis_runner.test_property(
                    code, criterion.to_hypothesis_property()
                )
                results.property_based_results.append(pb_result)

            # Layer 3: Symbolic execution for critical properties
            if criterion.critical:
                sym_result = self.z3_verifier.verify_symbolically(
                    code, criterion.to_z3_formula()
                )
                results.symbolic_results.append(sym_result)

        # Reconcile discrepancies
        results.reconciliation = self._reconcile(
            results.nl_results,
            results.property_based_results,
            results.symbolic_results
        )

        return results

    def _reconcile(
        self,
        nl_results: List[VerificationResult],
        pb_results: List[VerificationResult],
        sym_results: List[VerificationResult]
    ) -> ReconciliationReport:
        """
        Reconcile results from different verification methods

        Hierarchy of trust:
        1. Symbolic execution (highest trust)
        2. Property-based testing
        3. NL verification (lowest trust)
        """
        report = ReconciliationReport()

        for i, criterion in enumerate(acceptance_criteria):
            nl = nl_results[i]
            pb = pb_results[i] if i < len(pb_results) else None
            sym = sym_results[i] if i < len(sym_results) else None

            if sym:
                # Symbolic execution is ground truth
                if nl.passes and not sym.passes:
                    report.add_discrepancy(
                        criterion=criterion,
                        type="NL_FALSE_POSITIVE",
                        explanation="NL verified but symbolic execution found bug"
                    )
            elif pb:
                # Property-based testing is secondary
                if nl.passes and not pb.passes:
                    report.add_discrepancy(
                        criterion=criterion,
                        type="NL_FALSE_POSITIVE",
                        explanation="NL verified but property-based testing found counterexample"
                    )

        return report
```

---

## 6. Smart Contract-Style Validation

### 6.1 Executable Acceptance Criteria

```python
class ExecutableAcceptanceCriterion:
    """Acceptance criteria as executable smart contracts"""

    def __init__(
        self,
        story_id: str,
        criterion_id: str,
        given: List[Precondition],
        when: Action,
        then: List[Postcondition]
    ):
        self.story_id = story_id
        self.criterion_id = criterion_id
        self.given = given
        self.when = when
        self.then = then

    def verify_execution(self, context: Dict[str, Any]) -> VerificationResult:
        """
        Execute criterion as smart contract

        Returns cryptographically signed proof
        """
        # Check preconditions (Given)
        for precondition in self.given:
            if not precondition.holds(context):
                return VerificationResult.precondition_failed(
                    precondition=precondition,
                    context=context
                )

        # Execute action (When)
        result_context = self.when.execute(context)

        # Verify postconditions (Then)
        for postcondition in self.then:
            if not postcondition.holds(result_context):
                return VerificationResult.postcondition_failed(
                    postcondition=postcondition,
                    context=result_context
                )

        # Generate cryptographic proof
        proof = self._generate_proof(context, result_context)

        return VerificationResult.verified(proof=proof)

    def _generate_proof(
        self,
        initial_context: Dict[str, Any],
        final_context: Dict[str, Any]
    ) -> CryptographicProof:
        """Generate signed proof of execution"""
        proof_data = {
            'criterion_id': self.criterion_id,
            'story_id': self.story_id,
            'timestamp': datetime.utcnow().isoformat(),
            'initial_state_hash': self._hash_context(initial_context),
            'final_state_hash': self._hash_context(final_context),
            'verifier': 'automated-verification-system'
        }

        # Compute proof hash
        proof_hash = hashlib.sha256(
            json.dumps(proof_data, sort_keys=True).encode()
        ).hexdigest()

        # Sign with private key
        signature = self.sign_proof(proof_hash)

        return CryptographicProof(
            data=proof_data,
            hash=proof_hash,
            signature=signature
        )
```

### 6.2 Immutable Audit Trail with Merkle Trees

```python
class VerificationAuditTrail:
    """Immutable audit trail using Merkle trees"""

    def __init__(self):
        self.verifications: List[CryptographicProof] = []
        self.merkle_tree: Optional[MerkleTree] = None
        self.persistence = SQLiteAuditStore()

    def record_verification(self, proof: CryptographicProof):
        """Add verification to immutable log"""
        # Append to in-memory list
        self.verifications.append(proof)

        # Persist to database
        self.persistence.insert(proof)

        # Rebuild Merkle tree
        leaves = [
            json.dumps(v.data, sort_keys=True)
            for v in self.verifications
        ]
        self.merkle_tree = MerkleTree(leaves)

        # Publish Merkle root to NATS for distributed verification
        self.nats.publish("audit.merkle_root", {
            'root': self.merkle_tree.root,
            'leaf_count': len(self.verifications),
            'timestamp': datetime.utcnow().isoformat()
        })

    def verify_proof_inclusion(
        self,
        proof: CryptographicProof,
        claimed_root: str
    ) -> bool:
        """Verify proof was included in audit trail"""
        # Find proof index
        try:
            index = self.verifications.index(proof)
        except ValueError:
            return False

        # Get Merkle proof
        merkle_proof = self.merkle_tree.get_proof(index)

        # Verify proof
        leaf = json.dumps(proof.data, sort_keys=True)
        return self.merkle_tree.verify_proof(leaf, merkle_proof, claimed_root)

    def get_full_audit_trail(self, item_id: UUID) -> List[CryptographicProof]:
        """Get complete audit trail for an item"""
        return [
            proof for proof in self.verifications
            if proof.data.get('item_id') == str(item_id)
        ]
```

### 6.3 Multi-Signature Acceptance for Critical Changes

```python
class MultiSigApprovalWorkflow:
    """Multi-signature approval for critical acceptance criteria"""

    def __init__(self, required_approvers: int = 2):
        self.required_approvers = required_approvers
        self.pending_approvals: Dict[str, MultiSigAcceptance] = {}

    def request_approval(
        self,
        criterion: ExecutableAcceptanceCriterion,
        authorized_approvers: Set[str]
    ) -> str:
        """Request multi-sig approval for critical criterion"""
        approval_id = str(uuid4())

        multi_sig = MultiSigAcceptance(
            required_signatures=self.required_approvers,
            authorized_verifiers=authorized_approvers
        )

        self.pending_approvals[approval_id] = multi_sig

        # Notify approvers via NATS
        self.nats.publish("approval.requested", {
            'approval_id': approval_id,
            'criterion_id': criterion.criterion_id,
            'authorized_approvers': list(authorized_approvers)
        })

        return approval_id

    async def approve(
        self,
        approval_id: str,
        approver_id: str,
        signature: str
    ) -> bool:
        """Add approval signature"""
        if approval_id not in self.pending_approvals:
            raise ValueError(f"Unknown approval_id: {approval_id}")

        multi_sig = self.pending_approvals[approval_id]

        # Verify signature
        if not self._verify_signature(approver_id, signature):
            raise InvalidSignatureError()

        # Add signature
        multi_sig.sign(approver_id, signature)

        # Check if complete
        if multi_sig.is_accepted():
            # Move to approved
            await self._finalize_approval(approval_id)
            return True

        return False

    async def _finalize_approval(self, approval_id: str):
        """Finalize multi-sig approval"""
        multi_sig = self.pending_approvals.pop(approval_id)

        # Generate final proof
        proof = self._generate_final_proof(multi_sig)

        # Record in audit trail
        self.audit_trail.record_verification(proof)

        # Notify completion
        self.nats.publish("approval.completed", {
            'approval_id': approval_id,
            'signers': list(multi_sig.get_signers()),
            'merkle_root': self.audit_trail.merkle_tree.root
        })
```

---

## 7. Holistic Quality Metrics Integration

### 7.1 Comprehensive Validation Dashboard

```python
class ValidationDashboard:
    """Real-time validation dashboard with all metrics"""

    def __init__(self):
        self.acceptance_engine = ExecutableAcceptanceCriterion()
        self.quality_collector = QualityMetricsCollector()
        self.audit_trail = VerificationAuditTrail()

    def generate_report(self, project_id: UUID) -> ValidationReport:
        """Generate comprehensive validation report"""
        # Collect acceptance criteria verification
        acceptance = self._verify_all_acceptance_criteria(project_id)

        # Collect quality metrics
        coverage = self._compute_coverage(project_id)
        complexity = self._compute_complexity(project_id)
        security = self._compute_security(project_id)
        performance = self._compute_performance(project_id)

        # Collect verification proofs
        proofs = self.audit_trail.get_full_audit_trail(project_id)

        # Determine overall status
        overall_status = self._determine_status(
            acceptance, coverage, complexity, security, performance
        )

        return ValidationReport(
            acceptance_criteria=acceptance,
            coverage_metrics=coverage,
            complexity_metrics=complexity,
            security_metrics=security,
            performance_metrics=performance,
            verification_proofs=proofs,
            overall_status=overall_status,
            merkle_root=self.audit_trail.merkle_tree.root
        )

    def _compute_coverage(self, project_id: UUID) -> Dict[str, QualityMetric]:
        """Compute test coverage metrics"""
        # Integration with coverage.py
        cov = coverage.Coverage()
        cov.load()

        return {
            'line_coverage': QualityMetric(
                name='Line Coverage',
                value=cov.report(),
                threshold=80.0,
                status=MetricStatus.PASS if cov.report() >= 80 else MetricStatus.FAIL
            ),
            'branch_coverage': QualityMetric(
                name='Branch Coverage',
                value=cov.branch_coverage(),
                threshold=70.0,
                status=MetricStatus.PASS if cov.branch_coverage() >= 70 else MetricStatus.FAIL
            )
        }

    def _compute_complexity(self, project_id: UUID) -> Dict[str, QualityMetric]:
        """Compute code complexity metrics"""
        # Integration with radon
        results = radon.complexity.cc_visit(self._get_codebase())

        avg_complexity = sum(r.complexity for r in results) / len(results)

        return {
            'cyclomatic_complexity': QualityMetric(
                name='Avg Cyclomatic Complexity',
                value=avg_complexity,
                threshold=10.0,
                status=MetricStatus.PASS if avg_complexity <= 10 else MetricStatus.WARN
            ),
            'maintainability_index': QualityMetric(
                name='Maintainability Index',
                value=radon.metrics.mi_visit(self._get_codebase()),
                threshold=20.0,
                status=MetricStatus.PASS
            )
        }

    def _compute_security(self, project_id: UUID) -> Dict[str, QualityMetric]:
        """Compute security metrics"""
        # Integration with bandit
        results = bandit.run_bandit(self._get_codebase())

        return {
            'high_severity_issues': QualityMetric(
                name='High Severity Issues',
                value=len(results.high_severity),
                threshold=0,
                status=MetricStatus.FAIL if len(results.high_severity) > 0 else MetricStatus.PASS
            ),
            'medium_severity_issues': QualityMetric(
                name='Medium Severity Issues',
                value=len(results.medium_severity),
                threshold=5,
                status=MetricStatus.WARN if len(results.medium_severity) > 5 else MetricStatus.PASS
            )
        }

    def real_time_monitoring(self):
        """Real-time monitoring with NATS pub/sub"""
        async def monitor_loop():
            # Subscribe to relevant events
            await self.nats.subscribe("code.changed", self._on_code_change)
            await self.nats.subscribe("test.executed", self._on_test_executed)
            await self.nats.subscribe("verification.completed", self._on_verification)

            while True:
                await asyncio.sleep(1)

        asyncio.run(monitor_loop())
```

---

## 8. Implementation Phases

### Phase 1: Foundation (Weeks 1-4)

**Goals**: Core infrastructure, basic versioning, simple verification

**Deliverables**:
1. Multi-view data model and storage (SQLite + SQLAlchemy)
2. Event sourcing infrastructure with NATS
3. Basic commit/tree structure (Git-style)
4. Simple acceptance criteria execution
5. CLI with Typer + Rich

**Success Criteria**:
- Can create items across 16 views
- Can commit changes and view history
- Basic acceptance criteria verification works
- CLI functional for core operations

### Phase 2: Versioning & History (Weeks 5-8)

**Goals**: Complete Git-style versioning with time-travel

**Deliverables**:
1. Snapshot system for performance
2. Semantic versioning automation
3. Baseline/tag management
4. Time-travel engine
5. Branch/merge strategies
6. Diff visualization

**Success Criteria**:
- Can time-travel to any point in history
- Automatic version bumping from commits
- Can create and restore baselines
- Diff between versions works correctly

### Phase 3: PM Planning (Weeks 9-12)

**Goals**: PERT/WBS/DAG planning features

**Deliverables**:
1. WBS decomposition engine
2. PERT critical path calculation
3. DAG execution with NATS
4. Resource allocation
5. Schedule optimization
6. Gantt visualization (TUI with Textual)

**Success Criteria**:
- WBS auto-generated from features
- Critical path computed correctly
- DAG tasks execute in parallel
- Resource conflicts detected

### Phase 4: Verification & Quality (Weeks 13-16)

**Goals**: NL assertions and smart contract validation

**Deliverables**:
1. IntentGuard integration
2. Hybrid verification (NL + formal methods)
3. Cryptographic proof generation
4. Merkle tree audit trail
5. Multi-sig approval workflows
6. Quality metrics dashboard

**Success Criteria**:
- NL assertions verified with >85% confidence
- All verifications have cryptographic proofs
- Audit trail immutable and verifiable
- Quality dashboard shows real-time metrics

### Phase 5: Integration & Polish (Weeks 17-20)

**Goals**: Integration, optimization, documentation

**Deliverables**:
1. Continuous verification (watch mode)
2. Neo4j integration for graph queries
3. Performance optimization (caching, parallel processing)
4. Comprehensive documentation
5. Example projects and tutorials
6. Migration tools from other PM systems

**Success Criteria**:
- Real-time verification as code changes
- Graph queries <100ms
- All operations meet performance targets
- Complete documentation
- 3+ example projects

---

## 9. Performance Targets

### 9.1 Latency Requirements

| Operation | Target | Rationale |
|-----------|--------|-----------|
| View Switch | <50ms | Instant feel for power users |
| Item Creation | <200ms | Including intelligent CRUD |
| Commit | <100ms | For 100 items changed |
| Time-Travel | <500ms | Snapshot + replay |
| Diff Computation | <1s | Between any two versions |
| Critical Path | <2s | For 1000 task DAG |
| NL Verification | <5s | Per acceptance criterion |
| Full Validation | <30s | All metrics + verification |

### 9.2 Scalability Targets

| Dimension | Target | Strategy |
|-----------|--------|----------|
| Items per View | 10,000+ | Pagination + virtual scrolling |
| Total Items | 100,000+ | Sharding by view type |
| Commit History | Unlimited | Snapshot every 100 commits |
| Concurrent Agents | 1000+ | NATS horizontal scaling |
| DAG Task Nodes | 10,000+ | Distributed execution |
| Verification Proofs | Unlimited | Merkle tree compression |

### 9.3 Optimization Strategies

```python
# Caching Strategy
class PerformanceOptimizer:
    """Multi-layer caching for performance"""

    def __init__(self):
        # L1: In-memory LRU cache
        self.memory_cache = LRUCache(maxsize=1000)

        # L2: Redis for distributed caching (optional)
        self.redis_cache = RedisCache() if REDIS_AVAILABLE else None

        # L3: SQLite for disk cache
        self.disk_cache = SQLiteCache()

    def get_with_cache(self, key: str, compute_fn: Callable) -> Any:
        """Multi-tier cache lookup"""
        # Check L1
        if key in self.memory_cache:
            return self.memory_cache[key]

        # Check L2
        if self.redis_cache:
            value = self.redis_cache.get(key)
            if value:
                self.memory_cache[key] = value
                return value

        # Check L3
        value = self.disk_cache.get(key)
        if value:
            self.memory_cache[key] = value
            if self.redis_cache:
                self.redis_cache.set(key, value)
            return value

        # Compute and cache
        value = compute_fn()
        self.memory_cache[key] = value
        if self.redis_cache:
            self.redis_cache.set(key, value)
        self.disk_cache.set(key, value)

        return value
```

---

## 10. Deployment Architecture

### 10.1 Local Development (FastMCP stdio)

```bash
# Start trace MCP server
uv run python server.py

# Claude Code connects via stdio
# Multiple agents can share same server
```

### 10.2 Production (NATS Cluster)

```
┌─────────────────────────────────────────────────────────────┐
│                     Load Balancer (nginx)                    │
└────────────────────────┬────────────────────────────────────┘
                         │
         ┌───────────────┼───────────────┐
         │               │               │
┌────────▼────────┐ ┌───▼────────┐ ┌───▼────────┐
│  trace Server   │ │  trace     │ │  trace     │
│  Instance 1     │ │  Instance 2│ │  Instance 3│
└────────┬────────┘ └───┬────────┘ └───┬────────┘
         │               │               │
         └───────────────┼───────────────┘
                         │
         ┌───────────────▼────────────────┐
         │      NATS Cluster (3 nodes)     │
         │  - JetStream persistence        │
         │  - KV store for state          │
         │  - Pub/Sub coordination        │
         └───────────────┬────────────────┘
                         │
         ┌───────────────┴────────────────┐
         │                                │
┌────────▼─────────┐         ┌──────────▼─────────┐
│  PostgreSQL      │         │  Neo4j (optional)  │
│  (primary data)  │         │  (graph queries)   │
└──────────────────┘         └────────────────────┘
```

---

## 11. Summary: Why This Architecture

### 11.1 For Power Users (Orchestrating 1000+ Agents)

✅ **Maximum Features**: Every enterprise PM capability without compromise
✅ **Agent-Optimized**: NATS-based coordination, event-driven, distributed
✅ **Verification-First**: Cryptographic proofs for all changes
✅ **Time-Aware**: Complete history, time-travel to any point
✅ **Quality-Driven**: Holistic metrics continuously monitored

### 11.2 Technical Excellence

✅ **Git-Style Versioning**: Proven model, content-addressable, immutable
✅ **Event Sourcing**: Complete audit trail, replay capability
✅ **Formal Verification**: Smart contract-style validation
✅ **NL Assertions**: Natural language acceptance criteria
✅ **Distributed Coordination**: NATS for 1000+ agent orchestration

### 11.3 Implementation Path

✅ **Start Simple**: SQLite + local NATS
✅ **Scale Incrementally**: PostgreSQL + NATS cluster
✅ **Add Features Progressively**: Each phase builds on previous
✅ **No Technical Debt**: Clean architecture from day one

---

## Next Steps

1. **Review & Approve Architecture**: Validate approach aligns with vision
2. **Begin Phase 1 Implementation**: Core infrastructure and basic versioning
3. **Establish CI/CD Pipeline**: Automated testing and verification
4. **Create Example Projects**: Demonstrate capabilities
5. **Document as We Build**: Keep docs synchronized with implementation

This architecture provides a production-ready foundation for a comprehensive multi-view PM system optimized for orchestrating 1000+ AI agents with maximum robustness and features.
