buildwellai-model-v2 / BuildwellTHREAD_Complete_Specification.md

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	# BuildwellTHREAD: Complete System Specification
	## Multi-Agent AI System for UK Building Regulations Compliance & Golden Thread Management

	---

	Document Version: 1.0
	Date: January 2026
	Status: Development Specification
	Target Platform: Isambard AI Supercomputer (Nvidia GH200 Grace-Hopper)
	Project Lead: London Belgravia Surveyors

	---

	## EXECUTIVE SUMMARY

	BuildwellTHREAD is a comprehensive AI-powered system designed to automate UK Building Regulations compliance checking for Gateway 2 and Gateway 3 submissions, with integrated Golden Thread documentation management. The system addresses the critical challenge of high rejection rates at Building Safety Regulator (BSR) gateways while streamlining the compliance verification process.

	### Key Objectives

	1. Reduce Gateway Rejection Rates: Target reduction from current 40-67% to below 20%
	2. Comprehensive Coverage: All Building Regulations Parts A-T with specialized agents
	3. Golden Thread Integration: Automated generation and maintenance per Building Safety Act requirements
	4. Professional Standards: Compliance commentary aligned with RBI/Chartered Building Engineer code of conduct
	5. Processing Efficiency: Automated analysis replacing weeks of manual review with hours of AI-assisted checking

	### System Capabilities

	- Multi-agent architecture with 20+ specialized compliance agents
	- Multimodal document processing (text, drawings, specifications)
	- Spatial reasoning and measurement from architectural plans
	- Regulatory knowledge retrieval from Approved Documents and standards
	- Product verification against BBA and KIWA databases
	- Gateway 2/3 document organization and validation
	- Golden Thread documentation generation and management

	---

	## 1. SYSTEM FUNCTIONS

	### 1.1 Core Processing Functions

	#### Gateway 2 Submission Processing
	- Document Ingestion: Accept multi-format submissions (PDF, DWG, specifications)
	- Document Classification: Automatically categorize by document type and Building Regulation relevance
	- Completeness Validation: Check against Gateway 2 submission requirements per Build UK guidance
	- Compliance Analysis: Execute multi-agent compliance checking across all applicable Parts A-T
	- Report Generation: Produce comprehensive compliance commentary with evidence trails
	- Gap Identification: Flag missing information or non-compliances with specific recommendations

	#### Gateway 3 Support
	- Completion Certificate Readiness: Assess documentation completeness for Gateway 3 submission
	- As-Built Validation: Compare as-built documentation against approved Gateway 2 submission
	- Change Documentation: Track and validate variations from original approval
	- Final Compliance Verification: Confirm all conditions satisfied and works completed as approved

	#### Golden Thread Management
	- Structured Information Creation: Generate Golden Thread documentation per Building Safety Act requirements
	- Version Control: Maintain change history throughout project lifecycle
	- Stakeholder Access Management: Provide appropriate access levels for different project participants
	- BSR Portal Integration: Format documentation for submission to Building Safety Regulator systems
	- Lifecycle Maintenance: Support ongoing updates during construction and post-occupation phases

	### 1.2 Analysis Functions

	#### Spatial Analysis
	- Measurement Operations: Calculate lengths, areas, volumes, clearances from submitted drawings
	- Spatial Relationship Modeling: Understand topological relationships between building elements
	- Path Analysis: Evaluate circulation routes, means of escape, accessible routes
	- Visibility Analysis: Assess sightlines, protected views, overlooking considerations
	- Proximity Calculations: Measure distances between elements for compliance checking

	#### Compliance Verification
	- Rule-Based Checking: Validate explicit requirements (dimensions, ratings, specifications)
	- Performance-Based Assessment: Analyze designs against performance objectives
	- Cross-Part Validation: Identify conflicts between different Building Regulation requirements
	- Scenario Modeling: Simulate occupancy, egress, acoustic performance, energy use
	- Product Validation: Verify specified products against approved standards and certifications

	#### Documentation Functions
	- Evidence Collection: Link findings to specific submission documents with page/drawing references
	- Regulatory Citation: Reference specific Approved Document sections, British Standards, guidance
	- Recommendation Generation: Provide actionable suggestions for addressing identified issues
	- Confidence Scoring: Assign confidence levels to automated determinations
	- Escalation Flagging: Identify cases requiring expert human review

	---

	## 2. DEVELOPMENT APPROACH

	### 2.1 Development Philosophy

	#### Comprehensive Coverage
	- All Parts A-T: System addresses entire Building Regulations scope, not selective subset
	- No Gaps: Ensure no regulation part can be overlooked in analysis
	- Interconnected Requirements: Recognize cross-part dependencies and interactions
	- Future-Proof: Architecture supports addition of new requirements as regulations evolve

	#### Realistic Assessment Over Optimism
	- Critical Evaluation: Identify genuine challenges and limitations upfront
	- Feasibility Focus: Prioritize proven technologies and achievable goals
	- Transparent Limitations: Clearly communicate what system can and cannot determine
	- Conservative Bias: Err toward stricter interpretation when regulatory ambiguity exists

	#### Detailed Specification Before Implementation
	- Thorough Planning: Complete architectural design before coding begins
	- Clear Interfaces: Define data structures and APIs between components
	- Test Cases: Develop validation scenarios based on real-world submissions
	- Iterative Refinement: Plan for learning and improvement from initial deployments

	### 2.2 Processing Methodology

	#### Pipeline Architecture
	```
	Submission → Ingestion → Classification → Spatial Extraction →
	Storage → Retrieval → Multi-Agent Analysis → Validation →
	Report Generation → Golden Thread Update → Output
	```

	#### Sequential Processing Stages

	Stage 1: Document Ingestion (5-10 minutes)
	- Parse PDF submissions
	- Extract text content and metadata
	- Segment architectural drawings from specifications
	- Initial quality assessment
	- Generate document inventory

	Stage 2: Spatial Processing (15-30 minutes)
	- Run FloorplanTransformation on architectural drawings
	- Extract vector representations of building elements
	- Classify rooms, walls, doors, stairs, structural elements
	- Calculate dimensions and spatial relationships
	- Build graph database of building topology

	Stage 3: Knowledge Retrieval (Concurrent)
	- Query RAG system for relevant regulatory requirements
	- Retrieve applicable Approved Document sections
	- Identify similar precedent cases
	- Pull product certification databases
	- Assemble context for each specialist agent

	Stage 4: Multi-Agent Analysis (20-40 minutes)
	- Dispatch submission to relevant Part-specific agents
	- Execute parallel compliance checking across all Parts
	- Perform cross-part validation
	- Aggregate findings and resolve conflicts
	- Generate evidence-based determinations

	Stage 5: Report Generation (5-10 minutes)
	- Synthesize multi-agent findings
	- Structure compliance commentary per Gateway requirements
	- Generate Golden Thread documentation
	- Package outputs for delivery
	- Create audit trail

	Total Processing Time: 45-90 minutes per submission

	### 2.3 Deployment Considerations

	#### Platform: Isambard AI Supercomputer
	- Hardware: Nvidia GH200 Grace-Hopper superchips
	- Operating System: Linux-based (Ubuntu or similar)
	- Advantages: Massive parallel processing capability, high-bandwidth memory, ideal for large language models
	- Challenges: Windows software compatibility (FloorplanTransformation)

	#### Windows Dependency Resolution
	Problem: FloorplanTransformation tool is Windows-only
	Solution Options:
	1. Wine/Proton Compatibility Layer: Run Windows executable on Linux via Wine emulator
	2. Separate Windows Preprocessing Server: Dedicated Windows VM for FloorplanTransformation, feed results to Linux main system
	3. Alternative Tool Development: Develop Linux-native replacement using computer vision models
	4. Hybrid Approach: Use FloorplanTransformation for training data generation, deploy alternative for production

	#### Geographic Calibration
	- Ensure cost databases reflect UK construction pricing
	- Validate dimensional standards against UK practice (metric, UK-specific standards)
	- Align terminology with UK Building Regulations language
	- Reference UK-specific product certification bodies (BBA, KIWA, BSI)

	---

	## 3. SYSTEM ARCHITECTURE

	### 3.1 High-Level Architecture

	```
	┌─────────────────────────────────────────────────────────────────┐
	│ SUBMISSION INTERFACE │
	│ - Web portal / API for document upload │
	│ - Authentication and project management │
	│ - Progress tracking and notifications │
	└─────────────────────────┬───────────────────────────────────────┘
	│
	↓
	┌─────────────────────────────────────────────────────────────────┐
	│ DOCUMENT INGESTION & PROCESSING LAYER │
	│ │
	│ ┌──────────────┐ ┌──────────────┐ ┌────────────────┐ │
	│ │ PDF Parser │ │ OCR Engine │ │ Document │ │
	│ │ (PyMuPDF) │ │ (if needed) │ │ Classifier │ │
	│ └──────────────┘ └──────────────┘ └────────────────┘ │
	│ ↓ ↓ ↓ │
	│ ┌──────────────────────────────────────────────────────┐ │
	│ │ FloorplanTransformation Engine │ │
	│ │ (Windows compatibility via Wine/separate server) │ │
	│ │ → Extracts spatial vector data from drawings │ │
	│ └──────────────────────────────────────────────────────┘ │
	│ ↓ │
	│ ┌──────────────────────────────────────────────────────┐ │
	│ │ Spatial Analysis & Graph Building │ │
	│ │ → Identifies rooms, walls, doors, stairs │ │
	│ │ → Calculates measurements and relationships │ │
	│ │ → Constructs building topology graph │ │
	│ └──────────────────────────────────────────────────────┘ │
	│ │
	└─────────────────────────┬───────────────────────────────────────┘
	│
	↓
	┌─────────────────────────────────────────────────────────────────┐
	│ STORAGE LAYER │
	│ │
	│ ┌────────────────┐ ┌────────────────┐ ┌──────────────┐ │
	│ │ Vector Store │ │ Graph Database │ │ Relational DB│ │
	│ │ (Milvus) │ │ (Neo4j) │ │ (PostgreSQL) │ │
	│ │ │ │ │ │ │ │
	│ │ • Document │ │ • Spatial │ │ • Metadata │ │
	│ │ embeddings │ │ topology │ │ • Projects │ │
	│ │ • Regulatory │ │ • Compliance │ │ • Users │ │
	│ │ text │ │ chains │ │ • Products │ │
	│ │ • Precedents │ │ • Golden Thread│ │ • History │ │
	│ └────────────────┘ └────────────────┘ └──────────────┘ │
	│ │
	└─────────────────────────┬───────────────────────────────────────┘
	│
	↓
	┌─────────────────────────────────────────────────────────────────┐
	│ RAG SYSTEM (Multimodal) │
	│ │
	│ ┌────────────────────────────────────────────────────────┐ │
	│ │ Text Retrieval System │ │
	│ │ • Approved Documents A-T (chunked & embedded) │ │
	│ │ • British Standards (BS, BS EN) │ │
	│ │ • Guidance documents (BSR, NHBC, etc.) │ │
	│ │ • Historical precedents and interpretations │ │
	│ └────────────────────────────────────────────────────────┘ │
	│ │
	│ ┌────────────────────────────────────────────────────────┐ │
	│ │ Visual Retrieval System │ │
	│ │ • Approved Document diagrams (extracted & indexed) │ │
	│ │ • Reference construction details │ │
	│ │ • Visual similarity search for diagram comparison │ │
	│ └────────────────────────────────────────────────────────┘ │
	│ │
	│ ┌────────────────────────────────────────────────────────┐ │
	│ │ Product Database │ │
	│ │ • BBA approved products │ │
	│ │ • KIWA certifications │ │
	│ │ • CE marking database │ │
	│ │ • Manufacturer specifications │ │
	│ └────────────────────────────────────────────────────────┘ │
	│ │
	└─────────────────────────┬───────────────────────────────────────┘
	│
	↓
	┌─────────────────────────────────────────────────────────────────┐
	│ MULTI-AGENT COMPLIANCE FRAMEWORK │
	│ │
	│ ┌────────────────────────────────────────────────────────┐ │
	│ │ Master Orchestrator Agent │ │
	│ │ • Submission analysis & routing │ │
	│ │ • Agent coordination │ │
	│ │ • Conflict resolution │ │
	│ │ • Report synthesis │ │
	│ └────────────────────────────────────────────────────────┘ │
	│ ↓ │
	│ ┌────────────────┼────────────────┐ │
	│ ↓ ↓ ↓ │
	│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
	│ │ Part A │ │ Part B │ │ Part C │...│ Part T │ │
	│ │ Agent │ │ Agent │ │ Agent │ │ Agent │ │
	│ │ │ │ │ │ │ │ │ │
	│ │ Structure│ │ Fire │ │ Site/ │ │ Broadband│ │
	│ │ │ │ Safety │ │ Moisture │ │ │ │
	│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
	│ │
	│ Each agent has: │
	│ • Specialized knowledge base (RAG-retrieved) │
	│ • Domain-specific tools │
	│ • Compliance checking logic │
	│ • Standardized output format │
	│ │
	└─────────────────────────┬───────────────────────────────────────┘
	│
	↓
	┌─────────────────────────────────────────────────────────────────┐
	│ TOOL LAYER │
	│ │
	│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────┐ │
	│ │ Measurement │ │ Spatial Reasoning│ │ Scenario │ │
	│ │ Tools │ │ Tools │ │ Planning │ │
	│ │ │ │ │ │ Tools │ │
	│ │ • Distances │ │ • Path finding │ │ • Egress │ │
	│ │ • Areas │ │ • Adjacency │ │ • Acoustic │ │
	│ │ • Volumes │ │ • Containment │ │ • Energy │ │
	│ │ • Clearances │ │ • Visibility │ │ • Occupancy │ │
	│ └──────────────────┘ └──────────────────┘ └──────────────┘ │
	│ │
	│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────┐ │
	│ │ Comparison │ │ Product Checker │ │ Calculation │ │
	│ │ Tools │ │ Tools │ │ Tools │ │
	│ │ │ │ │ │ │ │
	│ │ • Design vs │ │ • BBA database │ │ • U-values │ │
	│ │ diagrams │ │ • KIWA database │ │ • SAP/SBEM │ │
	│ │ • As-built vs │ │ • CE marking │ │ • Loads │ │
	│ │ approved │ │ • Standards │ │ • Acoustics │ │
	│ └──────────────────┘ └──────────────────┘ └──────────────┘ │
	│ │
	└─────────────────────────┬───────────────────────────────────────┘
	│
	↓
	┌─────────────────────────────────────────────────────────────────┐
	│ OUTPUT GENERATION LAYER │
	│ │
	│ ┌────────────────────────────────────────────────────────┐ │
	│ │ Compliance Report Generator │ │
	│ │ • Gateway 2/3 format compliance commentary │ │
	│ │ • Evidence trails and regulatory citations │ │
	│ │ • Confidence scores and escalation flags │ │
	│ │ • Actionable recommendations │ │
	│ └────────────────────────────────────────────────────────┘ │
	│ │
	│ ┌────────────────────────────────────────────────────────┐ │
	│ │ Golden Thread Documentation Generator │ │
	│ │ • Structured information per Building Safety Act │ │
	│ │ • Version control and change tracking │ │
	│ │ • Stakeholder-appropriate views │ │
	│ │ • BSR portal formatted outputs │ │
	│ └────────────────────────────────────────────────────────┘ │
	│ │
	│ ┌────────────────────────────────────────────────────────┐ │
	│ │ Visualization & Evidence System │ │
	│ │ • Issue highlighting on drawings │ │
	│ │ • Interactive compliance dashboard │ │
	│ │ • Evidence package assembly │ │
	│ └────────────────────────────────────────────────────────┘ │
	│ │
	└─────────────────────────────────────────────────────────────────┘
	```

	### 3.2 Component Details

	#### 3.2.1 Document Ingestion Layer

	Purpose: Accept and process diverse submission documents into standardized formats

	Components:
	- PDF Parser (PyMuPDF): Extract text, images, metadata from PDF submissions
	- OCR Engine (Tesseract/PaddleOCR): Handle scanned or low-quality documents
	- Document Classifier: Categorize documents by type (architectural plans, specifications, calculations, reports)
	- Page Segmentation: Separate multi-page documents into logical sections
	- Quality Assessment: Flag low-quality or incomplete documents early

	Outputs:
	- Structured text content
	- Extracted images (drawings, photos, diagrams)
	- Document metadata (title, author, date, version)
	- Document classification labels
	- Quality scores

	#### 3.2.2 Spatial Processing Layer

	Purpose: Convert raster architectural drawings into structured spatial representations

	FloorplanTransformation Engine:
	- Input: PDF/image format architectural plans
	- Process: Computer vision-based element detection and vectorization
	- Output: Vector representations of building elements with classifications

	Linux Compatibility Solutions:
	1. Wine Emulator: Run Windows .exe via compatibility layer
	2. Dedicated Windows Server: Separate preprocessing service
	3. API Wrapper: Encapsulate Windows tool behind REST API
	4. Alternative CV Models: Develop replacement using open-source tools

	Spatial Analysis Pipeline:
	```python
	# Pseudo-code example
	def process_floorplan(pdf_path: str) -> SpatialData:
	# Extract plan images from PDF
	images = extract_plan_images(pdf_path)

	# Run FloorplanTransformation (via Wine or API)
	vector_data = floorplan_transformation(images)

	# Classify elements
	rooms = classify_rooms(vector_data)
	walls = classify_walls(vector_data)
	doors = classify_doors(vector_data)
	stairs = classify_stairs(vector_data)

	# Calculate measurements
	measurements = calculate_dimensions(vector_data)

	# Build topology
	topology = build_spatial_graph(rooms, walls, doors, stairs)

	return SpatialData(
	rooms=rooms,
	walls=walls,
	doors=doors,
	stairs=stairs,
	measurements=measurements,
	topology=topology
	)
	```

	Graph Database Population:
	- Create nodes for each spatial element (rooms, doors, stairs, etc.)
	- Define relationships (connects, contains, adjacent_to, above, below)
	- Store geometric properties (dimensions, coordinates, areas)
	- Enable graph queries for compliance checking

	#### 3.2.3 Storage Layer

	Milvus Vector Database:
	- Purpose: Store and retrieve document embeddings for semantic search
	- Contents:
	- Regulatory text embeddings (Approved Documents, Standards)
	- Submission document embeddings
	- Historical precedent embeddings
	- Visual embeddings (diagram similarity)
	- Indexing: HNSW (Hierarchical Navigable Small World) for fast approximate nearest neighbor search
	- Partitioning: Separate collections per Building Regulation Part for efficient retrieval

	Neo4j Graph Database:
	- Purpose: Model spatial relationships and compliance chains
	- Node Types:
	- Building elements (rooms, walls, doors, stairs)
	- Compliance requirements (regulations, sub-clauses)
	- Evidence items (document references, measurements)
	- Golden Thread information items
	- Relationship Types:
	- Spatial: connects, contains, adjacent_to, above, below
	- Compliance: requires, satisfies, conflicts_with
	- Evidence: references, supports, contradicts
	- Provenance: derived_from, modified_from, supersedes
	- Query Patterns:
	- Path finding (shortest egress route)
	- Subgraph extraction (all elements in fire compartment)
	- Dependency tracking (requirements affected by design change)

	PostgreSQL Relational Database:
	- Purpose: Store structured metadata and operational data
	- Schema:
	- Projects (ID, name, address, client, status)
	- Submissions (ID, project, gateway, date, status)
	- Users (authentication, roles, permissions)
	- Products (manufacturer, model, certifications)
	- Audit logs (actions, timestamps, users)
	- Processing history (submission, agent, runtime, outcome)

	#### 3.2.4 RAG System (Retrieval-Augmented Generation)

	Text Retrieval System:

	Indexing Strategy:
	```
	Approved Documents → Chunked by regulation section → Embedded → Indexed in Milvus
	↓
	Each chunk includes:
	- Text content
	- Regulation reference (e.g., "Part B1, Section 2.5")
	- Document source
	- Effective date
	- Related diagrams/tables
	- Keywords
	```

	Embedding Model:
	- NV-Embed-v2 or similar high-performance model
	- 768-dimensional dense vectors
	- Trained on technical/regulatory text for domain relevance

	Retrieval Process:
	1. Query Construction: Agent formulates query based on compliance check needed
	2. Embedding: Query embedded using same model as corpus
	3. Vector Search: Retrieve top-K most similar chunks from Milvus
	4. Reranking (optional): Use cross-encoder model to refine results
	5. Context Assembly: Combine retrieved chunks with query for LLM

	Visual Retrieval System:

	Diagram Corpus:
	- Extract all diagrams from Approved Documents
	- Annotate with metadata (Part, section, diagram number, caption)
	- Generate visual embeddings (CLIP or similar vision-language model)
	- Index in separate Milvus collection

	Visual Similarity Search:
	- Compare submitted designs against reference diagrams
	- Identify similar configurations or details
	- Flag deviations from approved construction methods

	Product Database Integration:
	- BBA Certificates: Regularly updated database of approved products
	- KIWA Certifications: European product approvals relevant to UK
	- CE Marking Database: Products complying with European standards
	- Search Functionality: Query by product name, manufacturer, application, performance criteria

	#### 3.2.5 Multi-Agent Framework

	Master Orchestrator Agent:

	Responsibilities:
	1. Submission Analysis: Determine which Building Regulation Parts apply
	2. Agent Dispatching: Route submission to relevant specialist agents
	3. Parallel Coordination: Manage concurrent agent execution
	4. Conflict Resolution: Identify and resolve inter-agent conflicts
	5. Report Synthesis: Aggregate findings into unified output

	Process Flow:
	```python
	def process_submission(submission: Submission) -> ComplianceReport:
	# 1. Analyze submission
	relevant_parts = identify_applicable_parts(submission)

	# 2. Dispatch to agents (parallel execution)
	agent_results = {}
	with ThreadPoolExecutor() as executor:
	futures = {
	executor.submit(agent.check_compliance, submission): part
	for part, agent in specialist_agents.items()
	if part in relevant_parts
	}

	for future in as_completed(futures):
	part = futures[future]
	agent_results[part] = future.result()

	# 3. Validate cross-part consistency
	conflicts = detect_conflicts(agent_results)

	# 4. Synthesize report
	report = synthesize_report(
	agent_results=agent_results,
	conflicts=conflicts,
	submission=submission
	)

	return report
	```

	Specialist Agents (Parts A-T):

	Agent Architecture (consistent across all Parts):

	```python
	class BuildingRegulationAgent:
	"""
	Base class for all Part-specific agents.
	"""

	def __init__(
	self,
	part_id: str,
	rag_system: RAGSystem,
	spatial_db: Neo4jClient,
	tools: ToolRegistry
	):
	self.part_id = part_id
	self.rag = rag_system
	self.spatial_db = spatial_db
	self.tools = tools

	# Load Part-specific knowledge
	self.knowledge_base = self._load_knowledge()

	def check_compliance(
	self,
	submission: Submission
	) -> List[ComplianceCheck]:
	"""
	Main entry point for compliance checking.

	Returns list of ComplianceCheck objects with:
	- Finding (pass/fail/unclear)
	- Evidence (specific references)
	- Reasoning (explanation)
	- Confidence score
	- Recommendations (if non-compliant)
	"""
	checks = []

	# 1. Extract relevant information
	relevant_data = self._extract_relevant_data(submission)

	# 2. Retrieve regulatory requirements
	requirements = self._get_requirements(relevant_data)

	# 3. Execute compliance checks
	for requirement in requirements:
	check = self._evaluate_compliance(
	requirement=requirement,
	data=relevant_data
	)
	checks.append(check)

	# 4. Cross-validate findings
	checks = self._cross_validate(checks)

	return checks

	def _load_knowledge(self) -> Dict:
	"""
	Load Part-specific knowledge from RAG system.
	"""
	# Retrieve relevant Approved Document sections
	# Retrieve applicable standards
	# Retrieve historical precedents
	pass

	def _get_requirements(self, data: Dict) -> List[Requirement]:
	"""
	Identify which specific requirements apply based on data.
	"""
	# Query RAG system for applicable requirements
	# Filter by building type, use, characteristics
	pass

	def _evaluate_compliance(
	self,
	requirement: Requirement,
	data: Dict
	) -> ComplianceCheck:
	"""
	Evaluate specific requirement against submission data.
	"""
	# Use tools to measure/calculate as needed
	# Compare against requirement criteria
	# Generate evidence trail
	# Assign confidence score
	pass
	```

	Agent Specializations:

	Each Part agent inherits from base class and adds specialized capabilities:

	Part A (Structure) Agent:
	- Load calculation tools
	- Structural material verification
	- Foundation adequacy assessment
	- Disproportionate collapse checking (for HRBs)

	Part B (Fire Safety) Agent:
	- Egress path modeling
	- Travel distance calculation
	- Compartmentation validation
	- Fire resistance verification
	- Occupancy load calculation

	Part C (Site Preparation) Agent:
	- Contamination assessment
	- Radon risk evaluation
	- Ground gas analysis
	- Moisture protection checking

	[Parts D-T]: Each with domain-specific tools and knowledge

	Agent Communication:
	- Shared access to spatial graph database
	- Message passing for cross-dependencies
	- Conflict notification to orchestrator
	- Evidence sharing between related agents

	#### 3.2.6 Tool Layer

	Measurement Tools:

	```python
	class MeasurementTools:
	"""
	Spatial measurement capabilities.
	"""

	def measure_distance(
	self,
	point_a: Coordinate,
	point_b: Coordinate,
	path_type: str = 'straight' # or 'along_route'
	) -> float:
	"""
	Calculate distance between points.
	"""
	if path_type == 'straight':
	return euclidean_distance(point_a, point_b)
	elif path_type == 'along_route':
	# Query graph DB for path
	path = self.spatial_db.shortest_path(point_a, point_b)
	return sum_path_length(path)

	def calculate_area(
	self,
	room_id: str
	) -> float:
	"""
	Calculate room floor area from polygon vertices.
	"""
	vertices = self.spatial_db.get_room_vertices(room_id)
	return polygon_area(vertices)

	def measure_clearance(
	self,
	element_id: str,
	direction: str
	) -> float:
	"""
	Measure clearance in specified direction.
	"""
	element_bounds = self.spatial_db.get_element_bounds(element_id)
	obstacles = self.spatial_db.find_obstacles(
	element_bounds,
	direction
	)
	return calculate_clearance(element_bounds, obstacles, direction)
	```

	Spatial Reasoning Tools:

	```python
	class SpatialReasoningTools:
	"""
	Higher-level spatial analysis.
	"""

	def find_egress_routes(
	self,
	start_room: str,
	building_id: str
	) -> List[EgressRoute]:
	"""
	Find all egress routes from room to building exit.
	"""
	query = """
	MATCH path = (start:Room {id: $start_room})-[:CONNECTS*]->(exit:Exit)
	WHERE exit.building_id = $building_id
	RETURN path
	ORDER BY length(path)
	"""
	paths = self.spatial_db.query(query, start_room=start_room, building_id=building_id)

	routes = []
	for path in paths:
	route = EgressRoute(
	path=path,
	distance=calculate_path_distance(path),
	doors=extract_doors_from_path(path),
	stairs=extract_stairs_from_path(path)
	)
	routes.append(route)

	return routes

	def check_accessibility(
	self,
	route: Route,
	requirements: AccessibilityRequirements
	) -> AccessibilityAssessment:
	"""
	Evaluate if route meets accessibility requirements.
	"""
	issues = []

	# Check widths
	for segment in route.segments:
	if segment.width < requirements.min_width:
	issues.append(f"Width {segment.width}mm < {requirements.min_width}mm at {segment.location}")

	# Check gradients
	for ramp in route.ramps:
	if ramp.gradient > requirements.max_gradient:
	issues.append(f"Gradient {ramp.gradient} > {requirements.max_gradient} at {ramp.location}")

	# Check door widths
	for door in route.doors:
	if door.clear_width < requirements.min_door_width:
	issues.append(f"Door width {door.clear_width}mm < {requirements.min_door_width}mm at {door.location}")

	return AccessibilityAssessment(
	compliant=(len(issues) == 0),
	issues=issues
	)
	```

	Scenario Planning Tools:

	```python
	class ScenarioPlanningTools:
	"""
	Simulation and modeling capabilities.
	"""

	def simulate_egress(
	self,
	building: Building,
	occupancy: int,
	fire_location: str
	) -> EgressSimulationResult:
	"""
	Simulate building evacuation scenario.
	"""
	# Initialize population
	occupants = distribute_occupants(building, occupancy)

	# Define fire spread
	fire_model = create_fire_model(building, fire_location)

	# Run evacuation simulation
	time_steps = []
	current_time = 0

	while not all_evacuated(occupants):
	# Move occupants toward exits
	occupants = move_occupants(occupants, building, fire_model)

	# Update fire spread
	fire_model = update_fire_model(fire_model, current_time)

	# Check for casualties
	casualties = check_casualties(occupants, fire_model)

	# Record state
	time_steps.append(SimulationState(
	time=current_time,
	occupants=occupants,
	fire=fire_model,
	casualties=casualties
	))

	current_time += SIMULATION_TIMESTEP

	if current_time > MAX_SIMULATION_TIME:
	break

	return EgressSimulationResult(
	evacuation_time=current_time,
	casualties=sum(ts.casualties for ts in time_steps),
	bottlenecks=identify_bottlenecks(time_steps),
	time_steps=time_steps
	)

	def model_acoustic_performance(
	self,
	wall: Wall,
	source_room: Room,
	receiver_room: Room
	) -> AcousticPerformance:
	"""
	Predict sound insulation performance.
	"""
	# Calculate direct transmission
	direct_loss = calculate_transmission_loss(wall.construction)

	# Calculate flanking paths
	flanking_loss = calculate_flanking_transmission(
	wall=wall,
	source_room=source_room,
	receiver_room=receiver_room
	)

	# Combine paths
	total_loss = combine_transmission_paths(direct_loss, flanking_loss)

	return AcousticPerformance(
	sound_reduction_index=total_loss,
	meets_requirement=total_loss >= REQUIREMENT_DnTw
	)
	```

	Comparison Tools:

	```python
	class ComparisonTools:
	"""
	Compare designs against reference standards.
	"""

	def compare_against_approved_diagram(
	self,
	submitted_design: SpatialData,
	reference_diagram_id: str
	) -> ComparisonResult:
	"""
	Compare submitted design against Approved Document diagram.
	"""
	# Retrieve reference diagram
	reference = self.rag.retrieve_diagram(reference_diagram_id)

	# Extract comparable features
	submitted_features = extract_features(submitted_design)
	reference_features = extract_features(reference)

	# Compare
	differences = []
	for feature_name, submitted_value in submitted_features.items():
	reference_value = reference_features.get(feature_name)

	if reference_value is None:
	differences.append(f"{feature_name}: Not in reference diagram")
	elif not values_match(submitted_value, reference_value, tolerance=0.05):
	differences.append(
	f"{feature_name}: Submitted {submitted_value} vs Reference {reference_value}"
	)

	return ComparisonResult(
	matches=(len(differences) == 0),
	differences=differences,
	similarity_score=calculate_similarity(submitted_features, reference_features)
	)

	def compare_as_built_to_approved(
	self,
	as_built: Submission,
	approved: Submission
	) -> ChangeReport:
	"""
	Identify changes between approved and as-built submissions.
	"""
	changes = []

	# Compare spatial data
	spatial_changes = self._compare_spatial_data(
	as_built.spatial_data,
	approved.spatial_data
	)
	changes.extend(spatial_changes)

	# Compare specifications
	spec_changes = self._compare_specifications(
	as_built.specifications,
	approved.specifications
	)
	changes.extend(spec_changes)

	# Categorize changes by significance
	critical_changes = [c for c in changes if c.affects_compliance]
	minor_changes = [c for c in changes if not c.affects_compliance]

	return ChangeReport(
	critical_changes=critical_changes,
	minor_changes=minor_changes,
	requires_revised_approval=(len(critical_changes) > 0)
	)
	```

	Product Verification Tools:

	```python
	class ProductVerificationTools:
	"""
	Check specified products against certification databases.
	"""

	def verify_bba_approval(
	self,
	product_name: str,
	manufacturer: str,
	application: str
	) -> ApprovalStatus:
	"""
	Check if product has current BBA approval.
	"""
	# Query BBA database
	results = self.bba_db.search(
	product=product_name,
	manufacturer=manufacturer,
	application=application
	)

	if not results:
	return ApprovalStatus(
	approved=False,
	message="Product not found in BBA database"
	)

	# Check approval is current
	most_recent = max(results, key=lambda r: r.certificate_date)

	if most_recent.expired:
	return ApprovalStatus(
	approved=False,
	message=f"BBA approval expired on {most_recent.expiry_date}",
	certificate_number=most_recent.certificate_number
	)

	return ApprovalStatus(
	approved=True,
	certificate_number=most_recent.certificate_number,
	certificate_date=most_recent.certificate_date,
	expiry_date=most_recent.expiry_date
	)

	def check_ce_marking(
	self,
	product: Product,
	standard: str
	) -> CEMarkingStatus:
	"""
	Verify product has CE marking to specified standard.
	"""
	# Query CE marking database
	marking = self.ce_db.get_marking(
	product_code=product.code,
	manufacturer_id=product.manufacturer_id
	)

	if not marking:
	return CEMarkingStatus(
	marked=False,
	message="No CE marking found for product"
	)

	if standard not in marking.standards:
	return CEMarkingStatus(
	marked=True,
	compliant=False,
	message=f"CE marked but not to required standard {standard}",
	standards=marking.standards
	)

	return CEMarkingStatus(
	marked=True,
	compliant=True,
	standards=marking.standards,
	notified_body=marking.notified_body
	)
	```

	#### 3.2.7 Output Generation Layer

	Compliance Report Generator:

	```python
	class ComplianceReportGenerator:
	"""
	Generate structured compliance reports for Gateway submissions.
	"""

	def generate_gateway2_report(
	self,
	submission: Submission,
	agent_results: Dict[str, List[ComplianceCheck]],
	conflicts: List[Conflict]
	) -> GatewayReport:
	"""
	Create comprehensive Gateway 2 compliance report.
	"""
	report = GatewayReport(
	submission_id=submission.id,
	date=datetime.now(),
	building=submission.building_details
	)

	# Executive summary
	report.executive_summary = self._generate_executive_summary(
	agent_results,
	conflicts
	)

	# Part-by-part findings
	for part, checks in agent_results.items():
	part_section = self._generate_part_section(
	part=part,
	checks=checks
	)
	report.add_section(part_section)

	# Cross-part issues
	if conflicts:
	conflict_section = self._generate_conflict_section(conflicts)
	report.add_section(conflict_section)

	# Recommendations
	report.recommendations = self._prioritize_recommendations(
	agent_results,
	conflicts
	)

	# Evidence package
	report.evidence = self._compile_evidence(agent_results)

	# Confidence assessment
	report.overall_confidence = self._calculate_overall_confidence(
	agent_results
	)

	return report

	def _generate_executive_summary(
	self,
	agent_results: Dict,
	conflicts: List
	) -> str:
	"""
	Create executive summary of key findings.
	"""
	total_checks = sum(len(checks) for checks in agent_results.values())

	compliant = sum(
	1 for checks in agent_results.values()
	for check in checks
	if check.status == "compliant"
	)

	non_compliant = sum(
	1 for checks in agent_results.values()
	for check in checks
	if check.status == "non_compliant"
	)

	requires_clarification = total_checks - compliant - non_compliant

	summary = f"""
	EXECUTIVE SUMMARY

	Total compliance checks performed: {total_checks}
	- Compliant: {compliant} ({compliant/total_checks*100:.1f}%)
	- Non-compliant: {non_compliant} ({non_compliant/total_checks*100:.1f}%)
	- Requires clarification: {requires_clarification} ({requires_clarification/total_checks*100:.1f}%)

	Cross-part conflicts identified: {len(conflicts)}

	CRITICAL ISSUES REQUIRING IMMEDIATE ATTENTION:
	"""

	# Add critical issues
	critical_issues = [
	check for checks in agent_results.values()
	for check in checks
	if check.status == "non_compliant" and check.priority == "critical"
	]

	for i, issue in enumerate(critical_issues, 1):
	summary += f"\n{i}. {issue.regulation}: {issue.requirement}"

	return summary

	def _generate_part_section(
	self,
	part: str,
	checks: List[ComplianceCheck]
	) -> ReportSection:
	"""
	Generate detailed section for specific Building Regulation Part.
	"""
	section = ReportSection(
	title=f"Part {part} - {PART_NAMES[part]}",
	checks=checks
	)

	# Group checks by sub-regulation
	grouped = self._group_checks_by_regulation(checks)

	for regulation, regulation_checks in grouped.items():
	subsection = f"\n\n### {regulation}\n\n"

	for check in regulation_checks:
	subsection += self._format_compliance_check(check)

	section.add_content(subsection)

	return section

	def _format_compliance_check(
	self,
	check: ComplianceCheck
	) -> str:
	"""
	Format individual compliance check for report.
	"""
	# Status icon
	if check.status == "compliant":
	status_icon = "✓"
	elif check.status == "non_compliant":
	status_icon = "✗"
	else:
	status_icon = "?"

	# Build formatted text
	text = f"""
	{status_icon} {check.requirement}

	Status: {check.status.upper()}
	Confidence: {check.confidence:.0%}
	Priority: {check.priority.upper()}

	Evidence:
	{self._format_evidence_list(check.evidence)}

	Reasoning:
	{check.reasoning}
	"""

	if check.recommendations:
	text += f"""

	Recommendations:
	{self._format_recommendations(check.recommendations)}
	"""

	if check.regulatory_references:
	text += f"""

	Regulatory References:
	{self._format_references(check.regulatory_references)}
	"""

	return text
	```

	Golden Thread Documentation Generator:

	```python
	class GoldenThreadGenerator:
	"""
	Generate and maintain Golden Thread documentation.
	"""

	def initialize_golden_thread(
	self,
	project: Project,
	gateway2_submission: Submission
	) -> GoldenThread:
	"""
	Create initial Golden Thread structure for project.
	"""
	golden_thread = GoldenThread(
	project_id=project.id,
	created_date=datetime.now(),
	current_version="1.0"
	)

	# Building Information
	golden_thread.building_info = self._extract_building_info(
	gateway2_submission
	)

	# Design Information
	golden_thread.design_info = self._extract_design_info(
	gateway2_submission
	)

	# Safety Case
	golden_thread.safety_case = self._generate_safety_case(
	gateway2_submission
	)

	# Compliance Record
	golden_thread.compliance_record = self._create_compliance_record(
	gateway2_submission
	)

	# Document Register
	golden_thread.document_register = self._create_document_register(
	gateway2_submission
	)

	return golden_thread

	def update_golden_thread(
	self,
	golden_thread: GoldenThread,
	change: DesignChange,
	authorization: Authorization
	) -> GoldenThread:
	"""
	Update Golden Thread with design change.
	"""
	# Create new version
	new_version = golden_thread.increment_version()

	# Record change
	new_version.add_change_record(
	date=datetime.now(),
	change_description=change.description,
	affected_elements=change.affected_elements,
	justification=change.justification,
	authorized_by=authorization.person,
	authorization_date=authorization.date
	)

	# Update affected sections
	if change.affects_building_info:
	new_version.building_info = self._update_building_info(
	golden_thread.building_info,
	change
	)

	if change.affects_design:
	new_version.design_info = self._update_design_info(
	golden_thread.design_info,
	change
	)

	if change.affects_safety_case:
	new_version.safety_case = self._update_safety_case(
	golden_thread.safety_case,
	change
	)

	# Re-validate compliance
	new_version.compliance_record = self._revalidate_compliance(
	new_version,
	change
	)

	# Archive previous version
	self._archive_version(golden_thread)

	return new_version

	def generate_bsr_submission_package(
	self,
	golden_thread: GoldenThread,
	gateway: str # "2" or "3"
	) -> BSRSubmissionPackage:
	"""
	Format Golden Thread for BSR portal submission.
	"""
	package = BSRSubmissionPackage(
	gateway=gateway,
	project_id=golden_thread.project_id,
	submission_date=datetime.now()
	)

	# Include required documents per gateway
	if gateway == "2":
	package.add_document("building_information", golden_thread.building_info)
	package.add_document("design_information", golden_thread.design_info)
	package.add_document("safety_case", golden_thread.safety_case)
	package.add_document("compliance_record", golden_thread.compliance_record)
	package.add_document("fire_strategy", golden_thread.fire_strategy)
	elif gateway == "3":
	package.add_document("completion_certificate_application", ...)
	package.add_document("as_built_information", golden_thread.as_built_info)
	package.add_document("change_log", golden_thread.change_log)
	package.add_document("final_compliance_verification", ...)
	package.add_document("operation_and_maintenance_manual", ...)

	# Format per BSR requirements
	package.format_for_bsr()

	return package
	```

	---

	## 4. AGENT DESIGN SPECIFICATIONS

	### 4.1 Standard Agent Design Pattern

	All Building Regulation Part agents follow this consistent pattern:

	```python
	from dataclasses import dataclass
	from typing import List, Dict, Optional
	from enum import Enum

	@dataclass
	class ComplianceCheck:
	"""
	Standard structure for compliance findings.
	"""
	regulation: str # e.g., "Part B1 - Means of Escape"
	requirement: str # What the regulation requires
	status: str # "compliant", "non_compliant", "insufficient_info"
	evidence: List[str] # Specific evidence from submission
	reasoning: str # Explanation of determination
	confidence: float # 0.0 to 1.0
	priority: str # "critical", "high", "medium", "low"
	recommendations: List[str] # Actions if non-compliant
	regulatory_references: List[str] # Approved Doc sections, standards
	related_checks: List[str] # Links to related findings


	class BuildingRegulationPartAgent:
	"""
	Template for all Part-specific agents.
	"""

	def __init__(
	self,
	part_id: str,
	rag_system: RAGSystem,
	spatial_analyzer: SpatialAnalyzer,
	tool_registry: ToolRegistry,
	llm_client: LLMClient
	):
	self.part_id = part_id
	self.rag = rag_system
	self.spatial = spatial_analyzer
	self.tools = tool_registry
	self.llm = llm_client

	# Load Part-specific knowledge
	self.knowledge_base = self._load_regulatory_knowledge()

	# Register Part-specific tools
	self.specialized_tools = self._register_specialized_tools()

	def check_compliance(
	self,
	submission: Submission,
	spatial_data: SpatialData,
	specifications: Dict
	) -> List[ComplianceCheck]:
	"""
	Main compliance checking entry point.

	Args:
	submission: Full Gateway submission
	spatial_data: Processed building spatial information
	specifications: Extracted technical specifications

	Returns:
	List of ComplianceCheck objects for all applicable requirements
	"""
	checks = []

	# 1. Identify applicable requirements
	requirements = self._identify_applicable_requirements(
	submission,
	spatial_data,
	specifications
	)

	# 2. Execute checks for each requirement
	for requirement in requirements:
	check = self._evaluate_requirement(
	requirement=requirement,
	submission=submission,
	spatial_data=spatial_data,
	specifications=specifications
	)
	checks.append(check)

	# 3. Cross-validate findings
	checks = self._cross_validate_checks(checks)

	# 4. Prioritize issues
	checks = self._prioritize_checks(checks)

	return checks

	def _load_regulatory_knowledge(self) -> Dict:
	"""
	Load Part-specific regulatory knowledge from RAG system.

	Returns:
	Dictionary containing:
	- Approved Document sections
	- Relevant British Standards
	- Guidance documents
	- Historical precedents
	- Reference diagrams
	"""
	# Query RAG for Part-specific content
	queries = self._generate_knowledge_queries()

	knowledge = {
	'approved_doc': [],
	'standards': [],
	'guidance': [],
	'precedents': [],
	'diagrams': []
	}

	for query in queries:
	# Text retrieval
	text_results = self.rag.retrieve_text(
	query=query,
	collection=f"part_{self.part_id.lower()}_text",
	top_k=20
	)
	knowledge['approved_doc'].extend(text_results)

	# Diagram retrieval
	diagram_results = self.rag.retrieve_diagrams(
	query=query,
	collection=f"part_{self.part_id.lower()}_diagrams",
	top_k=5
	)
	knowledge['diagrams'].extend(diagram_results)

	return knowledge

	def _identify_applicable_requirements(
	self,
	submission: Submission,
	spatial_data: SpatialData,
	specifications: Dict
	) -> List[Requirement]:
	"""
	Determine which specific requirements apply to this building.

	Requirements vary based on:
	- Building type (dwelling, commercial, HRB, etc.)
	- Building use
	- Building characteristics (height, occupancy, etc.)
	- Design approach (prescriptive vs performance)
	"""
	# Use LLM with RAG context to determine applicability
	building_characteristics = self._extract_characteristics(
	submission,
	spatial_data,
	specifications
	)

	prompt = f"""
	Based on the following building characteristics, identify which
	requirements from Part {self.part_id} apply:

	Building Characteristics:
	{building_characteristics}

	Regulatory Context:
	{self.knowledge_base['approved_doc']}

	List the specific requirements that apply, with justification.
	"""

	response = self.llm.generate(
	prompt=prompt,
	temperature=0.1 # Low temperature for consistency
	)

	requirements = self._parse_requirements(response)

	return requirements

	def _evaluate_requirement(
	self,
	requirement: Requirement,
	submission: Submission,
	spatial_data: SpatialData,
	specifications: Dict
	) -> ComplianceCheck:
	"""
	Evaluate a specific requirement against submission.

	Process:
	1. Extract relevant data using tools
	2. Compare against requirement criteria
	3. Generate evidence trail
	4. Determine compliance status
	5. Calculate confidence
	6. Generate recommendations if non-compliant
	"""
	# This is implemented by each specific agent
	# with domain-specific logic
	raise NotImplementedError

	def _cross_validate_checks(
	self,
	checks: List[ComplianceCheck]
	) -> List[ComplianceCheck]:
	"""
	Cross-validate findings for consistency.

	Ensures:
	- No contradictory findings
	- Related findings are consistent
	- Evidence supports conclusions
	"""
	# Check for contradictions
	contradictions = self._find_contradictions(checks)

	if contradictions:
	# Resolve contradictions (e.g., re-evaluate with more data)
	checks = self._resolve_contradictions(checks, contradictions)

	# Validate evidence chains
	for check in checks:
	check = self._validate_evidence(check)

	return checks

	def _prioritize_checks(
	self,
	checks: List[ComplianceCheck]
	) -> List[ComplianceCheck]:
	"""
	Assign priority levels to findings.

	Priority based on:
	- Safety implications
	- Likelihood of BSR rejection
	- Difficulty of remediation
	- Cost implications
	"""
	for check in checks:
	if check.status != "compliant":
	check.priority = self._calculate_priority(check)

	# Sort by priority
	checks.sort(key=lambda c: PRIORITY_ORDER[c.priority])

	return checks
	```

	### 4.2 Specific Agent Examples

	#### Part A Agent: Structure

	```python
	class PartAAgent(BuildingRegulationPartAgent):
	"""
	Part A: Structure - Checks structural adequacy and safety.

	Key areas:
	- Loading (dead loads, imposed loads, wind loads, snow loads)
	- Structural integrity and stability
	- Disproportionate collapse (for HRBs)
	- Ground movement
	- Foundations
	"""

	def __init__(self, args, *kwargs):
	super().__init__(part_id="A", args, *kwargs)

	# Part A specific tools
	self.load_calculator = LoadCalculator()
	self.structural_analyzer = StructuralAnalyzer()

	def _evaluate_requirement(
	self,
	requirement: Requirement,
	submission: Submission,
	spatial_data: SpatialData,
	specifications: Dict
	) -> ComplianceCheck:
	"""
	Evaluate structural requirement.
	"""
	if requirement.type == "loading":
	return self._check_loading(requirement, spatial_data, specifications)
	elif requirement.type == "disproportionate_collapse":
	return self._check_disproportionate_collapse(requirement, spatial_data, specifications)
	elif requirement.type == "foundations":
	return self._check_foundations(requirement, specifications)
	else:
	return self._generic_structural_check(requirement, spatial_data, specifications)

	def _check_loading(
	self,
	requirement: Requirement,
	spatial_data: SpatialData,
	specifications: Dict
	) -> ComplianceCheck:
	"""
	Check loading assumptions are appropriate.

	Requirements:
	- Dead loads: weight of structure and fixed equipment
	- Imposed loads: occupancy loads per BS EN 1991-1-1
	- Wind loads: per BS EN 1991-1-4
	- Snow loads: per BS EN 1991-1-3
	"""
	# Extract loading specifications
	loading_spec = specifications.get('structural_calculations', {}).get('loading', {})

	# Validate against standards
	issues = []

	# Check imposed loads
	for room in spatial_data.rooms:
	room_use = self._determine_room_use(room)
	required_imposed_load = IMPOSED_LOADS_BS_EN_1991[room_use]
	specified_load = loading_spec.get(room.id, {}).get('imposed_load')

	if specified_load is None:
	issues.append(f"No imposed load specified for {room.name}")
	elif specified_load < required_imposed_load:
	issues.append(
	f"{room.name}: Imposed load {specified_load} kN/m² "
	f"< required {required_imposed_load} kN/m²"
	)

	if issues:
	return ComplianceCheck(
	regulation="Part A - Loading",
	requirement="Loading assumptions must comply with BS EN 1991",
	status="non_compliant" if any("specified" in i for i in issues) else "insufficient_info",
	evidence=issues,
	reasoning="Loading specifications do not meet BS EN 1991 requirements for all areas.",
	confidence=0.90,
	priority="high",
	recommendations=[
	"Review imposed load specifications per BS EN 1991-1-1 Table 6.2",
	"Ensure all room types have appropriate loading specified",
	"Provide structural engineer's justification for any reduced loads"
	],
	regulatory_references=["BS EN 1991-1-1", "Approved Document A Section 2"]
	)
	else:
	return ComplianceCheck(
	regulation="Part A - Loading",
	requirement="Loading assumptions comply with standards",
	status="compliant",
	evidence=["Loading specifications reviewed", "All loads meet or exceed BS EN 1991 requirements"],
	reasoning="Structural loading specifications are adequate per BS EN 1991 series.",
	confidence=0.85,
	priority="medium",
	recommendations=[],
	regulatory_references=["BS EN 1991-1-1"]
	)

	def _check_disproportionate_collapse(
	self,
	requirement: Requirement,
	spatial_data: SpatialData,
	specifications: Dict
	) -> ComplianceCheck:
	"""
	Check disproportionate collapse provisions for HRBs.

	Requirement (Part A3):
	Buildings must be designed so that localized failure doesn't cause
	disproportionate collapse.

	For HRBs (Class 2B): Specific provisions required:
	- Effective horizontal ties
	- Effective vertical ties
	- Key element design
	- Or systematic risk assessment
	"""
	building_class = self._determine_building_class(spatial_data, specifications)

	if building_class != "2B":
	# Not HRB, standard provisions sufficient
	return ComplianceCheck(
	regulation="Part A3 - Disproportionate Collapse",
	requirement="Building class determined, Class 2B provisions not required",
	status="compliant",
	evidence=[f"Building class: {building_class}", "Class 2B requirements not applicable"],
	reasoning="Building does not fall into Class 2B (HRB) category requiring enhanced disproportionate collapse provisions.",
	confidence=0.95,
	priority="low",
	recommendations=[],
	regulatory_references=["Approved Document A Section 5"]
	)

	# Class 2B building - check for provisions
	structural_strategy = specifications.get('structural_calculations', {}).get('disproportionate_collapse', '')

	has_ties = 'ties' in structural_strategy.lower() or 'tying' in structural_strategy.lower()
	has_key_elements = 'key element' in structural_strategy.lower()
	has_risk_assessment = 'risk assessment' in structural_strategy.lower()

	if not (has_ties or has_key_elements or has_risk_assessment):
	return ComplianceCheck(
	regulation="Part A3 - Disproportionate Collapse (Class 2B)",
	requirement="HRBs must have disproportionate collapse provisions",
	status="insufficient_info",
	evidence=[
	"Building is Class 2B (HRB)",
	"No disproportionate collapse strategy evident"
	],
	reasoning="Class 2B buildings require specific provisions per Approved Document A Section 5. No strategy documented.",
	confidence=0.95,
	priority="critical",
	recommendations=[
	"Provide structural engineer's disproportionate collapse strategy",
	"Options:",
	" 1. Effective horizontal and vertical ties (typical approach)",
	" 2. Key element design (elements to withstand 34 kN/m²)",
	" 3. Systematic risk assessment per BS EN 1991-1-7",
	"Submit calculations demonstrating chosen approach"
	],
	regulatory_references=["Approved Document A Section 5", "BS EN 1992-1-1 Section 9"]
	)

	# Strategy present - validate it
	return ComplianceCheck(
	regulation="Part A3 - Disproportionate Collapse (Class 2B)",
	requirement="Disproportionate collapse strategy for HRB",
	status="compliant",
	evidence=[
	"Disproportionate collapse strategy provided",
	"Ties" if has_ties else "",
	"Key elements" if has_key_elements else "",
	"Risk assessment" if has_risk_assessment else ""
	],
	reasoning="Disproportionate collapse provisions documented for Class 2B building.",
	confidence=0.80,
	priority="high",
	recommendations=[
	"Verify structural calculations support stated strategy",
	"Ensure design reviewed by Building Safety Regulator"
	],
	regulatory_references=["Approved Document A Section 5"]
	)
	```

	#### Part B Agent: Fire Safety

	```python
	class PartBAgent(BuildingRegulationPartAgent):
	"""
	Part B: Fire Safety - Most complex and critical agent.

	Sections:
	- B1: Means of escape
	- B2: Internal fire spread (linings)
	- B3: Internal fire spread (structure)
	- B4: External fire spread
	- B5: Access and facilities for fire service

	This is often the primary cause of Gateway rejections.
	"""

	def __init__(self, args, *kwargs):
	super().__init__(part_id="B", args, *kwargs)

	# Part B specific tools
	self.egress_simulator = EgressSimulator()
	self.compartmentation_checker = CompartmentationChecker()
	self.fire_resistance_validator = FireResistanceValidator()

	def _evaluate_requirement(
	self,
	requirement: Requirement,
	submission: Submission,
	spatial_data: SpatialData,
	specifications: Dict
	) -> ComplianceCheck:
	"""
	Evaluate fire safety requirement.
	"""
	if requirement.section == "B1":
	return self._check_means_of_escape(requirement, spatial_data, specifications)
	elif requirement.section == "B2":
	return self._check_internal_linings(requirement, specifications)
	elif requirement.section == "B3":
	return self._check_fire_resistance(requirement, spatial_data, specifications)
	elif requirement.section == "B4":
	return self._check_external_fire_spread(requirement, spatial_data, specifications)
	elif requirement.section == "B5":
	return self._check_fire_service_access(requirement, spatial_data, specifications)

	def _check_means_of_escape(
	self,
	requirement: Requirement,
	spatial_data: SpatialData,
	specifications: Dict
	) -> ComplianceCheck:
	"""
	Check means of escape provisions (B1).

	Key requirements:
	- Travel distances to exits
	- Number of escape routes
	- Width of escape routes
	- Protected stairways
	- Emergency lighting
	- Signage

	For HRBs: Enhanced requirements including 2.5 minute evacuation time.
	"""
	is_hrb = specifications.get('building_type') == 'HRB'

	# Calculate travel distances
	travel_distance_issues = []

	for room in spatial_data.rooms:
	if room.is_habitable:
	routes = self.spatial.find_egress_routes(
	start_room=room.id,
	building_id=spatial_data.building_id
	)

	shortest_route = min(routes, key=lambda r: r.distance)

	# Determine maximum permitted travel distance
	if is_hrb:
	max_distance = 9.0 if len(routes) < 2 else 18.0 # meters
	else:
	max_distance = self._get_max_travel_distance(
	room.use,
	len(routes)
	)

	if shortest_route.distance > max_distance:
	travel_distance_issues.append({
	'room': room.name,
	'distance': shortest_route.distance,
	'max_allowed': max_distance,
	'excess': shortest_route.distance - max_distance
	})

	if travel_distance_issues:
	return ComplianceCheck(
	regulation="Part B1 - Means of Escape (Travel Distance)",
	requirement=f"Travel distance to nearest exit: {'HRB: 9m (one direction) or 18m (two directions)' if is_hrb else 'Per Approved Document B Table 3.1'}",
	status="non_compliant",
	evidence=[
	f"{len(travel_distance_issues)} room(s) exceed travel distance limits",
	f"Worst case: {max(travel_distance_issues, key=lambda x: x['excess'])['room']} "
	f"({max(travel_distance_issues, key=lambda x: x['excess'])['distance']:.1f}m)"
	],
	reasoning="Travel distances exceed limits per Approved Document B. This is a critical life safety issue.",
	confidence=0.95,
	priority="critical",
	recommendations=[
	"Reduce travel distances by:",
	" - Adding additional exits/stairways",
	" - Reconfiguring floor layout",
	" - Repositioning protected escape routes",
	f"Rooms requiring attention: {', '.join(i['room'] for i in travel_distance_issues[:5])}"
	],
	regulatory_references=["Approved Document B Volume 2 Table 3.1", "BS 9999"]
	)

	# Check alternative means of escape
	alternative_escape_issues = []

	for room in spatial_data.rooms:
	if room.requires_alternative_escape: # Determined by room characteristics
	routes = self.spatial.find_egress_routes(room.id, spatial_data.building_id)

	if len(routes) < 2:
	alternative_escape_issues.append({
	'room': room.name,
	'routes_found': len(routes),
	'routes_required': 2
	})

	if alternative_escape_issues:
	return ComplianceCheck(
	regulation="Part B1 - Alternative Means of Escape",
	requirement="Rooms requiring alternative escape must have two independent routes",
	status="non_compliant",
	evidence=[
	f"{len(alternative_escape_issues)} room(s) lack alternative escape routes",
	"Rooms affected: " + ", ".join(i['room'] for i in alternative_escape_issues[:10])
	],
	reasoning="Insufficient alternative means of escape. Some rooms only have single escape route when two required.",
	confidence=0.90,
	priority="critical",
	recommendations=[
	"Provide second independent escape route for affected rooms",
	"Routes must be truly independent (not converge before final exit)",
	"Consider additional external escape stair if necessary"
	],
	regulatory_references=["Approved Document B Section 2"]
	)

	# If we reach here, basic egress provisions appear satisfactory
	return ComplianceCheck(
	regulation="Part B1 - Means of Escape",
	requirement="Adequate means of escape provisions",
	status="compliant",
	evidence=[
	f"Travel distances checked for {len(spatial_data.rooms)} rooms",
	"All travel distances within limits",
	"Alternative escape routes provided where required"
	],
	reasoning="Means of escape provisions meet Approved Document B requirements.",
	confidence=0.85,
	priority="high",
	recommendations=[
	"Verify emergency lighting and signage specifications",
	"Ensure protected routes maintained during construction"
	],
	regulatory_references=["Approved Document B Section 2"]
	)
	```

	### 4.3 RAG Strategy Per Agent

	Each agent has tailored RAG retrieval strategy:

	#### Part A (Structure) RAG Strategy

	```python
	class PartARAGStrategy:
	"""
	RAG strategy optimized for structural requirements.
	"""

	def generate_queries(self, building_characteristics: Dict) -> List[str]:
	"""
	Generate queries specific to structural checking.
	"""
	queries = [
	# Loading queries
	f"imposed loads for {building_characteristics['use']} buildings BS EN 1991",
	f"wind loads {building_characteristics['location']} UK",
	f"snow loads {building_characteristics['location']} UK",

	# Structural integrity
	f"structural stability requirements {building_characteristics['height']}m building",
	f"disproportionate collapse class 2B {building_characteristics['storeys']} storey",

	# Foundations
	f"foundation design {building_characteristics['ground_conditions']}",
	f"foundation depth frost heave UK",

	# Fire resistance (structural)
	f"fire resistance requirements {building_characteristics['use']} {building_characteristics['height']}m",
	f"structural fire design {building_characteristics['structural_material']}"
	]

	return queries

	def retrieve_context(
	self,
	query: str,
	rag_system: RAGSystem
	) -> Dict:
	"""
	Retrieve context with emphasis on standards and calculations.
	"""
	# Text retrieval with focus on BS EN standards
	text_results = rag_system.retrieve_text(
	query=query,
	collection="part_a_text",
	top_k=15,
	filters={'document_type': ['approved_document', 'bs_en_standard']}
	)

	# Retrieve calculation examples
	precedent_results = rag_system.retrieve_text(
	query=f"{query} calculation example",
	collection="structural_precedents",
	top_k=5
	)

	# Retrieve diagrams
	diagram_results = rag_system.retrieve_diagrams(
	query=query,
	collection="part_a_diagrams",
	top_k=3
	)

	return {
	'regulatory_text': text_results,
	'precedents': precedent_results,
	'diagrams': diagram_results
	}
	```

	#### Part B (Fire Safety) RAG Strategy

	```python
	class PartBRAGStrategy:
	"""
	RAG strategy for fire safety - most complex due to broad scope.
	"""

	def generate_queries(self, building_characteristics: Dict) -> List[str]:
	"""
	Generate comprehensive fire safety queries.
	"""
	queries = []

	# Means of escape queries
	queries.extend([
	f"travel distance requirements {building_characteristics['use']}",
	f"alternative means of escape {building_characteristics['occupancy']} occupants",
	f"protected stairway requirements {building_characteristics['storeys']} storeys",
	f"evacuation time requirements HRB" if building_characteristics['is_hrb'] else "",
	])

	# Compartmentation queries
	queries.extend([
	f"compartmentation requirements {building_characteristics['use']}",
	f"fire resistance periods {building_characteristics['height']}m building",
	f"cavity barriers requirements",
	])

	# External fire spread (post-Grenfell critical)
	if building_characteristics['is_hrb']:
	queries.extend([
	f"external wall systems HRB requirements post-Grenfell",
	f"cladding fire performance requirements",
	f"combustible materials restrictions HRB"
	])

	# Fire service access
	queries.extend([
	f"fire fighting shaft requirements {building_characteristics['height']}m",
	f"fire service vehicle access requirements",
	f"dry rising mains requirements"
	])

	return [q for q in queries if q] # Remove empty strings

	def retrieve_context(
	self,
	query: str,
	rag_system: RAGSystem,
	is_hrb: bool
	) -> Dict:
	"""
	Retrieve fire safety context with HRB emphasis if applicable.
	"""
	# Base retrieval
	text_results = rag_system.retrieve_text(
	query=query,
	collection="part_b_text",
	top_k=20 # More results due to complexity
	)

	# HRB-specific guidance if applicable
	if is_hrb:
	hrb_results = rag_system.retrieve_text(
	query=f"{query} HRB higher risk building",
	collection="hrb_guidance",
	top_k=10
	)
	else:
	hrb_results = []

	# Rejection precedents (learn from common failures)
	rejection_results = rag_system.retrieve_text(
	query=f"{query} BSR rejection reasons",
	collection="bsr_rejections",
	top_k=5
	)

	# Diagrams (especially for egress layouts)
	diagram_results = rag_system.retrieve_diagrams(
	query=query,
	collection="part_b_diagrams",
	top_k=5
	)

	return {
	'regulatory_text': text_results,
	'hrb_guidance': hrb_results,
	'rejection_precedents': rejection_results,
	'diagrams': diagram_results
	}
	```

	---

	## 5. REQUIRED OUTCOMES

	### 5.1 Primary Deliverables

	#### Gateway 2 Compliance Package

	Contents:
	1. Executive Summary
	- Overall compliance status
	- Critical issues requiring immediate attention
	- Summary statistics (compliant vs non-compliant checks)
	- Estimated likelihood of BSR approval

	2. Part-by-Part Compliance Commentary
	- Detailed findings for each applicable Building Regulation Part (A-T)
	- Evidence-based determinations with regulatory citations
	- Confidence scores for each finding
	- NOT design advice - commentary only per RBI/Chartered Building Engineer code

	3. Cross-Part Analysis
	- Identification of conflicts between different regulation parts
	- Recommendations for resolving conflicts
	- Dependencies and interactions highlighted

	4. Gap Analysis
	- Missing information that prevents full assessment
	- Additional documentation required
	- Clarifications needed from design team

	5. Prioritized Issue List
	- Critical issues (life safety, structural integrity, likely BSR rejection)
	- High priority issues (compliance required, moderate remediation difficulty)
	- Medium priority issues (compliance preferred, easier remediation)
	- Low priority observations (enhancement suggestions, best practice)

	6. Evidence Package
	- Document references supporting each finding
	- Drawing annotations highlighting issues
	- Calculation summaries where applicable
	- Product verification results

	7. Recommendations
	- Specific, actionable steps to address non-compliances
	- Alternative approaches where applicable
	- Suggested consultations with specialists
	- Timeline implications of changes

	Format:
	- PDF report for human review
	- Structured JSON/XML for system integration
	- Interactive dashboard for project team access
	- Annotated drawings with issue highlighting

	Example Report Structure:
	```
	BUILDWELLTHREAD COMPLIANCE REPORT
	Gateway 2 Submission Assessment

	Project: [Project Name]
	Address: [Address]
	Submission Date: [Date]
	Report Date: [Date]
	Reference: [Unique ID]

	EXECUTIVE SUMMARY
	━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

	Overall Assessment: NON-COMPLIANT [requires revisions]

	Compliance Summary:
	✓ Compliant: 156 checks (64%)
	✗ Non-Compliant: 45 checks (18%)
	? Requires Clarification: 44 checks (18%)

	Total Checks Performed: 245

	CRITICAL ISSUES (IMMEDIATE ATTENTION REQUIRED):
	1. Part B1 - Travel Distance: 8 rooms exceed maximum (see Section 2.1)
	2. Part B3 - Fire Resistance: Compartmentation incomplete (see Section 2.3)
	3. Part A3 - Disproportionate Collapse: Strategy not provided for HRB (see Section 1.3)

	LIKELIHOOD OF BSR APPROVAL: LOW (without revisions)
	Estimated remediation time: 4-6 weeks

	...

	PART A: STRUCTURE
	━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

	A1: LOADING

	✓ COMPLIANT: Imposed load specifications
	Status: Compliant
	Confidence: 85%
	Evidence:
	- Imposed loads specified per BS EN 1991-1-1 Table 6.2
	- All room types have appropriate loading
	- Structural calculations signed by Chartered Engineer

	✗ NON-COMPLIANT: Wind load calculations
	Status: Non-Compliant
	Priority: HIGH
	Confidence: 90%
	Evidence:
	- Wind load calculations use Eurocode but not UK National Annex
	- Basic wind speed not adjusted for UK location
	- No consideration of terrain category

	Reasoning:
	Wind load calculations must use UK National Annex to BS EN 1991-1-4.
	Current calculations use generic Eurocode values which are not appropriate
	for UK sites. This affects sizing of structural elements and connections.

	Recommendations:
	1. Recalculate wind loads using BS EN 1991-1-4 with UK National Annex
	2. Use UK wind map for site-specific basic wind speed
	3. Include terrain category and orography factors for site
	4. Verify structural adequacy with revised loads

	Regulatory References:
	- BS EN 1991-1-4:2005+A1:2010
	- UK National Annex to BS EN 1991-1-4
	- Approved Document A Section 2.1

	...
	```

	#### Gateway 3 Support Package

	Contents:
	1. Completion Certificate Readiness Assessment
	- Verification all Gateway 2 conditions satisfied
	- Outstanding items list
	- Completion certificate application status

	2. As-Built Validation
	- Comparison of as-built to approved drawings
	- Material substitutions verification
	- Performance testing results review

	3. Change Log
	- All variations from approved Gateway 2 submission
	- Justification for changes
	- Re-validation of compliance where affected
	- Authorization trail

	4. Final Compliance Verification
	- Confirmation all Building Regulations requirements met
	- Testing and commissioning results verification
	- Certification package completeness check

	5. Operation and Maintenance Information
	- Building manual adequacy assessment
	- Safety information compilation
	- Handover documentation review

	Outcome: Supporting documentation for Gateway 3 submission with confidence that completion certificate will be granted.

	#### Golden Thread Documentation

	Structure (per Building Safety Act requirements):

	```
	Golden Thread Information Set
	├── Building Information
	│ ├── Location and address
	│ ├── Building characteristics (height, storeys, occupancy)
	│ ├── Use and occupancy information
	│ ├── Principal contractor details
	│ └── Principal designer details
	│
	├── Design Information
	│ ├── Architectural plans (by floor and section)
	│ ├── Structural design
	│ ├── Fire safety strategy
	│ ├── Building services design
	│ ├── Materials specifications
	│ └── Product certifications
	│
	├── Safety Case
	│ ├── Fire safety strategy
	│ ├── Structural fire protection
	│ ├── Means of escape provisions
	│ ├── Fire service access and facilities
	│ ├── Risk assessments
	│ └── Safety management plan
	│
	├── Compliance Record
	│ ├── Gateway 2 approval documentation
	│ ├── Gateway 3 completion certificate
	│ ├── Building Regulations compliance certificates
	│ ├── Fire safety compliance
	│ ├── Structural approval
	│ └── Other regulatory approvals
	│
	├── Change Control
	│ ├── Design change register
	│ ├── Change authorization records
	│ ├── Impact assessments
	│ ├── Re-validation of compliance
	│ └── As-built vs approved comparison
	│
	├── Construction Phase Information
	│ ├── Progress updates
	│ ├── Inspection reports
	│ ├── Testing and commissioning
	│ ├── Site safety records
	│ └── Handover information
	│
	└── Operation and Maintenance
	├── Building user manual
	├── Safety information for residents
	├── Maintenance schedules
	├── Emergency procedures
	└── Contact information
	```

	Characteristics:
	- Structured: Consistent format following Building Safety Act guidance
	- Versioned: Complete change history with dates and authorization
	- Accessible: Different views for different stakeholders (residents, facilities management, BSR)
	- Persistent: Maintained throughout building lifecycle
	- Interoperable: Compatible with BSR digital platform

	### 5.2 Performance Targets

	#### Accuracy Targets

	Overall Goal: >90% agreement with expert human reviewers

	By Priority Level:
	- Critical safety issues: >95% detection rate (minimize false negatives)
	- High priority compliance: >90% accuracy
	- Medium priority: >85% accuracy
	- Low priority observations: >80% accuracy

	False Positive Tolerance: <15% (balance between catching issues and not overwhelming with false flags)

	#### Processing Targets

	- Time: 45-90 minutes per submission (end-to-end)
	- Throughput: Process multiple submissions in parallel
	- Availability: 99% uptime (critical for workflow integration)
	- Scalability: Support 10-50 concurrent submissions

	#### Quality Targets

	- Completeness: 100% of applicable Building Regulations Parts checked
	- Evidence Quality: Every finding supported by specific document references
	- Clarity: Recommendations actionable without additional interpretation
	- Consistency: Same submission produces same results across multiple runs

	#### Business Targets

	- BSR Rejection Rate Reduction: Target <20% (from current 40-67%)
	- Time Savings: Reduce manual review time by 70-80%
	- Cost Reduction: Lower compliance checking costs by 50%+
	- Quality Improvement: Catch issues earlier in design process

	### 5.3 Success Metrics

	#### Technical Success Indicators

	1. Validation Against Human Experts
	- Blind comparison: AI findings vs expert review
	- Agreement rate measured
	- Types of discrepancies analyzed
	- Continuous improvement based on discrepancies

	2. BSR Gateway Performance
	- Track rejection rates of AI-reviewed submissions
	- Analyze rejection reasons (were they preventable?)
	- Compare to industry baseline
	- Measure improvement over time

	3. User Feedback
	- Usefulness ratings from design teams
	- Accuracy perception from reviewers
	- Time savings reported
	- Adoption rate within organization

	#### Operational Success Indicators

	1. System Reliability
	- Uptime percentage
	- Mean time between failures
	- Average processing time
	- Error rate by component

	2. Golden Thread Utility
	- Completeness of generated documentation
	- Acceptance by BSR
	- Ease of maintenance and updates
	- Stakeholder satisfaction

	3. Business Impact
	- Cost per submission
	- Time to completion
	- Number of design iterations reduced
	- Overall project timeline improvement

	---

	## 6. ERROR HANDLING AND QUALITY ASSURANCE

	### 6.1 Error Categories and Responses

	#### Input Processing Errors

	Corrupt or Unreadable PDFs:
	```python
	class CorruptPDFHandler:
	"""
	Handle corrupted or problematic PDF inputs.
	"""

	def handle(self, pdf_path: str) -> Result:
	"""
	Attempt recovery or graceful degradation.
	"""
	try:
	# Attempt standard parsing
	document = parse_pdf(pdf_path)
	return Success(document)

	except CorruptPDFError as e:
	# Attempt recovery
	try:
	repaired = repair_pdf(pdf_path)
	document = parse_pdf(repaired)

	return Success(
	document,
	warnings=["PDF was repaired - verify content integrity"]
	)

	except Exception:
	# Cannot recover
	return Failure(
	error="PDF is corrupt and cannot be recovered",
	action="manual_review_required",
	user_message="This PDF cannot be processed. Please re-export and resubmit.",
	file=pdf_path
	)
	```

	Poor Quality Drawings:
	```python
	class DrawingQualityAssessor:
	"""
	Assess and handle low-quality drawings.
	"""

	def assess_quality(self, image: Image) -> QualityScore:
	"""
	Score drawing quality on multiple dimensions.
	"""
	scores = {
	'resolution': self._check_resolution(image),
	'contrast': self._check_contrast(image),
	'clarity': self._check_clarity(image),
	'completeness': self._check_completeness(image)
	}

	overall = np.mean(list(scores.values()))

	return QualityScore(
	overall=overall,
	dimensions=scores
	)

	def handle(self, image: Image, quality: QualityScore) -> Result:
	"""
	Decide how to proceed based on quality.
	"""
	if quality.overall > 0.8:
	# High quality - proceed normally
	return Success(image)

	elif quality.overall > 0.5:
	# Acceptable but suboptimal
	# Proceed with warnings and reduced confidence
	return Success(
	image,
	warnings=[
	f"Drawing quality suboptimal (score: {quality.overall:.2f})",
	"Some automated measurements may be less reliable",
	"Manual verification recommended for critical dimensions"
	],
	confidence_penalty=0.15
	)

	else:
	# Unacceptable quality
	# Request better quality submission
	return Failure(
	error="Drawing quality insufficient for automated analysis",
	quality_score=quality.overall,
	issues=[dim for dim, score in quality.dimensions.items() if score < 0.5],
	user_message="Please provide higher quality drawings (minimum 300 DPI, clear lines)",
	action="resubmission_required"
	)
	```

	Missing or Incomplete Submissions:
	```python
	class CompletenessChecker:
	"""
	Validate submission completeness before processing.
	"""

	REQUIRED_DOCUMENTS = {
	'gateway_2': [
	'site_plan',
	'floor_plans',
	'elevations',
	'sections',
	'specifications',
	'structural_calculations',
	'fire_strategy'
	]
	}

	def check(self, submission: Submission) -> CompletenessResult:
	"""
	Check if submission is complete.
	"""
	required = self.REQUIRED_DOCUMENTS['gateway_2']
	provided = submission.document_types

	missing = [doc for doc in required if doc not in provided]

	if missing:
	return CompletenessResult(
	complete=False,
	missing_documents=missing,
	can_proceed=len(missing) < 3, # Can partially process if only minor gaps
	user_message=self._format_missing_docs_message(missing)
	)

	return CompletenessResult(
	complete=True,
	missing_documents=[],
	can_proceed=True
	)

	def _format_missing_docs_message(self, missing: List[str]) -> str:
	"""
	Create user-friendly message about missing documents.
	"""
	return f"""
	The following required documents are missing from your submission:
	{self._format_list(missing)}

	Please provide these documents to enable complete compliance checking.

	Note: We can perform partial analysis with the documents provided,
	but some compliance checks cannot be completed without the missing items.
	"""
	```

	#### Spatial Processing Errors

	FloorplanTransformation Failures:
	```python
	class FloorplanTransformationErrorHandler:
	"""
	Handle failures in spatial processing pipeline.
	"""

	def handle(
	self,
	pdf_path: str,
	error: Exception
	) -> Result:
	"""
	Attempt fallback strategies when FloorplanTransformation fails.
	"""
	# Log the error
	logger.error(f"FloorplanTransformation failed for {pdf_path}: {error}")

	# Attempt fallback strategy 1: Alternative CV model
	try:
	spatial_data = self.alternative_cv_model.process(pdf_path)

	return Success(
	spatial_data,
	warnings=[
	"Used alternative processing method",
	"Spatial measurements may be less accurate",
	"Manual verification recommended"
	],
	confidence_penalty=0.20
	)

	except Exception as e2:
	logger.error(f"Alternative CV model also failed: {e2}")

	# Attempt fallback strategy 2: Manual annotation interface
	try:
	# Create manual annotation task
	task_id = self.manual_annotation_queue.create_task(
	pdf_path=pdf_path,
	priority="high",
	reason="Automated processing failed"
	)

	return Pending(
	message="Automated processing failed - manual annotation required",
	task_id=task_id,
	estimated_time="30-60 minutes",
	user_message="We're having trouble automatically processing these plans. "
	"Our team will manually review and we'll notify you when complete."
	)

	except Exception as e3:
	logger.error(f"Could not create manual task: {e3}")

	# Complete failure - cannot proceed
	return Failure(
	error="Unable to process floorplans",
	action="expert_review_required",
	user_message="These plans could not be processed automatically. "
	"Please contact support for manual review options."
	)
	```

	Ambiguous Element Detection:
	```python
	class AmbiguityResolver:
	"""
	Handle uncertain spatial element identification.
	"""

	def resolve(
	self,
	element: SpatialElement,
	confidence: float
	) -> Resolution:
	"""
	Decide how to handle ambiguous elements.
	"""
	if confidence > 0.9:
	# High confidence - accept
	return Accept(element)

	elif confidence > 0.6:
	# Medium confidence - flag and proceed with caveats
	return AcceptWithCaveats(
	element=element,
	confidence=confidence,
	warnings=[
	f"Element '{element.id}' identification uncertain (confidence: {confidence:.0%})",
	f"Type: {element.type} (alternative: {element.alternative_types})",
	"Recommend manual verification"
	]
	)

	else:
	# Low confidence - request clarification
	return RequestClarification(
	element=element,
	confidence=confidence,
	question=f"Please clarify: Is element at {element.location} a {element.type}?",
	alternatives=element.alternative_types,
	impact="Affects: " + ", ".join(element.dependent_checks)
	)
	```

	#### RAG System Errors

	No Relevant Regulations Retrieved:
	```python
	class RetrievalFailureHandler:
	"""
	Handle cases where RAG system returns no relevant results.
	"""

	def handle(
	self,
	query: str,
	collection: str
	) -> Result:
	"""
	Respond to failed retrieval.
	"""
	# Log the issue
	logger.warning(f"No results for query: {query} in collection: {collection}")

	# Attempt query expansion
	expanded_queries = self._expand_query(query)

	for expanded in expanded_queries:
	results = self.rag.retrieve(expanded, collection)

	if results:
	return Success(
	results,
	warnings=["Used expanded query for retrieval"]
	)

	# Still no results - this might be a novel situation
	return Failure(
	error="No relevant regulatory guidance found",
	query=query,
	action="flag_for_expert",
	user_message="This appears to be a novel or unusual situation. "
	"Expert review recommended.",
	escalation_priority="medium"
	)

	def _expand_query(self, query: str) -> List[str]:
	"""
	Generate alternative queries.
	"""
	# Use LLM to generate alternative phrasings
	prompt = f"""
	The following query returned no results:
	"{query}"

	Generate 3 alternative queries that might retrieve relevant information
	from UK Building Regulations documents.
	"""

	alternatives = self.llm.generate(prompt)

	return parse_alternatives(alternatives)
	```

	Contradictory Guidance:
	```python
	class ContradictionResolver:
	"""
	Handle cases where retrieved regulations appear contradictory.
	"""

	def resolve(
	self,
	regulations: List[Regulation]
	) -> Resolution:
	"""
	Identify and resolve contradictions.
	"""
	# Check for obvious contradictions
	contradictions = self._find_contradictions(regulations)

	if not contradictions:
	return NoContradiction(regulations)

	# Attempt resolution by checking:
	# 1. Document hierarchy (later supersedes earlier)
	# 2. Specificity (specific overrides general)
	# 3. Context applicability

	resolved = []
	for contradiction in contradictions:
	resolution = self._resolve_single_contradiction(contradiction)
	resolved.append(resolution)

	if all(r.confident for r in resolved):
	return ResolvedContradiction(
	regulations=resolved,
	explanations=[r.explanation for r in resolved]
	)

	# Cannot confidently resolve - escalate
	return UnresolvableContradiction(
	regulations=regulations,
	contradictions=contradictions,
	action="escalate_to_expert",
	user_message="We've identified potentially conflicting requirements. "
	"Expert interpretation needed."
	)
	```

	#### Agent-Level Errors

	Confidence Below Threshold:
	```python
	class ConfidenceManager:
	"""
	Manage low-confidence agent determinations.
	"""

	CONFIDENCE_THRESHOLDS = {
	'critical_safety': 0.90,
	'high_priority': 0.80,
	'medium_priority': 0.70,
	'low_priority': 0.60
	}

	def handle(
	self,
	check: ComplianceCheck,
	threshold_category: str
	) -> HandlingDecision:
	"""
	Decide how to handle low-confidence findings.
	"""
	required_confidence = self.CONFIDENCE_THRESHOLDS[threshold_category]

	if check.confidence >= required_confidence:
	# Acceptable confidence
	return Accept(check)

	elif check.confidence >= required_confidence - 0.10:
	# Slightly below threshold - accept with strong caveats
	return AcceptWithReview(
	check=check,
	warnings=[
	f"Confidence ({check.confidence:.0%}) below preferred threshold",
	"Recommend expert verification"
	],
	review_priority="high" if threshold_category == "critical_safety" else "medium"
	)

	else:
	# Significantly below threshold - cannot make determination
	return InsufficientConfidence(
	check=check,
	status="requires_expert_review",
	reason=f"Confidence {check.confidence:.0%} insufficient for {threshold_category} finding",
	required_action="expert_review_before_proceeding"
	)
	```

	Inter-Agent Conflicts:
	```python
	class ConflictResolver:
	"""
	Resolve conflicts between different agent findings.
	"""

	def detect_conflicts(
	self,
	agent_results: Dict[str, List[ComplianceCheck]]
	) -> List[Conflict]:
	"""
	Identify conflicts between agent findings.
	"""
	conflicts = []

	# Check for direct contradictions
	# (e.g., Part L wants more insulation, Part O wants less to avoid overheating)

	for part_a, checks_a in agent_results.items():
	for part_b, checks_b in agent_results.items():
	if part_a >= part_b:
	continue # Avoid duplicate comparisons

	part_conflicts = self._find_conflicts_between_parts(
	part_a, checks_a,
	part_b, checks_b
	)

	conflicts.extend(part_conflicts)

	return conflicts

	def resolve(self, conflict: Conflict) -> Resolution:
	"""
	Attempt to resolve identified conflict.
	"""
	# Check if there's established precedence
	precedence = self._check_precedence(conflict.part_a, conflict.part_b)

	if precedence:
	return PrecedenceResolution(
	winner=precedence.primary_part,
	rationale=precedence.explanation,
	recommendation=f"Prioritize {precedence.primary_part} requirement. "
	f"Seek alternative solutions for {precedence.secondary_part}."
	)

	# Check for alternative solutions that satisfy both
	alternatives = self._find_alternative_solutions(conflict)

	if alternatives:
	return AlternativeSolution(
	alternatives=alternatives,
	recommendation="Consider alternative approaches that satisfy both requirements"
	)

	# Cannot resolve - escalate
	return UnresolvedConflict(
	conflict=conflict,
	action="expert_judgment_required",
	recommendation="This conflict requires design team discussion and expert judgment"
	)
	```

	#### Database Errors

	Vector Database Unavailable:
	```python
	class VectorDBFailureHandler:
	"""
	Handle Milvus vector database failures.
	"""

	def handle(self, error: Exception) -> Result:
	"""
	Respond to vector DB unavailability.
	"""
	logger.critical(f"Vector database unavailable: {error}")

	# Attempt reconnection
	if self._reconnect_attempt():
	return Recovered()

	# Use cached results if available
	if self.cache.has_recent_data():
	return DegradedMode(
	message="Using cached regulatory data",
	limitations=[
	"Cannot retrieve newest regulations",
	"Precedent search unavailable",
	"Some contextual information may be missing"
	],
	max_degraded_time="30 minutes"
	)

	# Cannot proceed
	return Failure(
	error="Regulatory knowledge base unavailable",
	action="system_maintenance_required",
	user_message="System temporarily unavailable. Please try again shortly.",
	estimated_recovery="15-30 minutes"
	)
	```

	Graph Database Inconsistencies:
	```python
	class GraphDBValidation:
	"""
	Detect and handle Neo4j graph inconsistencies.
	"""

	def validate_spatial_graph(
	self,
	spatial_data: SpatialData
	) -> ValidationResult:
	"""
	Check for logical inconsistencies in spatial graph.
	"""
	issues = []

	# Check for orphaned nodes
	orphans = self._find_orphaned_nodes(spatial_data)
	if orphans:
	issues.append(
	Issue(
	type="orphaned_elements",
	elements=orphans,
	severity="medium",
	message=f"{len(orphans)} elements not connected to building topology"
	)
	)

	# Check for impossible relationships
	impossible = self._find_impossible_relationships(spatial_data)
	if impossible:
	issues.append(
	Issue(
	type="impossible_topology",
	relationships=impossible,
	severity="high",
	message=f"{len(impossible)} impossible spatial relationships detected"
	)
	)

	# Check for duplicate elements
	duplicates = self._find_duplicates(spatial_data)
	if duplicates:
	issues.append(
	Issue(
	type="duplicate_elements",
	elements=duplicates,
	severity="low",
	message=f"{len(duplicates)} duplicate elements found"
	)
	)

	if issues:
	return ValidationFailure(
	issues=issues,
	action="repair_or_notify",
	can_proceed=all(i.severity != "high" for i in issues)
	)

	return ValidationSuccess()

	def repair(self, issues: List[Issue]) -> RepairResult:
	"""
	Attempt automatic repair of graph inconsistencies.
	"""
	repaired = []
	failed = []

	for issue in issues:
	try:
	if issue.type == "orphaned_elements":
	self._repair_orphans(issue.elements)
	repaired.append(issue)

	elif issue.type == "duplicate_elements":
	self._remove_duplicates(issue.elements)
	repaired.append(issue)

	else:
	# Cannot auto-repair
	failed.append(issue)

	except Exception as e:
	logger.error(f"Repair failed for {issue.type}: {e}")
	failed.append(issue)

	return RepairResult(
	repaired=repaired,
	failed=failed,
	success=len(failed) == 0
	)
	```

	### 6.2 Quality Assurance Mechanisms

	#### Multi-Level Validation

	```python
	class QualityAssurance:
	"""
	Multi-level quality assurance for compliance determinations.
	"""

	def validate_finding(
	self,
	check: ComplianceCheck
	) -> QAResult:
	"""
	Apply multiple validation checks to finding.
	"""
	validations = []

	# 1. Evidence consistency check
	evidence_valid = self._validate_evidence(check)
	validations.append(('evidence', evidence_valid))

	# 2. Reasoning coherence check
	reasoning_valid = self._validate_reasoning(check)
	validations.append(('reasoning', reasoning_valid))

	# 3. Regulatory citation accuracy
	citations_valid = self._validate_citations(check)
	validations.append(('citations', citations_valid))

	# 4. Confidence calibration
	confidence_calibrated = self._calibrate_confidence(check)
	validations.append(('confidence', confidence_calibrated))

	# 5. Cross-check with similar cases
	precedent_consistent = self._check_precedent_consistency(check)
	validations.append(('precedent', precedent_consistent))

	# Aggregate results
	all_valid = all(valid for _, valid in validations)

	if all_valid:
	return QAPass(check)

	else:
	failed = [name for name, valid in validations if not valid]
	return QAFail(
	check=check,
	failed_validations=failed,
	action="review_and_revise"
	)

	def _validate_evidence(self, check: ComplianceCheck) -> bool:
	"""
	Check that evidence actually supports conclusion.
	"""
	# Use LLM to assess if evidence supports conclusion
	prompt = f"""
	Does the following evidence support the conclusion?

	Conclusion: {check.status} - {check.requirement}

	Evidence:
	{chr(10).join(check.evidence)}

	Answer: yes/no with brief explanation
	"""

	response = self.llm.generate(prompt, temperature=0.1)

	return 'yes' in response.lower()

	def _calibrate_confidence(self, check: ComplianceCheck) -> bool:
	"""
	Ensure confidence score is appropriate.
	"""
	# Factors that should reduce confidence:
	reducing_factors = []

	if len(check.evidence) < 2:
	reducing_factors.append("insufficient_evidence")

	if check.status == "insufficient_info":
	# Confidence should be high for "insufficient info" findings
	# (high confidence that we don't have enough information)
	if check.confidence < 0.80:
	reducing_factors.append("confidence_too_low_for_insufficient_info")

	if check.regulatory_references == []:
	reducing_factors.append("no_regulatory_references")

	if reducing_factors and check.confidence > 0.70:
	# Confidence should be reduced
	check.confidence = max(0.60, check.confidence - 0.15)
	check.metadata['confidence_adjusted'] = True
	check.metadata['adjustment_reason'] = reducing_factors

	return True # Calibration applied
	```

	#### Audit Trail Generation

	```python
	class AuditTrail:
	"""
	Comprehensive audit trail for all system decisions.
	"""

	def log_decision(
	self,
	decision_type: str,
	input_data: Dict,
	output: Any,
	agent: str,
	reasoning: str,
	confidence: float,
	timestamp: datetime
	):
	"""
	Log a compliance decision with full context.
	"""
	entry = AuditEntry(
	id=generate_uuid(),
	timestamp=timestamp,
	decision_type=decision_type,
	agent=agent,
	input_hash=hash_data(input_data),
	input_summary=summarize(input_data),
	output=output,
	reasoning=reasoning,
	confidence=confidence,
	model_version=self.model_version,
	rag_version=self.rag_version,
	system_version=self.system_version
	)

	# Store in database
	self.db.store(entry)

	# Create human-readable log
	self.log_file.write(self._format_entry(entry))

	def _format_entry(self, entry: AuditEntry) -> str:
	"""
	Format audit entry for human review.
	"""
	return f"""
	━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
	AUDIT ENTRY: {entry.id}
	━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
	Timestamp: {entry.timestamp}
	Agent: {entry.agent}
	Decision Type: {entry.decision_type}

	Input Summary:
	{entry.input_summary}

	Output:
	{entry.output}

	Reasoning:
	{entry.reasoning}

	Confidence: {entry.confidence:.0%}

	System Versions:
	- Model: {entry.model_version}
	- RAG: {entry.rag_version}
	- System: {entry.system_version}
	━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
	"""

	def generate_audit_report(
	self,
	submission_id: str
	) -> AuditReport:
	"""
	Generate comprehensive audit report for submission.
	"""
	entries = self.db.get_entries(submission_id=submission_id)

	report = AuditReport(
	submission_id=submission_id,
	total_decisions=len(entries),
	agents_involved=list(set(e.agent for e in entries)),
	decision_timeline=self._create_timeline(entries),
	confidence_distribution=self._analyze_confidence(entries),
	model_versions_used=list(set(e.model_version for e in entries)),
	full_trail=entries
	)

	return report
	```

	#### Continuous Learning and Improvement

	```python
	class FeedbackLoop:
	"""
	Collect feedback and improve system over time.
	"""

	def record_outcome(
	self,
	submission_id: str,
	ai_assessment: ComplianceReport,
	actual_outcome: Outcome
	):
	"""
	Record actual BSR decision for learning.
	"""
	# Compare AI assessment with actual outcome
	comparison = self._compare_assessment_to_outcome(
	ai_assessment,
	actual_outcome
	)

	# Store for analysis
	self.db.store_outcome(
	submission_id=submission_id,
	ai_assessment=ai_assessment,
	actual_outcome=actual_outcome,
	comparison=comparison
	)

	# Update performance metrics
	self._update_metrics(comparison)

	# Flag cases for review
	if comparison.significant_discrepancy:
	self._flag_for_review(submission_id, comparison)

	def analyze_performance(self) -> PerformanceReport:
	"""
	Analyze system performance over time.
	"""
	outcomes = self.db.get_all_outcomes()

	metrics = {
	'overall_accuracy': self._calculate_accuracy(outcomes),
	'accuracy_by_part': self._calculate_accuracy_by_part(outcomes),
	'false_positive_rate': self._calculate_false_positive_rate(outcomes),
	'false_negative_rate': self._calculate_false_negative_rate(outcomes),
	'confidence_calibration': self._assess_confidence_calibration(outcomes)
	}

	# Identify patterns in errors
	error_patterns = self._identify_error_patterns(outcomes)

	# Generate recommendations
	recommendations = self._generate_improvement_recommendations(
	metrics,
	error_patterns
	)

	return PerformanceReport(
	metrics=metrics,
	error_patterns=error_patterns,
	recommendations=recommendations
	)

	def trigger_retraining(self, criteria: RetrainingCriteria) -> bool:
	"""
	Determine if model retraining is needed.
	"""
	performance = self.analyze_performance()

	should_retrain = (
	performance.accuracy < criteria.min_accuracy or
	performance.false_negative_rate > criteria.max_false_negative or
	performance.significant_error_patterns
	)

	if should_retrain:
	logger.info("Retraining triggered based on performance analysis")
	self._initiate_retraining(performance)

	return should_retrain
	```

	---

	## 7. PROFESSIONAL STANDARDS COMPLIANCE

	### 7.1 RBI/Chartered Building Engineer Code Alignment

	Key Principle: System provides compliance commentary, not design advice

	#### Distinguishing Commentary from Advice

	Commentary (Permitted):
	- "The submitted design does not meet Part B1 travel distance requirements (45m max, 60m provided)"
	- "Approved Document B Section 2.5 requires alternative means of escape for this occupancy"
	- "The specification lacks fire resistance ratings for the separating wall"
	- "Similar configurations have been rejected at Gateway 2 for inadequate compartmentation"

	Design Advice (Not Provided):
	- ✗ "You should relocate the stairway to the northwest corner"
	- ✗ "We recommend using 2-hour fire-rated construction"
	- ✗ "The optimal approach is to reduce the corridor length"
	- ✗ "Install an additional exit here [specific location]"

	Implementation in System:

	```python
	class ProfessionalStandardsValidator:
	"""
	Ensure all outputs comply with professional standards.
	"""

	PROHIBITED_PHRASES = [
	"you should",
	"we recommend",
	"you must",
	"the best approach",
	"optimal design",
	"ideal solution"
	]

	def validate_output(
	self,
	compliance_check: ComplianceCheck
	) -> ValidationResult:
	"""
	Ensure output is commentary, not advice.
	"""
	# Check reasoning and recommendations
	text_to_check = (
	compliance_check.reasoning + " " +
	" ".join(compliance_check.recommendations)
	).lower()

	# Flag prohibited phrases
	violations = [
	phrase for phrase in self.PROHIBITED_PHRASES
	if phrase in text_to_check
	]

	if violations:
	return ValidationFailure(
	violations=violations,
	suggestion="Rephrase to describe requirements rather than prescribe solutions"
	)

	# Check for imperative language
	if self._contains_imperatives(text_to_check):
	return ValidationFailure(
	issue="imperative_language",
	suggestion="Use 'requirements state' rather than 'you must'"
	)

	# Ensure regulatory basis cited
	if not compliance_check.regulatory_references:
	return ValidationFailure(
	issue="no_regulatory_basis",
	suggestion="Include regulatory references for all findings"
	)

	return ValidationSuccess()

	def reframe_as_commentary(
	self,
	advice_text: str
	) -> str:
	"""
	Convert advice-style text to commentary-style.
	"""
	# Use LLM to reframe
	prompt = f"""
	Reframe the following as regulatory commentary rather than design advice.

	Focus on:
	- What the regulations require
	- Where the submission falls short
	- What information is needed

	Avoid:
	- Prescriptive recommendations
	- Specific design solutions
	- Imperative language

	Original: {advice_text}

	Reframed commentary:
	"""

	reframed = self.llm.generate(prompt, temperature=0.1)

	return reframed
	```

	#### Limitation Disclaimers

	All outputs include clear disclaimers:

	```
	IMPORTANT DISCLAIMER:

	This compliance commentary is provided as a guidance tool to assist in
	preparing Building Regulations submissions. It does NOT constitute:

	- Design advice or recommendations
	- Professional engineering or architectural services
	- Final determination of compliance (only Building Safety Regulator can make final determinations)
	- A substitute for qualified professional review

	All findings should be reviewed by appropriately qualified professionals
	(Chartered Engineers, Registered Building Inspectors, etc.) before relying
	on them for submission or construction purposes.

	This system identifies potential compliance issues based on submitted
	documentation, but cannot account for all project-specific factors or
	replace expert judgment.

	The user retains full responsibility for ensuring submissions meet all
	applicable regulations and standards.
	```

	### 7.2 Liability and Professional Responsibility

	System Positioning:
	- Decision Support Tool: Augments human expertise, doesn't replace it
	- Early Warning System: Identifies potential issues for expert review
	- Documentation Aid: Helps organize and structure compliance evidence

	User Responsibility:
	- Verification: All AI findings must be verified by qualified professionals
	- Interpretation: Regulations require professional judgment in application
	- Final Decisions: Human experts make all final compliance determinations
	- Documentation: Users responsible for accuracy of submitted information

	Recommended Workflow:
	```
	1. Submit documents to BuildwellTHREAD
	2. Receive AI compliance commentary
	3. Review findings with qualified building engineer/RBI
	4. Conduct additional analysis as needed
	5. Make professional determination
	6. Prepare final Gateway submission
	7. Maintain audit trail of all review steps
	```

	---

	## 8. DEPLOYMENT AND OPERATIONS

	### 8.1 Deployment on Isambard AI

	#### Resource Allocation

	```yaml
	Isambard AI Configuration:
	Platform: Nvidia GH200 Grace-Hopper Superchips
	Operating System: Ubuntu 22.04 LTS
	Container Platform: Docker or Singularity

	Compute Resources (Example Allocation):
	Development/Training:
	- GPUs: 8x GH200 (480GB total GPU memory)
	- CPUs: 64 ARM cores
	- RAM: 1TB system memory
	- Storage: 10TB NVMe

	Production Inference:
	- GPUs: 4x GH200 (240GB total GPU memory)
	- CPUs: 32 ARM cores
	- RAM: 512GB system memory
	- Storage: 5TB NVMe

	Network:
	- High-speed interconnect between nodes
	- External connectivity for document uploads/downloads
	- Firewall rules for security
	```

	#### Software Stack

	```dockerfile
	# Base Image
	FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04

	# System dependencies
	RUN apt-get update && apt-get install -y \
	python3.11 \
	python3-pip \
	git \
	wget \
	wine64 \ # For FloorplanTransformation
	&& rm -rf /var/lib/apt/lists/*

	# Python dependencies
	COPY requirements.txt /tmp/
	RUN pip3 install -r /tmp/requirements.txt

	# Key packages
	# - transformers (for LLMs)
	# - pymilvus (vector DB client)
	# - neo4j (graph DB client)
	# - fastapi (API server)
	# - celery (task queue)
	# - pytorch (deep learning framework)

	# Application code
	COPY buildwellthread /app/buildwellthread
	WORKDIR /app

	# FloorplanTransformation setup
	COPY FloorplanTransformation /app/floorplan_transform
	ENV WINEPREFIX=/app/.wine

	# Entrypoint
	CMD ["uvicorn", "buildwellthread.api:app", "--host", "0.0.0.0", "--port", "8000"]
	```

	### 8.2 Operational Procedures

	#### Submission Processing Workflow

	```python
	# Orchestration with task queue

	from celery import Celery, chain

	app = Celery('buildwellthread')

	@app.task
	def ingest_documents(submission_id: str):
	"""
	Task 1: Ingest and parse uploaded documents.
	"""
	submission = load_submission(submission_id)

	parsed = document_ingestion.process(submission.files)

	save_parsed_documents(submission_id, parsed)

	return submission_id

	@app.task
	def extract_spatial_data(submission_id: str):
	"""
	Task 2: Process floorplans into spatial data.
	"""
	parsed = load_parsed_documents(submission_id)

	spatial_data = spatial_processor.process(parsed.drawings)

	save_spatial_data(submission_id, spatial_data)

	return submission_id

	@app.task
	def build_knowledge_context(submission_id: str):
	"""
	Task 3: Retrieve relevant regulations from RAG.
	"""
	submission = load_submission(submission_id)
	spatial_data = load_spatial_data(submission_id)

	context = rag_system.build_context(submission, spatial_data)

	save_context(submission_id, context)

	return submission_id

	@app.task
	def run_compliance_agents(submission_id: str):
	"""
	Task 4: Execute multi-agent compliance checking.
	"""
	submission = load_submission(submission_id)
	spatial_data = load_spatial_data(submission_id)
	context = load_context(submission_id)

	agent_results = orchestrator.process(submission, spatial_data, context)

	save_agent_results(submission_id, agent_results)

	return submission_id

	@app.task
	def generate_outputs(submission_id: str):
	"""
	Task 5: Generate reports and Golden Thread docs.
	"""
	agent_results = load_agent_results(submission_id)

	compliance_report = report_generator.generate(agent_results)
	golden_thread = golden_thread_generator.generate(agent_results)

	save_outputs(submission_id, compliance_report, golden_thread)

	# Notify user
	notify_user(submission_id, "complete")

	return submission_id

	# Chain tasks together
	def process_submission(submission_id: str):
	"""
	Orchestrate full processing pipeline.
	"""
	workflow = chain(
	ingest_documents.s(submission_id),
	extract_spatial_data.s(),
	build_knowledge_context.s(),
	run_compliance_agents.s(),
	generate_outputs.s()
	)

	result = workflow.apply_async()

	return result
	```

	#### Monitoring and Alerting

	```python
	class SystemMonitor:
	"""
	Monitor system health and performance.
	"""

	def collect_metrics(self):
	"""
	Collect system metrics for monitoring.
	"""
	return {
	'active_submissions': self.get_active_submission_count(),
	'queue_depth': self.get_queue_depth(),
	'processing_time_p50': self.get_processing_percentile(0.50),
	'processing_time_p95': self.get_processing_percentile(0.95),
	'error_rate': self.get_error_rate(),
	'gpu_utilization': self.get_gpu_utilization(),
	'memory_usage': self.get_memory_usage(),
	'storage_usage': self.get_storage_usage(),
	'database_latency': self.get_database_latency(),
	'confidence_distribution': self.get_confidence_distribution()
	}

	def check_health(self) -> HealthStatus:
	"""
	Perform health check on all components.
	"""
	checks = {
	'api': self.ping_api(),
	'task_queue': self.check_celery(),
	'vector_db': self.ping_milvus(),
	'graph_db': self.ping_neo4j(),
	'relational_db': self.ping_postgres(),
	'storage': self.check_storage_availability(),
	'model_server': self.check_model_availability()
	}

	all_healthy = all(checks.values())

	return HealthStatus(
	healthy=all_healthy,
	component_status=checks,
	timestamp=datetime.now()
	)

	def alert_if_needed(self, metrics: Dict):
	"""
	Send alerts for concerning metrics.
	"""
	# High error rate
	if metrics['error_rate'] > 0.10: # 10% errors
	self.send_alert(
	severity='HIGH',
	message=f"Error rate elevated: {metrics['error_rate']:.1%}"
	)

	# Long processing times
	if metrics['processing_time_p95'] > 7200: # 2 hours
	self.send_alert(
	severity='MEDIUM',
	message=f"P95 processing time: {metrics['processing_time_p95']/60:.0f} minutes"
	)

	# Queue backlog
	if metrics['queue_depth'] > 100:
	self.send_alert(
	severity='MEDIUM',
	message=f"Queue depth: {metrics['queue_depth']} submissions"
	)

	# Storage space
	if metrics['storage_usage'] > 0.85: # 85% full
	self.send_alert(
	severity='HIGH',
	message=f"Storage {metrics['storage_usage']:.0%} full"
	)
	```

	---

	## 9. CONCLUSION AND NEXT STEPS

	### 9.1 Summary

	BuildwellTHREAD represents a comprehensive solution for automating UK Building Regulations compliance checking and Golden Thread management. The system addresses critical challenges in the current Gateway submission process:

	Problems Solved:
	- High Gateway 2/3 rejection rates (40-67% → target <20%)
	- Lengthy manual review processes (days → hours)
	- Inconsistent compliance checking across projects
	- Difficulty maintaining Golden Thread documentation
	- Limited early detection of compliance issues

	Core Capabilities:
	- Multi-agent architecture with specialized compliance checking per Building Regulation Part
	- Multimodal document processing (text, drawings, specifications)
	- Intelligent spatial reasoning and measurement
	- Regulatory knowledge retrieval via RAG system
	- Automated Golden Thread generation and maintenance
	- Professional standards-compliant commentary (not design advice)

	Technical Foundation:
	- Deployment on Isambard AI supercomputer (Nvidia GH200)
	- Sophisticated error handling and quality assurance
	- Comprehensive audit trails for all decisions
	- Continuous learning and improvement mechanisms

	### 9.2 Development Roadmap

	Phase 1: Foundation (Months 1-3)
	- Set up infrastructure on Isambard AI
	- Develop document ingestion pipeline
	- Implement FloorplanTransformation integration
	- Build core RAG system
	- Establish databases (Milvus, Neo4j, PostgreSQL)

	Phase 2: Core Agents (Months 4-6)
	- Implement Part A (Structure) agent
	- Implement Part B (Fire Safety) agent
	- Implement Part M (Access) agent
	- Develop orchestrator framework
	- Build tool library (measurement, spatial reasoning)

	Phase 3: Comprehensive Coverage (Months 7-9)
	- Implement remaining Part agents (C-L, N-T)
	- Develop cross-part validation
	- Build Golden Thread generator
	- Create reporting system

	Phase 4: Testing and Validation (Months 10-12)
	- Validation against expert human reviews
	- Testing with real Gateway submissions
	- Performance optimization
	- User interface development
	- Documentation and training materials

	Phase 5: Deployment and Operations (Month 12+)
	- Production deployment
	- User onboarding
	- Continuous monitoring and improvement
	- Regular model updates based on outcomes

	### 9.3 Success Factors

	Critical for Success:
	1. High-Quality Training Data: Access to real Gateway submissions and BSR decisions
	2. Expert Collaboration: Ongoing input from building control professionals
	3. Iterative Refinement: Continuous improvement based on real-world performance
	4. Professional Standards: Strict adherence to RBI/Chartered Engineer codes
	5. Computational Resources: Reliable access to Isambard AI or equivalent infrastructure

	Risk Mitigation:
	1. Technical Risks: Fallback strategies for all critical dependencies
	2. Regulatory Risks: Clear positioning as decision support, not replacement for professionals
	3. Operational Risks: Comprehensive monitoring and alert systems
	4. Quality Risks: Multi-level validation and audit trails

	### 9.4 Expected Impact

	For Design Teams:
	- Faster identification of compliance issues (days → hours)
	- Clearer understanding of regulatory requirements
	- Higher quality submissions with fewer iterations
	- Reduced risk of costly late-stage redesigns

	For Building Control Bodies:
	- More consistent compliance checking
	- Reduced review burden for complete submissions
	- Better-documented decision trails
	- Improved communication with applicants

	For BSR:
	- Higher quality Gateway submissions
	- Reduced rejection rates
	- Better-maintained Golden Thread documentation
	- More efficient review process

	For Industry:
	- Accelerated project timelines
	- Reduced compliance-related costs
	- Improved building safety outcomes
	- Better regulatory adherence

	---

	## DOCUMENT CONTROL

	Version: 1.0
	Date: January 2026
	Author: BuildwellTHREAD Development Team
	Status: Development Specification

	Distribution:
	- Isambard AI Development Team
	- London Belgravia Surveyors
	- Building Safety Regulator (for consultation)
	- RBI/Chartered Building Engineers (for professional standards review)

	Revision History:
	- v1.0 (Jan 2026): Initial comprehensive specification

	Contact:
	For questions or clarifications, please contact the BuildwellTHREAD project team.

	---

	## APPENDICES

	### Appendix A: Building Regulation Parts Reference

	Quick reference for all UK Building Regulations Parts covered:

	- Part A: Structure
	- Part B: Fire Safety
	- Part C: Site Preparation and Resistance to Contaminants and Moisture
	- Part D: Toxic Substances
	- Part E: Resistance to the Passage of Sound
	- Part F: Ventilation
	- Part G: Sanitation, Hot Water Safety and Water Efficiency
	- Part H: Drainage and Waste Disposal
	- Part J: Combustion Appliances and Fuel Storage
	- Part K: Protection from Falling, Collision and Impact
	- Part L: Conservation of Fuel and Power
	- Part M: Access to and Use of Buildings
	- Part N: Glazing (Safety in Relation to Impact, Opening and Cleaning)
	- Part O: Overheating
	- Part P: Electrical Safety (Dwellings)
	- Part Q: Security (Dwellings)
	- Part R: Physical Infrastructure for High-Speed Electronic Communications Networks
	- Part S: Infrastructure for Charging of Electric Vehicles
	- Part T: Infrastructure for Broadband

	### Appendix B: Glossary

	BSR: Building Safety Regulator
	HRB: Higher-Risk Building (≥18m or ≥7 storeys)
	Gateway 2: BSR gateway for final design approval before construction
	Gateway 3: BSR gateway for completion certificate
	Golden Thread: Structured set of information about building throughout lifecycle
	RAG: Retrieval-Augmented Generation
	RBI: Registered Building Inspector
	BBA: British Board of Agrément
	KIWA: International certification body

	### Appendix C: Technical Specifications

	Model Requirements:
	- Base LLM: 7B-72B parameters (depends on deployment resources)
	- Embedding Model: NV-Embed-v2 or equivalent (768-dimensional)
	- Vision Model: CLIP or similar (if needed for diagram comparison)

	Database Requirements:
	- Vector DB: Milvus 2.x+ with HNSW indexing
	- Graph DB: Neo4j 5.x+ with GDS (Graph Data Science) library
	- Relational DB: PostgreSQL 14+ with full-text search capabilities

	Storage Requirements:
	- Regulatory corpus: ~50GB (Approved Documents, Standards, Guidance)
	- Submission documents: ~100GB per 1000 submissions
	- Processed spatial data: ~50GB per 1000 submissions
	- System logs and audit trails: ~10GB per month

	Compute Requirements:
	- Development: 8x high-end GPUs with 480GB+ total memory
	- Production: 4x GPUs with 240GB+ total memory for concurrent processing
	- CPU: 32-64 cores for parallel processing tasks

	---

	END OF DOCUMENT