Multi-Agent Estimation System
Overview
In 2026, AI agents are moving from single-task assistants to orchestrated multi-agent systems. This skill enables building a crew of specialized AI agents that work together to automate construction estimation.
"Thanks to LLM nodes, you can simply ask ChatGPT, Claude, or any advanced AI assistant to generate n8n automation pipelines — whether for extracting tables from PDFs, validating parameters, or producing custom QTO tables — and get ready-to-run workflows in seconds." — Artem Boiko
Architecture
┌─────────────────────────────────────────────────────────────────┐ │ MULTI-AGENT ESTIMATION │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ QTO │ │ Pricing │ │Validation│ │ Report │ │ │ │ Agent │──▶│ Agent │──▶│ Agent │──▶│ Agent │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ │ │ │ │ │ │ │ ▼ ▼ ▼ ▼ │ │ Extract Match to Validate Generate │ │ quantities CWICR DB totals Excel/PDF │ │ │ └─────────────────────────────────────────────────────────────────┘
Quick Start with CrewAI
from crewai import Agent, Task, Crew from langchain_openai import ChatOpenAI
Initialize LLM
llm = ChatOpenAI(model="gpt-4o", temperature=0)
QTO Agent - Extracts quantities from documents
qto_agent = Agent( role="Quantity Takeoff Specialist", goal="Extract accurate quantities from IFC models and PDF drawings", backstory="""You are an expert quantity surveyor with 20 years of experience in construction. You meticulously extract volumes, areas, and counts from building models and drawings.""", llm=llm, verbose=True )
Pricing Agent - Matches items to price database
pricing_agent = Agent( role="Cost Estimator", goal="Match extracted quantities to CWICR database and apply unit rates", backstory="""You are a senior estimator who knows construction costs inside out. You match work items to standardized codes and apply appropriate unit rates based on project location and conditions.""", llm=llm, verbose=True )
Validation Agent - Checks for errors and outliers
validation_agent = Agent( role="Quality Assurance Specialist", goal="Validate estimate accuracy and flag potential errors", backstory="""You review estimates for completeness, accuracy, and reasonableness. You catch errors that others miss and ensure estimates are defensible.""", llm=llm, verbose=True )
Report Agent - Generates final deliverables
report_agent = Agent( role="Report Generator", goal="Create professional estimate reports in Excel and PDF", backstory="""You transform raw estimate data into polished, professional reports that clients can understand and trust.""", llm=llm, verbose=True )
Define Tasks
Task 1: Extract quantities from IFC
qto_task = Task( description=""" Extract all quantities from the provided IFC model: - Walls: volumes, areas, lengths - Slabs: areas, volumes - Columns: counts, volumes - Beams: lengths, volumes
Group by building level and element type.
Output as structured JSON.
""",
expected_output="JSON with quantities grouped by level and type",
agent=qto_agent
)
Task 2: Match to price database
pricing_task = Task( description=""" For each extracted quantity: 1. Match to CWICR code using semantic search 2. Apply unit rate from price database 3. Calculate line item totals 4. Add markup percentages (OH&P, contingency)
Output detailed cost breakdown.
""",
expected_output="Cost breakdown with CWICR codes and totals",
agent=pricing_agent,
context=[qto_task]
)
Task 3: Validate estimate
validation_task = Task( description=""" Review the estimate for: - Missing scope items - Unrealistic unit rates (compare to historical) - Math errors - Inconsistent quantities
Flag any issues with severity rating.
""",
expected_output="Validation report with issues and severity",
agent=validation_agent,
context=[pricing_task]
)
Task 4: Generate report
report_task = Task( description=""" Generate professional estimate report: - Executive summary with total - Detailed breakdown by CSI division - Assumptions and exclusions - Risk items identified during validation
Format for Excel export.
""",
expected_output="Formatted estimate report ready for export",
agent=report_agent,
context=[pricing_task, validation_task]
)
Run the Crew
Create the crew
estimation_crew = Crew( agents=[qto_agent, pricing_agent, validation_agent, report_agent], tasks=[qto_task, pricing_task, validation_task, report_task], verbose=True )
Execute
result = estimation_crew.kickoff(inputs={ "ifc_path": "building.ifc", "price_db": "cwicr_prices.xlsx", "project_location": "Berlin, Germany" })
print(result)
n8n Integration
{ "workflow": "Multi-Agent Estimation", "nodes": [ { "name": "Trigger", "type": "Webhook", "note": "Receive IFC file upload" }, { "name": "QTO Agent", "type": "AI Agent", "model": "gpt-4o", "tools": ["ifcopenshell", "pandas"] }, { "name": "Pricing Agent", "type": "AI Agent", "model": "gpt-4o", "tools": ["qdrant_search", "cwicr_api"] }, { "name": "Validation Agent", "type": "AI Agent", "model": "gpt-4o", "tools": ["historical_db", "outlier_detection"] }, { "name": "Generate Excel", "type": "Spreadsheet", "operation": "create" }, { "name": "Send Email", "type": "Email", "to": "estimator@company.com" } ] }
Why Multi-Agent in 2026?
Single Agent Multi-Agent
One prompt, one task Specialized experts collaborate
Context limits Distributed memory
Single point of failure Redundancy and validation
Hard to debug Clear responsibility
Generic output Domain-specific quality
Requirements
pip install crewai langchain-openai ifcopenshell pandas qdrant-client
Resources