multi-agent-estimation

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
)