Multi Agent Estimation

What This Skill Does

The Multi-Agent Estimation skill is designed to transition construction firms from manual, error-prone spreadsheets to highly automated, agentic workflows. By leveraging frameworks like CrewAI and LangGraph, this skill orchestrates a team of specialized AI agents—a QTO (Quantity Takeoff) agent, a Pricing agent, a Validation agent, and a Reporting agent. The system operates as a pipelined assembly line: the QTO agent parses technical drawings and IFC models to extract structural data, the Pricing agent correlates this data with standardized cost databases like CWICR, the Validation agent performs logic checks to identify outliers or omissions, and the Reporting agent compiles the final output into professional documentation. This framework reduces human fatigue and significantly increases the precision of construction estimates.

Installation

To integrate this skill into your environment, run the following command in your terminal: clawhub install openclaw/skills/skills/datadrivenconstruction/multi-agent-estimation Ensure you have your environment variables set for your preferred LLM provider (e.g., OPENAI_API_KEY) and access to the necessary data repositories.

Use Cases

• Residential and Commercial Estimation: Automatically generate preliminary cost estimates for new build projects using architect floor plans.
• Material Takeoff Digitization: Convert legacy PDF drawing sets into structured JSON or CSV quantity lists.
• Risk Mitigation in Bidding: Use the validation agent to cross-reference bid items against historical project costs to detect potential pricing anomalies.
• Rapid Iteration: Perform quick 'what-if' scenarios by modifying material specs and re-running the estimate pipeline in minutes.

Example Prompts

1. "Run a full takeoff on the structural steel plans in the 'Project_Alpha_Drawings' folder and generate a preliminary estimate."
2. "Update the existing estimate for the 'HighRise_V2' project with current concrete unit rates from our Q3 supplier database."
3. "Review the pending bid for the 'Retail_Center' project and flag any line items that are 20% higher than our average historical cost for similar work."

Tips & Limitations

• Data Quality: The system is highly dependent on the quality of the input files. Clear, well-labeled IFC models yield the best results.
• Human-in-the-Loop: While the agents are powerful, the Validation agent should always flag high-risk items for a human senior estimator's final review before a bid is submitted.
• LLM Selection: Use high-reasoning models like GPT-4o or Claude 3.5 Sonnet for complex spatial reasoning during the QTO phase to ensure accurate volume and count calculations.