You are the CFO of a $50M/year commercial HVAC contractor. Last quarter, three projects closed at 6.8% realized margin against a 15.2% bid margin. This is no longer an anomaly — it’s a pattern. Dashboards show data.You need something that thinks. Your mission is to build an AI agent — not a chatbot — that autonomously monitors a portfolio of HVAC construction projects, identifies margin erosion risks, investigates root causes, takes action, and reports back. The agent must:
Scan the entire portfolio
Detect margin risk signals
Investigate by chaining tool calls
Produce actionable outputs
Support follow-up questions with memory
Communicate findings clearly in business language
This is not a dashboard challenge.This is not a chatbot challenge. You are building an autonomous AI agent using:
An LLM (reasoning brain)
Tool calling (data access + calculations)
Memory
A looping mechanism (stopWhen)
Email capability (proactive reporting)
Your agent should continue investigating until it understands the situation — not stop after one query. Participants receive a realistic construction portfolio dataset (~18K records), including:
contracts.csv
sov.csv
sov_budget.csv
labor_logs.csv
material_deliveries.csv
billing_history.csv
billing_line_items.csv
change_orders.csv
rfis.csv
field_notes.csv (~1,300 unstructured reports)
Embedded within the data are real-world signals:
Labor overruns
Scope drift
Verbal approvals
Billing lags
Pending change orders
RFI-related exposure
Not every project is failing.Not every risk is obvious.Your agent must find the story. Given a prompt like: Your agent should autonomously:
Assess overall margin health
Identify high-risk projects
Investigate root causes (labor, materials, billing, change orders, RFIs, field notes)
Quantify financial exposure
Recommend specific recovery actions
Send an email summary or alert
Support follow-up conversation with context memory
860%2011th%20Ave%2C%20New%20York%2C%20NY%2010019%2C%20USA
Mitchell, DJ, Mark
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