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cre-acquisition-orchestrator

by ahacker-1

Multi-agent CRE acquisition orchestrator with phase-based agent workflows

TypeScript
Updated Jul 15, 2026
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Summary

Orchestrates a 31-agent workflow for commercial real estate (CRE) acquisitions across five deal phases. Uses domain-specialist agents (underwriting, due diligence, market analysis) and a real-time dashboard to visualize progress and outcomes without requiring API keys. Distinctive for its large, domain-focused agent pool and phase-based orchestration tailored to CRE transactions. This setup aligns with the Orchestrator-Worker Pattern.

Why It Matters

As agents take on specialized roles, understanding their track records and failure modes in real workflows becomes essential for trust. This repo surfaces agent behaviors across deal phases so teams can audit decisions, spot weak agents, and build reputation signals tied to concrete outcomes. Until now CRE-focused multi-agent examples were rare; this provides a domain-specific testbed for agent-to-agent evaluation and operational debugging. It also highlights potential issues like Inter-Agent Miscommunication to consider during deployment. Inter-Agent Miscommunication

Ideal For

Proptech teams and researchers building or evaluating multi-agent pipelines for commercial real estate deal analysis and underwriting. See how this maps to scalable coordination with patterns like Market-Based Coordination Pattern and Semantic Capability Matching Pattern to improve selection and alignment of agent capabilities.

Applications

  • Run end-to-end CRE deal simulations to observe agent delegation and failure modes
  • Audit and compare agent decisions across underwriting, due diligence, and market analysis phases
  • Prototype reputation signals by tracking agent outcomes through multi-phase transactions
Works With
reacttypescript
Topics
agent-orchestrationai-agentscommercial-real-estatecredue-diligencellm-orchestrationmulti-agent-systemsproptechreactreal-estate+2 more
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Keywords
multi-agent trustmulti-agent orchestrationagent-to-agent evaluation