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Build Office REIT Lease Restructuring Simulator using AutoGen Multi-Agent Conversations

With commercial REITs facing impending debt defaults and going-concern risks, negotiating lease extensions and tenant retention terms is vital. Developers will build a multi-agent negotiation framework using AutoGen. The framework features a REIT Asset Manager Agent, a Tenant Representative Agent, and a Lender Agent. The agents engage in multi-turn automated dialogue to negotiate rent reductions, lease extension durations, and landlord tenant improvement (TI) allowances, seeking a consensus that avoids bankruptcy while preserving property cash flow.

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What you are building

Develop an autonomous multi-agent negotiation simulator using AutoGen to model office lease restructurings under debt distress.

How work is evaluated

Evaluation

Evaluates negotiation efficiency, consensus reaching probability, and post-negotiation DSCR impact.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

DSCR Threshold Compliance

Verifies negotiated lease terms maintain REIT DSCR above minimum threshold (1.15)

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Consensus Rate

Rate of successfully reaching valid restructuring agreement • target: 0.9 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Configure AutoGen ConversationalAgents representing REIT Manager, Tenant, and Lender

  • Implement custom conversation turn limits, group chat management, and human-in-the-loop controls

  • Model cash flow optimization trade-offs between landlord concession costs and vacancy loss

  • Extract structured consensus lease agreements from multi-agent negotiation transcripts

Resources

Dataset notes

Dataset of 30 commercial lease agreement scenarios experiencing lease expiration within 12 months under default risk.

Challenge setup

Evaluates negotiation efficiency, consensus reaching probability, and post-negotiation DSCR impact.

Challenge input

JSON with initial_rent_psf, remaining_lease_years, tenant_threat_level, reit_min_dscr

AutoGen

Required framework for multi-agent dynamic conversations.

Evaluated output

JSON with consensus_reached (boolean), new_rent_psf, extension_years, ti_allowance_psf, projected_dscr

Evaluation checks
  • Verifies negotiated lease terms maintain REIT DSCR above minimum threshold (1.15)
  • Rate of successfully reaching valid restructuring agreement • target: 0.9 • range: 0-1
Configured evaluation evidence
  • Benchmark: NegotiationBench 2025
  • Consensus Rate target: 90%
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • AutoGen · Required
  • Sarvam AI · Optional
  • Confident AI · Optional

Evaluation contract

  • DSCR Threshold Compliance · Weight 1
  • Consensus Rate · Weight 1

Metrics

  • Consensus Rate · Target 0.9

Benchmarks

  • NegotiationBench 2025
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with initial_rent_psf, remaining_lease_years, tenant_threat_level, reit_min_dscr

    Output: JSON with consensus_reached (boolean), new_rent_psf, extension_years, ti_allowance_psf, projected_dscr

    Execution harness: Python

Evaluation tools

  • AutoGen · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build Office REIT Lease Restructuring Simulator using AutoGen Multi-Agent Conversations