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Challenge

High-Throughput Office Asset Scoring Engine using Google ADK and TensorRT-LLM

Institutional investors evaluating return-to-office market trends struggle to process thousands of municipal building dossiers in real time. Build a high-throughput asset scoring engine using Google ADK and TensorRT-LLM inference runtime. Achieve sub-100ms inference latency per asset while maintaining 90% classification accuracy.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop an optimized scoring pipeline with Google ADK agents powered by TensorRT-LLM serving engine to score office real estate assets.

Delivery guide

How work is evaluated

Evaluation

Evaluates classification accuracy of asset scoring and verifies sub-100ms latency execution.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

latency_threshold_check

Validates execution speed remains under 100ms

Binary check

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

Dimension 2

tier_classification_accuracy

Accuracy of property asset tier assignment • 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

What you should walk away with

  • Construct a Google ADK agent pipeline utilizing Vertex AI / Gemini models for office asset grading

  • Deploy optimized local LLM endpoints using NVIDIA TensorRT-LLM for ultra-low latency execution

  • Score assets based on transit proximity, tenant credit quality, building energy rating, and flight-to-quality indicators

  • Benchmark end-to-end inference speed and throughput across large building portfolios

Resources and assets

Reference links and supporting material

Dataset notes

Commercial office building profile dataset containing occupancy figures, transit scores, and sustainability accreditations.

How this agent runs

Evaluates classification accuracy of asset scoring and verifies sub-100ms latency execution.

Challenge input

JSON containing occupancy_rate, transit_walk_score, lead_cert, tenant_type

Google ADK

Google Agent Development Kit for building Gemini-powered agent applications.

TensorRT-LLM

High-performance inference engine for accelerated LLM execution.

Evaluated output

JSON containing asset_tier, capital_investment_recommended, execution_time_ms

Checks for
  • Validates execution speed remains under 100ms
  • Accuracy of property asset tier assignment • target: 0.9 • range: 0-1
Proof of success
  • Benchmark: CRE Asset Classification Benchmark
  • Tier Classification Accuracy target: 0.9
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Google ADK · Required
  • Google · Optional
  • TensorRT-LLM · Optional

Evaluation contract

  • latency_threshold_check · Weight 1
  • tier_classification_accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about High-Throughput Office Asset Scoring Engine using Google ADK and TensorRT-LLM