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Challenge

Orchestrate Life-Sciences Park Feasibility Team with CrewAI

Evaluating complex life-science development projects requires cross-functional synthesis across market demand, specialized MEP engineering, and biotech zoning. Build a CrewAI agent team to collaborate on site selection reports and achieve an evaluation pass rate of 90% on benchmark cases.

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Challenge brief

What you are building

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

Deploy a multi-agent CrewAI orchestration with specialized agent roles for market research, biotech zoning, and specialized MEP compliance.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent CrewAI output completeness and domain constraint validation.

Datasets

Shared data for this challenge

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

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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

Multi-Specialist Criteria Test

Verifies both zoning and MEP agent assertions are reflected in final decision.

Binary check

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

Dimension 2

Feasibility Accuracy

Correct classification of site suitability for biotech tenants. • 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

  • Define specialized CrewAI agents (Zoning Specialist, MEP Engineer, Life Science Market Analyst).

  • Design sequential and hierarchical tasks to create comprehensive site feasibility blueprints.

  • Implement agent memory and tool sharing for cross-domain validation.

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of international life science hub site properties, including structural floor loading ratings, vibration dampening specs, and HVAC air exchanges.

How this agent runs

Evaluates multi-agent CrewAI output completeness and domain constraint validation.

Challenge input

JSON with site_location, floor_load_capacity_psf, hvac_air_changes_per_hr, lab_biosafety_level

CrewAI

Assigned framework for role-playing multi-agent workflows.

Evaluated output

JSON with overall_viability, zoning_clearance, mep_readiness_score, key_recommendation

Checks for
  • Verifies both zoning and MEP agent assertions are reflected in final decision.
  • Correct classification of site suitability for biotech tenants. • target: 0.9 • range: 0-1
Proof of success
  • Benchmark: MultiAgent_Collaboration_Eval
  • Feasibility Accuracy target: 0.9
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • crewAI · Optional
  • Zed · Optional

Evaluation contract

  • Multi-Specialist Criteria Test · Weight 1
  • Feasibility Accuracy · Weight 1

Recipe state

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

Frequently Asked Questions about Orchestrate Life-Sciences Park Feasibility Team with CrewAI