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Challenge

Offshore Field Expansion Multi-Agent System with CrewAI

Offshore engineering consortia require collaborative evaluation of EPC contract bids across technical, commercial, and local content metrics. Build a multi-agent bidding evaluation swarm using CrewAI where specialized agents analyze bid submissions.

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Challenge brief

What you are building

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

Orchestrate role-playing CrewAI agents (Technical Auditor, Cost Estimator, Compliance Officer) to analyze complex offshore bids.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent score output consistency and trade-off synthesis in offshore tender reviews.

Datasets

Challenge datasets

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

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

score_range_check

Verifies composite score falls between 0 and 100.

Binary check

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

Dimension 2

consensus_agreement

Degree of alignment between agent scores and benchmark ground truth. • target: 0.94 • 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 collaborative Agent roles and Goals using CrewAI

  • Set up sequential and hierarchical Task execution pipelines

  • Integrate custom search and procurement analysis tools

  • Produce consolidated EPC bidder scorecards with executive recommendations

Resources and assets

Reference links and supporting material

Dataset notes

Offshore platform engineering proposals, jacket procurement costs, and ICV (In-Country Value) breakdown scores for Qatar offshore expansions.

Challenge setup

Evaluates multi-agent score output consistency and trade-off synthesis in offshore tender reviews.

Challenge input

JSON containing bidder_name, technical_spec_compliance_pct, and bid_price_usd_m

CrewAI

Framework for multi-agent role-playing and collaboration.

Evaluated output

JSON with winning_bidder, composite_score, and primary_rationale

Evaluation checks
  • Verifies composite score falls between 0 and 100.
  • Degree of alignment between agent scores and benchmark ground truth. • target: 0.94 • range: 0-1
Configured evaluation evidence
  • Benchmark: Procurement-Consensus-Eval
  • Consensus Agreement target: 0.94
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

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

Evaluation contract

  • score_range_check · Weight 1
  • consensus_agreement · Weight 1

Metrics

  • consensus_agreement · Target 0.94

Benchmarks

  • Procurement-Consensus-Eval
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing bidder_name, technical_spec_compliance_pct, and bid_price_usd_m

    Output: JSON with winning_bidder, composite_score, and primary_rationale

    Execution harness: Python

Evaluation tools

  • CrewAI · agent_framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Offshore Field Expansion Multi-Agent System with CrewAI