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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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What you are building

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

How work is evaluated

Evaluation

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

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

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

  • 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

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)

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