Energy Bilateral Trade Agreement Compliance Agent with AutoGen
Energy conglomerates managing multi-billion dollar cross-border commercial agreements require continuous compliance negotiation and contract term checks. Build a multi-agent conversation framework using Microsoft AutoGen where buyer, seller, and legal agents cross-examine agreement clauses.
What you are building
The core problem, expected build, and operating context for this challenge.
Utilize Microsoft AutoGen multi-agent conversations to conduct automated compliance checks on bilateral trade contracts.
How work is evaluated
Evaluates clause compliance detection and group chat termination accuracy.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
compliance_flag_check
Ensures agent conversation correctly identifies non-compliant clause status.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
legal_reasoning_precision
Precision of extracted legal reasoning relative to target trade regulation. • target: 0.91 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct multi-agent chat conversations using Microsoft AutoGen UserProxyAgent and AssistantAgent
Enforce compliance validation against French and Saudi trade legal frameworks
Incorporate human-in-the-loop validation triggers for non-standard indemnity clauses
Extract structured clause obligation matrices from conversational turns
Reference links and supporting material
Sample bilateral trade agreements, supply contract clauses, and cross-border commercial compliance rules between French and Saudi energy entities.
How this agent runs
Evaluates clause compliance detection and group chat termination accuracy.
Challenge input
JSON with agreement_clause_text and target_jurisdiction
AutoGen
Microsoft framework for building multi-agent conversational ecosystems.
Evaluated output
JSON with compliance_status (COMPLIANT|NON_COMPLIANT), flag_reason, and suggested_revision
- Ensures agent conversation correctly identifies non-compliant clause status.
- Precision of extracted legal reasoning relative to target trade regulation. • target: 0.91 • range: 0-1
- Benchmark: Legal-Compliance-Bench
- Legal Reasoning Precision target: 0.91
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- AutoGen · Required
- Exa · Optional
- Vercel AI Gateway · Optional
- Vercel AI Gateway · Optional
Evaluation contract
- compliance_flag_check · Weight 1
- legal_reasoning_precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.