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Challenge

Sanctions Regime Transition Monitor with LangChain and Modal

Rapid shifts in state terrorism designations require instant dynamic updates to global transaction filtering rules without introducing processing downtime. You will construct a serverless LangGraph streaming pipeline on Modal that evaluates dynamic compliance policies, ensuring zero unmonitored payment transactions during active regulatory transitions.

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Challenge brief

What you are building

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

Build a serverless dynamic policy update pipeline on Modal using LangChain and LangGraph to process transaction streams through updated regulatory state engines.

Delivery guide

How work is evaluated

Evaluation

Evaluates transaction clearance logic before and after dynamic policy update event.

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

policy_switch_precision

Ensures policy switch applies immediately to post-update transactions

Binary check

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

Dimension 2

switch_latency_ms

Time taken to apply new rule engine in pipeline • target: 50 • range: 0-500

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 dynamic stateful graphs using LangChain and LangGraph

  • Deploy serverless, high-concurrency event handlers on Modal runtime

  • Handle live policy switches during ongoing transaction streaming

  • Achieve zero transaction processing downtime during compliance rule updates

Resources and assets

Reference links and supporting material

Dataset notes

Stream of 5,000 cross-border transactions straddling a simulated regulatory status transition event.

Challenge setup

Evaluates transaction clearance logic before and after dynamic policy update event.

Challenge input

JSON containing transaction jurisdiction and timestamp

LangChain

Provides LangGraph state machinery and framework primitives.

Modal

Serverless Python deployment engine for high-concurrency execution.

Evaluated output

JSON with status ('CLEARED'|'BLOCKED') and applied_policy_version

Evaluation checks
  • Ensures policy switch applies immediately to post-update transactions
  • Time taken to apply new rule engine in pipeline • target: 50 • range: 0-500
Configured evaluation evidence
  • Benchmark: SanctionSwitch-Eval
  • Switch Latency Ms target: 50
  • 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.

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Configured tools

Environment
  • Modal · Optional
Action Space
  • Langchain · Required
  • LangChain · Optional
Policy Serving
  • Modal · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • policy_switch_precision · Weight 1
  • switch_latency_ms · Weight 1

Metrics

  • switch_latency_ms · Target 50

Benchmarks

  • SanctionSwitch-Eval
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing transaction jurisdiction and timestamp

    Output: JSON with status ('CLEARED'|'BLOCKED') and applied_policy_version

    Execution harness: Python

Evaluation tools

  • LangChain · agent_framework
  • Modal · inference_and_model_runtime

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Sanctions Regime Transition Monitor with LangChain and Modal