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Challenge

Build an Embedded Payment Optimization Engine with LangChain and Amazon Bedrock

Complex embedded software payments demand real-time intelligent routing across multiple payment processors. Develop a dynamic payment router using LangChain and Amazon Bedrock to minimize processing fees while avoiding payment downtime.

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Challenge brief

What you are building

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

Build a stateful LangGraph agent hosted on Amazon Bedrock to dynamically select optimal payment processing rails.

Delivery guide

How work is evaluated

Evaluation

Evaluates cost savings and failover resilience on dynamic transaction batches.

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

test_degraded_bypass

Ensure degraded processor is bypassed despite lower cost

Binary check

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

Dimension 2

routing_efficiency

Percentage optimal cost-reliability choices made • target: 0.95 • range: 0.85-1

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

  • Construct a stateful routing agent with LangGraph and Amazon Bedrock.

  • Calculate real-time fee trade-offs between Interchange Plus and flat-rate payment rails.

  • Implement automatic failover logic when processing network latency spikes.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic embedded payment stream with varying card types, amounts, and live provider uptime signals.

Challenge setup

Evaluates cost savings and failover resilience on dynamic transaction batches.

Challenge input

JSON transaction request with card_type, transaction_amount, rail_statuses

LangChain

Framework requirement

Amazon Bedrock

Inference tool requirement

Evaluated output

JSON with selected_gateway, estimated_fee, route_reasoning

Evaluation checks
  • Ensure degraded processor is bypassed despite lower cost
  • Percentage optimal cost-reliability choices made • target: 0.95 • range: 0.85-1
Configured evaluation evidence
  • Benchmark: Payment Routing Benchmark v1
  • Routing Efficiency target: 0.95
  • 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
  • Langchain · Required
  • LangChain · Optional
  • Amazon Bedrock · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • test_degraded_bypass · Weight 1
  • routing_efficiency · Weight 1

Metrics

  • routing_efficiency · Target 0.95

Benchmarks

  • Payment Routing Benchmark v1
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON transaction request with card_type, transaction_amount, rail_statuses

    Output: JSON with selected_gateway, estimated_fee, route_reasoning

    Execution harness: Python

Evaluation tools

  • LangChain · agent_framework
  • Amazon Bedrock · inference_runtime

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build an Embedded Payment Optimization Engine with LangChain and Amazon Bedrock