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Challenge

Build Autonomous Data Center Energy Negotiator with LangChain and Pydantic AI

Develop a multi-agent system to simulate energy procurement strategy for large-scale AI infrastructure. The system uses Pydantic AI models for structured decision-making within a LangChain graph workflow. The agents analyze news on energy costs and regulatory hurdles, using Bland AI to conduct mock negotiations with utility providers, while Patronus AI monitors and evaluates the agent's negotiation logic against safety and policy constraints.

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Challenge brief

What you are building

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

Orchestrate a LangChain multi-agent system using Pydantic AI to simulate autonomous energy procurement negotiations for AI data centers.

Delivery guide

How work is evaluated

Evaluation

Evaluation of agent negotiation outcomes and policy adherence.

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

PolicyCheck

Ensure no violation of regulatory constraints

Binary check

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

Dimension 2

CostReduction

Percentage below base rate • target: 20 • range: 0-100

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

  • Master the integration of Pydantic AI models into LangChain state graphs for robust, type-safe agent decision trees

  • Implement multi-agent loops where a negotiator agent and a strategy agent iteratively refine energy procurement tactics

  • Orchestrate Patronus AI evaluation hooks at each turn of the negotiation to ensure compliance with predefined cost-efficiency policies

  • Build real-time voice interface pipelines with Bland AI for agent-to-human negotiation simulations

  • Utilize Gemini 3.1 Pro via LangChain tool-calling to synthesize complex regulatory news inputs

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

Evaluation of agent negotiation outcomes and policy adherence.

Challenge input

JSON

LangChain

Framework for building LLM applications

Patronus AI

Evaluation and guardrail platform.

Evaluated output

JSON

Evaluation checks
  • Ensure no violation of regulatory constraints
  • Percentage below base rate • target: 20 • range: 0-100
Configured evaluation evidence
  • CostReduction target: 20
  • 1 public reference case
Configured runtime
  • Python execution harness

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
Reward / Eval
  • Patronus AI · Optional
Safety / Guardrails
  • Patronus AI · Optional
Orchestration
  • LangChain · Required
  • Langchain · Optional

Evaluation contract

  • PolicyCheck · Weight 1
  • CostReduction · Weight 1

Metrics

  • CostReduction · Target 20

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON

    Output: JSON

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

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