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.
What you are building
The core problem, expected build, and operating context for this challenge.
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.
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.
PolicyCheck
Ensure no violation of regulatory constraints
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
CostReduction
Percentage below base rate • target: 20 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
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
How this agent runs
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
- Ensure no violation of regulatory constraints
- Percentage below base rate • target: 20 • range: 0-100
- CostReduction target: 20
- 1 public reference case
- Python execution harness
View technical recipe
Configured tools
- LangChain · Required
- Langchain · Optional
- Patronus AI · Optional
- Patronus AI · Optional
- LangChain · Required
- Langchain · Optional
Evaluation contract
- PolicyCheck · Weight 1
- CostReduction · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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