Build a Multi-Agent Data Pipeline in 50 Lines of Neam

#ai #machinelearning #opensource #programming

In this tutorial, you'll build a working multi-agent data pipeline using Neam, an agentic AI programming language. By the end, you'll have a DIO orchestrating five agents through a churn prediction workflow.

Step 1: Define Your Infrastructure Profile. This tells every agent where data lives and what compliance rules apply:

infrastructure_profile MyInfra {
    data_warehouse: {
        platform: "postgres",
        connection: env("DB_URL")
    },
    governance: { regulations: ["GDPR"] }
}

Step 2: Declare Your Agents. Each agent is a specialist. Note the budget constraints:

budget B { cost: 50.00, tokens: 500000 }

databa agent MyBA { provider: "openai",
    model: "gpt-4o", budget: B }
datascientist agent MyDS { provider: "openai",
    model: "gpt-4o", budget: B }
datatest agent MyDT { provider: "openai",
    model: "gpt-4o", budget: B }

Step 3: Wire Up the DIO. The orchestrator coordinates everything:

budget DioBudget { cost: 500.00, tokens: 2000000 }

dio agent MyDIO {
    mode: "hybrid",
    task: "Predict customer churn, identify drivers",
    infrastructure: MyInfra,
    agent_md: "./my_domain.agent.md",
    provider: "openai", model: "gpt-4o",
    budget: DioBudget
}

let result = dio_solve(MyDIO, task)
print(result)

Step 4: Create Your Agent.MD. This is the secret weapon — encode domain knowledge:

## @organization-context
Company: My E-Commerce Co
Scale: 500K customers, 5M orders

## @known-data-issues
- signup_date timezone drift before 2024-03
- Product ratings skew positive (self-reported)

## @agent-preferences
DataScientist: XGBoost for tabular, AUC-ROC metric