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🛡 05 | Data Governance & MLOps Agentification (agent-mlops)

"You cannot govern the AI layer with human processes running at human speed."

📋 The Problem

As enterprises rush to build production RAG systems and deploy LLMs across operations, data security and model reliability become major bottlenecks: * Silent Data Degradation: Data in Snowflake/Databricks databases degrades in quality silently, leading to incorrect agent outputs. * Undetected Model Drift: Downstream production model outputs drift as consumer patterns shift, resulting in "hallucination spikes." * Documentation & Lineage Churn: Regulatory compliance reporting (e.g., GDPR, EU AI Act, SOC2) requires tracking data lineage from raw ingestion to model outputs—a process that is highly manual and error-prone.

📐 The Solution: agent-mlops

agent-mlops is an autonomous AI governance and MLOps monitoring platform. Instead of relying on manual reviews or rigid static rule alerts, it deploys active agents that continuously profile databases, audit model parameters, trace data lineage, and trigger automated retraining loops when anomalies are detected.

graph TD
    Snowflake[("Snowflake / Databricks")] --> DataAgent[Data Quality Profiler]
    Models[Production Models] --> ModelAgent[Drift Auditor]

    DataAgent -->|Anomalies Detected| Alert[Governance Orchestrator]
    ModelAgent -->|Drift > Threshold| Retrain[Trigger Retraining pipeline]

    Alert -->|Trace Lineage| Auditor[Audit Report Generator]
    Retrain -.-> ModelAgent

    Auditor -->|Compliance Report| Compliance[Regulators / SOC 2]

    style DataAgent fill:#eefcff,stroke:#00a3e0,stroke-width:1px
    style ModelAgent fill:#eefcff,stroke:#00a3e0,stroke-width:1px

⚡ Core Agentic Patterns & Capabilities

1. Data Quality Profiling Agents

Data profiling agents act as background system processes, continuously running schema, typing, value distribution, and relationship validity checks on core Snowflake/Databricks tables. If an anomaly is identified (e.g., null counts in key demographic columns spike), the agent isolates the table and flags the ingestion source.

2. Autonomous Drift Detection & Retraining

The drift auditor intercepts production API request/response logs, mapping embeddings to detect semantic drift. If drift surpasses a critical mathematical threshold, the auditor triggers a LangGraph workflow that automatically extracts the latest training sets, kicks off a model retraining pipeline, and validates the updated model outputs against a baseline gold-standard test set.

3. Automated Lineage Tracing

A specialized tracing agent inspects code repositories, database queries, and API logs to reconstruct a complete, verifiable graph of data lineage. If a field's source code is updated, the lineage is instantly re-mapped, and an audit-ready compliance document is compiled.

🛠 Tech Stack

  • Orchestration: Python 3.11+, LangGraph, MLflow
  • Database: PostgreSQL (pgvector for embedding analysis), Snowflake API
  • Monitoring: Prometheus, OpenTelemetry
  • Compliance Frameworks: Patterns mapped to EU AI Act, GDPR, and SOC2 Section CC CC6.x.

🔗 Repository

Explore the complete codebase and deployment instructions: * GitHub Repository: https://github.com/mailtotanvir/agent-mlops