Cleanagent

Cleanagent is a software concept describing an automated, agent-based system designed to perform data cleaning and quality assurance within data pipelines. The term can refer to a framework, library, or platform that coordinates a set of modular cleaning agents to identify, correct, and validate data issues.

In operation, cleaning agents are small, purpose-built components that apply rules, heuristics, or machine learning models to tasks such as anomaly detection, missing-value imputation, standardization of formats, deduplication, normalization, and record reconciliation. A central orchestrator schedules tasks, coordinates agent execution, and enforces reproducibility through versioned configurations and provenance logs.

Architecturally, cleanagents typically comprise an agent registry or catalog, a task manager or workflow orchestrator, data connectors to sourcing and destination stores, an execution environment for isolated agents, and an observability layer. Pipelines may run in batch or streaming modes, and agents can be deployed locally, in containers, or serverless.

Applications include preprocessing for ETL pipelines, data lake or warehouse hygiene, and governance-heavy environments such as healthcare or finance where data quality is critical. By modularizing cleaning logic, organizations can iterate on rules and models without rewriting entire pipelines, while maintaining audit trails and rollback capabilities.

Although there is no single standard for cleanagents, the concept is widely discussed in data engineering and AI safety as a way to automate repetitive data quality tasks while preserving traceability and controllability. Implementations vary in language, plugin ecosystems, and integration patterns. See also data cleaning, data quality, data governance, and workflow automation.