LLM Agents for Data Cleaning

Understanding LLM Agents for Data Cleaning

Large language models (LLMs) like GPT-4, Claude, and PaLM2 have moved beyond text generation. When orchestrated as agents—self-directed processes with memory, a goal, and access to external tools—they can autonomously profile datasets, detect anomalies, propose fixes, and even rewrite transformation code. This article explores how LLM agents improve data cleaning workflows, best practices for production use, and common pitfalls to avoid.

Why Traditional Data Cleaning Falls Short

Manual data cleaning is tedious, error-prone, and poorly documented. Rule-based approaches in SQL or Python can’t easily adapt to schema changes or novel edge cases. LLM agents bring flexible, context-aware reasoning: they can read free-text documentation, infer semantics from column names, and generate code to fix issues—all while explaining their reasoning in natural language.

Core Components of an LLM Cleaning Agent

1. Goal Definition

The agent receives a high-level goal such as “standardize date formats in orders.csv and remove duplicates.”

2. Memory & Context

State is stored in a vector database or JSON file so the agent can recall previous steps, intermediate results, and schema histories.

3. Toolset

• Execution tools: Python REPL, SQL engines, or dbt commands.
• Inspection tools: Pandas profiling, Great Expectations, or custom SQL queries.
• Documentation tools: Ability to read data dictionaries or Jira tickets.

4. Planning & Reflection Loop

The agent decomposes the goal into sub-tasks, executes them, evaluates results, and iterates until success criteria are met.

Practical End-to-End Example

Below is a condensed run cycle of an LLM agent cleaning a customer table:

# 1. Profile the table
desc = agent.run("Profile CUSTOMER table for nulls, outliers, and datatype mismatches")
# 2. Generate hypotheses about issues
plan = agent.run(f"Generate cleaning plan based on: {desc}")
# 3. Execute fixes in SQL
agent.run(plan)
# 4. Validate and document
agent.run("Create Great Expectations suite and attach summary comments")

The agent can be prompted to output both the raw SQL it executed and plain-English justifications, creating a living audit trail.

When to Use LLM Agents

• Exploratory data analysis on new sources
• One-off migrations where rule writing overhead is high
• Incremental pipelines that face frequent schema drift
• Generating first drafts of dbt models or Great Expectations rules

For stable, regulated pipelines, fully autonomous agents may be excessive—humans in the loop remain essential.

Best Practices for Production

Guardrails and Evaluations

Combine LLM reasoning with deterministic checks. For example, after the agent fixes data types, run unit tests that assert no numeric field contains alphabetic characters.

Human-in-the-Loop (HITL)

Require approval for destructive operations (e.g., DELETE) and have the agent propose a pull request rather than directly modifying production tables.

Prompt Engineering Patterns

• System prompts that define role: “You are a senior data engineer following best practices in SQL and dbt.”
• Chain-of-thought prompts to force the agent to reason step-by-step.
• Code-and-explain prompts: “Write the SQL, then explain why it is safe.”

Telemetry and Feedback

Log prompts, responses, and execution metrics to continuously fine-tune the model and improve agent reliability.

Common Misconceptions

“LLM agents will replace data engineers.” In reality, agents handle repetitive cleansing while humans handle edge-case logic, governance, and architecture.

“Accuracy is guaranteed because the model is large.” LLMs hallucinate; always add validation layers.

“Agents can clean any dataset out of the box.” Business context still matters—models must be primed with domain knowledge.

Galaxy and LLM-Driven Cleaning

While Galaxy is primarily a modern SQL editor, its context-aware AI copilot can serve as a lightweight cleaning agent in interactive mode. Users can:

• Highlight a dirty column and ask the copilot to generate an UPDATE statement to standardize values.
• Automatically document transformations in Collections so teams can endorse vetted cleaning queries.
• Chat directly with the database schema to detect null spikes after a deployment.

This bridges ad-hoc agent-like reasoning with traditional SQL workflows.

Next Steps

1. Experiment with open-source frameworks like LangChain Agents and LlamaIndex.
2. Layer Great Expectations or dbt tests for validation.
3. Instrument feedback loops to steadily promote autonomy.