Using AI Agents to Generate Synthetic Data

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TL;DR: Genesis Data Agents automate the process of building realistic test data through a six-phase blueprint. The agent reads your business requirements, designs a schema with realistic data patterns, writes and tests the generation code, documents everything, and produces a structured handoff for the next pipeline phase. In a live asset management example, it completed the full workflow in 137 minutes and delivered a raw schema ready for bronze, silver, and gold medallion processing.

What Is Synthetic Data Generation?

Synthetic data plays a critical role in modern data environments. It enables teams to test pipelines, validate models, and experiment safely without exposing sensitive or regulated information. Genesis uses data agents to make the creation of high-quality synthetic data faster, repeatable, and governed.

Instead of manually crafting sample datasets or relying on brittle scripts, Genesis agents understand the structure and intent of your data. They generate synthetic datasets that preserve schema, relationships, and statistical characteristics, while removing the risk associated with real production data.

Because this work is handled by agents, synthetic data generation becomes part of a structured workflow rather than a one-off task. The agents document what they create, follow predefined standards, and can regenerate data consistently as requirements change.

This approach is especially valuable for testing, development, and validation workflows. Teams can spin up realistic datasets on demand, validate transformations across environments, and move faster without waiting on production access or anonymization processes.

Why It Matters

• Faster testing and development without using sensitive data
• Consistent synthetic datasets aligned with real schemas
• Repeatable workflows that reduce manual effort
• Safer experimentation across teams and environments

By using data agents to generate synthetic data, Genesis removes friction from one of the most time-consuming parts of the data lifecycle. Teams get realistic data when they need it, without compromising security, governance, or delivery speed.

A Real Example: Asset Management Data, Built for a Specific Dashboard

To see how this works concretely, consider a data engineering team that needs to build a dashboard for an asset management firm. The business questions driving that dashboard are specific: which funds are performing best, which are consistent across market cycles, how assets under management have grown over time, and how individual funds rank within their peer groups.

Before any of that analysis is possible, the team needs raw data that looks and behaves like real asset management data. Building that from scratch, with realistic distributions and meaningful patterns, is not trivial. If the data is too flat or too random, the downstream dashboard will be useless for development and testing.

Using Genesis, the engineer kicks off the synthetic data generation blueprint with two inputs: a list of the business questions the data needs to eventually answer, and a rough sketch of the target dashboard layout. That is the entire setup, and the agent takes it from there.

How the Synthetic Data Generation Blueprint Works

Genesis agents do not generate synthetic data in a single pass. The synthetic data generation blueprint follows six sequential phases, each with defined actions, context documents passed forward from the previous phase, and exit criteria the agent must satisfy before moving on. This is what keeps long-running autonomous work on track, the agent cannot skip ahead.

In the asset management example, the agent worked for 137 minutes, progressing through all six phases autonomously:

1. Context understanding. The agent reads the business questions and dashboard sketch, identifies the industry domain, and proposes an initial data model -- documenting the patterns the data should exhibit so the resulting dashboard shows meaningful variation rather than flat or unrealistic outputs.
2. Schema design. The agent proposes the tables, columns, and relationships needed to support the raw schema, with the downstream bronze, silver, and gold medallion structure already in mind.
3. Data generation logic. The agent writes Python programs, for example, generateDataClean.py -- to produce the synthetic records, accounting for special cases and edge conditions identified during planning.
4. Testing and validation. Generated data is tested against the schema design and the original business requirements before the agent proceeds.
5. Documentation. The agent produces written documentation and diagrams describing what was built, why, and how it connects to the broader pipeline. This is useful for engineers, downstream agents, and anyone auditing the work later.
6. Handoff. A structured handoff document is produced for the next phase, in this case source-to-target mapping for the medallion layers.

Genesis agents have access to over 100 tools throughout this process: writing files, executing code, running tests, creating diagrams. Engineers can also use the built-in replay capability to review exactly what the agent did, step by step, at any point during or after the mission.

What the Agent Actually Produces

At the end of the process, the team has a complete raw schema in Snowflake. In the asset management example, that included tables for trade activity, product data, and portfolio positions, structured and patterned to behave like real data from that type of company in that industry.

The final dashboard, built on top of the bronze, silver, and gold layers derived from this raw data, showed realistic fund performance patterns, rankings, and assets under management trends. Synthetic data that does not behave realistically is not useful for testing; it just produces false confidence. The point of the blueprint's planning phases is to prevent exactly that.

Where Synthetic Data Generation Fits in the Pipeline

Synthetic data generated by Genesis hands off directly to the next phase of a data engineering workflow. In a typical sequence:

1. Genesis generates the raw synthetic schema from natural language inputs and any reference documents or sketches provided.
2. A source-to-target mapping blueprint maps the raw schema to bronze, silver, and gold medallion layers.
3. A data engineering phase uses dbt, Snowpark, or Databricks to render the data into those layers within Snowflake. For a closer look at the dbt side of this, see AI Agent Builds dbt Analytics Schema in 30 Minutes.
4. Dashboards or query agents are built on top of the gold layer to answer the original business questions.

The synthetic data generation step is not a workaround or a placeholder. It is the foundation that makes the rest of the pipeline possible before production data is available, and because the agent documents everything it creates, each handoff is clean.

Genesis supports this same workflow on Databricks. For a look at how it runs in that environment, see How Genesis Automates Synthetic Data Generation for Databricks Dev Environments in Under 34 Minutes.

Why Traditional Approaches Fall Short

Manual synthetic data creation tends to produce one of two outcomes: data that is too simple to surface real problems, or data that took so long to build that the team cut corners somewhere else to compensate.

Scripted approaches help with repeatability but require maintenance as schemas evolve. Anonymized production data reduces risk but introduces compliance overhead and often requires a security review before it can be used in development or shared across environments.

Neither approach generates the documentation that makes synthetic data useful beyond a single engineer's local environment. The context problem in long-running data work compounds every time a dataset changes hands without proper documentation.

Genesis treats synthetic data generation as a first-class data engineering task: documented, versioned, reproducible, and tied to the business requirements it is meant to serve.

Frequently Asked Questions

What is synthetic data generation in Genesis? An automated, six-phase workflow in which a Genesis Data Agent creates a complete schema of realistic test data inside Snowflake or Databricks. The engineer provides requirements and any reference documents; the agent handles design, generation, validation, documentation, and handoff.

How long does it take? It depends on schema complexity. The asset management example completed in 137 minutes. The Databricks version of this workflow has run in under 34 minutes for simpler schemas.

How does it connect to the rest of the pipeline? The blueprint produces a raw schema and a structured handoff document that feeds directly into source-to-target mapping, then into bronze, silver, and gold medallion processing, and finally into dashboards or query agents.

What is a Genesis blueprint? A structured methodology that defines how a Genesis agent approaches a specific category of work, broken into sequential phases with defined actions, context, and exit criteria at each step. For a deeper look, see Blueprints: How We Teach Agents to Work the Way Data Engineers Do.