Production data is often off-limits—locked behind privacy regulations, access controls, or simply nonexistent when you're building new features. Yet you still need realistic datasets for development, model training, and demos that behave like the real thing.
Synthetic data generation via agentic AI solves this by combining large language models with autonomous planning to create high-fidelity, privacy-preserving records on demand. Instead of writing scripts or waiting for sanitized production snapshots, you describe what you need in natural language and let a Data Agent handle schema design, relationships, and statistical distributions.
This approach accelerates:
- Greenfield product development – build features before production data exists
- Agentic workflow development – test AI agents on realistic scenarios
- Model training and testing – oversample rare cases or balance datasets
- Sales demos and customer onboarding – showcase capabilities without privacy risk
You iterate rapidly, generate data at any scale, and never expose real customer information in non-production environments.
What is synthetic data?
Synthetic data mimics real-world datasets without containing actual user information. Organizations use it when production data is inaccessible, too sensitive to share, or insufficient for specific use cases—like training fraud detection models when you only have a handful of fraud examples, or provisioning realistic test databases before your application has real users.
The key to generating good synthetic data is preserving what matters: schema structure, statistical distributions, and relationships between tables. This lets you test code against production-realistic scenarios and train AI models on representative data without exposing personal details through outputs, logs, or accidental leaks.
What is agentic AI?
Agentic AI describes systems built on large language models (LLMs) or similar frameworks that set goals, plan steps, and interact through a conversational interface. Unlike simple script-based generators, agentic systems handle context, memory, and feedback loops autonomously. You give the agent high-level requirements, and it executes a data-generation plan.
In synthetic data generation, an AI agent combines the domain expertise of LLMs with specialized data generators. It infers value ranges, relationship constraints, and realistic distributions—all without manual script writing. As you chat, the agent remembers prior instructions and refines outputs, so you iterate quickly until the dataset fits your needs.
Implementation blueprint for generating synthetic data with agentic AI
Here's a step-by-step guide to building synthetic datasets with Tonic Fabricate's Data Agent. You'll see how simple it is to use agentic AI to automate the prompt-generate-feedback-export cycle.
Step 1: Describe your ideal dataset to the Data Agent or upload a schema via the chat UI
Be as specific or as generic as you like. You can say, "Generate 100k customer records with 10% premium tier, purchase history fields, and realistic timestamps," or simply upload a JSON schema. If you want to be more granular, you can dictate table names, column types, relationships, volume targets, and any distribution constraints—or just answer Fabricate’s questions when it prompts you for more details. Fabricate interprets your intent and translates it into a working data model.
Step 2: Watch the Agent generate your data on the fly
Fabricate’s Data Agent uses LLM-driven inference to fill in value ranges and structural links. You'll see sample rows streamed in real time, reflecting your schema. Behind the scenes, Fabricate combines its statistical generators and schema-aware engines to preserve referential integrity across related tables.
Step 3: Provide feedback to the Agent to fine-tune the results
If you need more rare events, location-specific address formats, or specific edge-case patterns, tell the agent: "Increase wire-transfer frequency to 5%" or "Use UK postcode style." The agent remembers context from previous messages and applies changes globally across tables. This conversational loop replaces hours of manual script adjustments.
Step 4: Export datasets as SQL, JSON, PDF, DOCX, PPTX, any text file type, and more
When the data meets your criteria, select the export format. If you want to create files of unstructured data based on the structured dataset you’ve generated in Fabricate, simply ask the Agent to generate what you need, like, “Create PDF receipts for the first 100 customer transactions.” Fabricate packages the data in ready-to-load files or API endpoints. No manual scripts, no provisioning delays—just download or integrate through CI/CD. You get production-realistic test data in minutes instead of days.
This blueprint turns synthetic data generation into a conversational workflow, freeing you to focus on development instead of boilerplate scripts.
Best practices for synthetic data via agentic AI
To make the most of agentic workflows for generating synthetic data, including Fabricate’s Data Agent, follow these guidelines:
- Start with a schema, if you have one: Clarify table definitions, data types, and foreign keys upfront so the agent enforces structural integrity that meets your needs. Uploading an existing schema accelerates generation and reduces the number of feedback iterations required to reach a fit-for-purpose dataset.
- Provide adequate context for your needs: Detail target volumes, distribution skews, and edge cases in your initial prompt to minimize iterations. The more specific you are about rare events, categorical splits, or time-series patterns, the closer the first draft will be to your requirements.
- Equip the agent with the tools it needs: Upload any custom generators or domain-specific value lists, and ensure the agent runs on an LLM tuned for your data domain. Fabricate's Data Agent is equipped with proprietary anti-assumption protocols and Tonic.ai's industry-leading synthetic data generators to achieve unprecedented realism and steer clear of risky hallucinations.
- Store context and memory from agent conversations: Enable conversation history or session memory so the agent retains prior instructions across prompts. This prevents the agent from reverting to earlier configurations and ensures that every refinement builds on what came before.
Optimize synthetic data via agentic AI with Tonic.ai
Tonic Fabricate's Data Agent brings agentic AI synthetic data generation into your toolkit with minimal friction. You chat to describe your ideal dataset, watch it build in real time, iterate on the fly, and export in minutes. This approach unblocks product development, AI model training, and testing without risking privacy leakage or compliance debt.
With Tonic Fabricate, you get:
- Schema-first, from-scratch synthetic data generation – Upload your schema and watch Fabricate fill it with hyper-realistic synthetic data in under five minutes.
- Realistic, relationship-preserving records – Fabricate mimics foreign-key relationships and produces databases that are referentially intact across tables.
- Chat-based iteration powered by a domain-expert agent – The agent remembers context from previous messages and applies changes globally across tables.
- Multi-format exports and CI/CD integration – Export datasets as SQL, JSON, PDF, DOCX, PPTX, any text file type, and more.
Innovation is no longer blocked by a lack of access to quality data. Let the Data Agent synthesize, so you can let the builders build.
Ready to see it in action? Create your free account or connect with our team to see how Fabricate's Data Agent transforms synthetic data workflows into an agentic, conversational process.
