How Agentic Infrastructure is Revolutionizing Synthetic Data Generation and Structuring in 2025

In 2025, AI is moving fast, but it still hits a wall when it comes to data.

Real-world data is hard to find, expensive, and rooted in privacy regulations. That’s where synthetic data comes in. It’s artificially generated data that looks and behaves like the real data.

It fills gaps, protects privacy, and saves tons of time and money. But here’s the catch: traditional ways of creating synthetic data can be slow, rigid, and manual.

Solution?

Implementing an agentic infrastructure. It uses autonomous AI agents that plan, learn, and adapt on their own. These agents can generate synthetic data, structure it, improve it, and make sure it meets goals. All of this happens without constant human input.

In this blog, let’s explore

The limitations of traditional synthetic data workflows

Agentic infrastructure and how it benefits data workflows

Benefits of implementing agentic infra for synthetic data generation

What the future of synthetic data generation looks like.

Let’s go!

The Problem with Traditional Synthetic Data Workflows

About 57% of data scientists say that cleaning and organizing data is the most boring part of their job.

Most synthetic data generation today still relies on static, rule-based scripts or one-off machine learning models. These pipelines often use popular techniques like

GANs (Generative Adversarial Networks)

VAEs (Variational Autoencoders)

But while math is powerful, the process around them is far from flexible; it’s hectic and complex.

First, there’s a lot of manual work involved. Data engineers spend a lot of time

Setting up the data schema

Defining transformation rules

Fine-tuning model parameters

Performing post-generation validation.

Traditional ways of synthetic data generation are not plug-and-play. They are more like building a custom toolchain for every new use case. Even a small change in a target domain (like switching from banking transactions to insurance claims) can mean starting from scratch.

These traditional methods also struggle when data evolves

For example, if your downstream machine learning model needs new fields, updated formats, or better edge-case handling, most synthetic data generators can’t adjust automatically. You have to go back to the drawing board, tweak parameters manually, or write new scripts.

Scalability becomes a problem

If you want to expand from tabular data to time-series data or add synthetic logs for an LLM training pipeline, you will hit a roadblock. Now, you’ll need more engineers, new models, and additional validation logic.

Traditional pipelines don’t easily generalize across data types or domains without significant reengineering.

And then there’s quality control

How do you know if your synthetic data is good? Most traditional pipelines don’t include feedback loops. They generate data once and stop. Unless you manually inspect the outputs, run diagnostics, or compare downstream model performance, poor data can quietly make your data unusable for models.

While each of these processes has its own value, doing them manually wastes time and resources. This slows down model training. There’s a growing need for automation.

What Is Agentic Infrastructure?

93% of business leaders think companies that use AI agents well in the next year will get ahead of their competitors. (Source: Capgemini)

Agentic infrastructure flips the script on how synthetic data is created and managed.

Instead of relying on rigid scripts or static workflows, it uses a network of AI agents where each agent has a specialized role, like generating samples, validating quality, or adapting schemas. These agents continuously gather feedback, evaluate the usefulness of the data they generate, and improve their methods over time.

Unlike traditional pipelines, which follow fixed instructions, agentic systems adapt to context. For instance, if a downstream model struggles with rare events, an agent can detect that gap and generate new synthetic examples to fill it. Another agent might adjust data formats or balance class distributions. All this happens without human supervision.

Features of Agentic infrastructure in synthetic data generation:

Context awareness: Agents monitor logs, performance metrics, and usage patterns to understand what kind of synthetic data is most needed.

Autonomous decision-making: Agents act independently to update data generation strategies, select models, or fine-tune parameters.

Continuous learning: As they receive feedback from model performance or data validation layers, agents adjust their behavior to produce more relevant and higher-quality data.

Collaboration: Many AU agents can work at the same time. For example, one agent focuses on data structure while another focuses on privacy compliance.

In short, agentic infrastructure turns synthetic data generation into a living, self-improving ecosystem that is more responsive, scalable, and intelligent than ever before. Synthetic data generation platforms like Sycnora.ai make use of this infrastructure.

How Agentic Systems Improve Synthetic Data Generation

1. Adaptive Agents

These agents generate data, test how useful it is, and refine their approach. They use feedback from models or evaluation tools to make the next batch better. Over time, they learn to produce more realistic and useful examples.

2. Simulated Environments

Multi-agent simulations let you create synthetic datasets based on real-world interactions. You can simulate traffic, financial transactions, social behavior, and more. The result is data that reflects complex patterns that would be hard to model otherwise.

3. Cross-domain Collaboration

One agent generates text, another makes matching images, and a third agent stimulates sensor data for the same scenario at the same time. This is possible with agentic AI. These systems can coordinate these outputs so they align, creating rich, multi-modal datasets that work together.

4. End-to-end Pipelines

Instead of stitching together a bunch of tools, agentic infrastructure handles the entire synthetic data lifecycle. From ingesting raw inputs to validating final outputs, agents can automate and optimize every step.

5. Dynamic Structuring

Agents can automatically choose or change data formats depending on the use case. If a model performs poorly on certain inputs, agents can reformat the data or add new metadata. This keeps your synthetic data aligned with real needs.

What’s Next: Agentic AI + Synthetic Data Generation

Syncora.ai is a next-generation synthetic data platform that fully embraces agentic AI.

Instead of relying on rigid workflows, this synthetic data generation tool deploys AI agents to generate, structure, and continuously refine synthetic datasets. All this happens while protecting privacy and staying compliant with GDPR, HIPAA, and other norms.

These agents learn from feedback and adapt to changing model needs. Your data stays accurate, diverse, and production-ready. With built-in privacy controls and tokenized rewards for data contributors, Syncora.ai makes it easy to scale data generation fast and safely.

Try Syncora for free

A Smarter Data Ecosystem is The Future

As per a report, the global AI agents’ market is expected to grow from $5.29 billion today to $216.8 billion by 2035. That’s a massive jump, growing at around 40% every year.

Synthetic data is essential for the future of AI, but it’s agentic infrastructure that will make it fast, flexible, and scalable. Instead of manually curating and engineering data, we can build systems that do it for us.

These systems don’t just generate synthetic data; they understand the purpose behind it and adapt to meet that need. As more teams adopt agentic approaches, we’ll see AI models trained on smarter, more diverse, and more ethical datasets.