Synthetic Data Vs Agentic Synthetic Data: What Is the Difference? 

According to a survey by Blueprism in 2025: 

• 29% of organizations are already using agentic AI  

• 44% plan to adopt Agentic AI within the next year 

The numbers say it all. People want to use agentic AI, whether it’s for automation or other tasks. When the world of AI and data is considered, agentic synthetic data can be of help.  

Synthetic data is needed for creating artificial datasets that look and behave like real data.  

But now, newer systems called “agentic” synthetic data generation are taking the stage. These agent-based synthetic data generation tools not only generate synthetic data but also understand the context, learn from patterns, and autonomously refine the data to meet specific needs.  

In this blog, we will explain what agentic synthetic data is and how it differs from synthetic data. We will also see a comparison between agentic synthetic data vs synthetic data and see the approach of both for 2 different use cases.  

What Is Synthetic Data? 

Synthetic data is artificially generated data that acts as real-world data. The traditional way of creating it includes using software algorithms. You can also use simple statistical models or complex neural networks like GANs.  

These tools produce datasets with the same patterns and relationships as real data; but they do not expose any personal or sensitive details. This makes them useful for training AI, testing systems, and preserving privacy. 

What Is Agentic Synthetic Data? 

Agentic synthetic data takes the idea of generating synthetic data to the next level. Instead of just generating datasets, this approach uses autonomous agents (AI systems) that can make decisions, plan tasks, and learn from outcomes.  

While synthetic data can just give you a new data set, agentic synthetic data tools offer much more.  

• These agents can sense gaps in the data 

• They can decide what new samples are needed. 

• Based on the information, they can create new samples and test them  

• They can run this cycle repeatedly to generate new datasets for various scenarios.  

Agentic data generation tools like Syncora.ai are already doing these without constant human control.  

Comparison: Synthetic vs. Agentic Synthetic Data 

Feature	Synthetic Data	Agentic Synthetic Data
Creation Method	Fixed algorithms or generative models (GANs, VAEs)	Autonomous agents simulate, learn, plan, and iterate to generate data
Human Involvement	Manual setup and guidance	Minimal (agents decide what data is needed)
Adaptability	Can’t adjust once set (limited)	Self‑adjusting based on feedback and performance
Goal Orientation	Generates data based on static instructions	Agents pursue clear goals (e.g., fill data gaps, support a diagnosis model)
Feedback Loop	No ongoing evaluation	Continually tests and improves the data it creates
Handling Complex Scenarios	Can generate edge cases if specified, but needs manual work	Simulates complex interactions and rare events automatically
Privacy & Compliance Awareness	No intelligence; the risk depends on the setup	Agents can enforce ethical and privacy constraints during generation

Use Cases of Synthetic and Agentic Synthetic Data Generation 

Here are two simple examples showing how synthetic data and agentic synthetic data work in different scenarios. 

1. Healthcare  

Requirements: A hospital research team wants to train an AI model to detect early signs of a rare heart condition. 

Synthetic Data Generation Approach  Since real patient records are limited and protected under privacy laws, the team uses a generative model (like a GAN) to create 10,000 synthetic patient records. These records mimic the structure and patterns of real electronic health records like blood pressure readings, heart rate trends, family history, etc. However, they still need to manually check if these generated records cover all disease stages. Doctors and data scientists review them to ensure rare variations of the heart condition are included. If not, they go back, tweak the model, and regenerate the data.

Agentic Synthetic Data Generation Approach   An agentic AI system is given the goal: “Improve early detection for rare heart conditions.” The agent first analyzes the real data available and spots missing patterns. It autonomously generates synthetic patient records to fill this gap, using simulation and clinical logic. After creating these new samples, the agent immediately tests the model’s performance, sees where it still fails, and iterates by adding more edge cases (e.g., patients with comorbidities or unusual symptoms). All this happens without human intervention. The agent even ensures the synthetic data complies with medical privacy standards.

2. Automobile Industry 

Requirements: A self-driving car company needs nighttime driving images to train their AI for automated driving during nighttime.  

Synthetic Data Generation Approach  The team uses a generative model like a GAN to create 10,000 dark street scenes. But the team has to set up the inputs manually — like where to place cars or pedestrians. After generating the images, they check them to remove unrealistic ones, and then label each image with boxes around objects. This takes a lot of time and might still miss rare situations like a pedestrian crossing in heavy fog or sudden movements.

Agentic Synthetic Data Generation Approach   With agentic synthetic data, an intelligent agent simulates full driving environments on its own. It sets the lighting, weather, traffic, and pedestrian behavior without help. If it notices the car model performs poorly in foggy conditions, it creates new scenes focusing on fog and tricky pedestrian crossings. It automatically labels all objects and keeps testing the model after each round of new data.

In short, traditional synthetic data needs a lot of manual work and still has blind spots. On the other hand, agentic synthetic data adapts automatically, fills in the gaps, and keeps improving the model without human effort. 

Agentic Synthetic Data is The Future  

Traditional synthetic data generation relies on pre-set models and manual inputs. While it helps fill data gaps, it often needs human effort to set up, tune, and validate results. Agentic synthetic data employs AI agents that do all this without the need for human command.  

These systems don’t just follow instructions; they actively generate data by simulating environments, adjusting their outputs, and improving as they learn. They not only know what data you need but also figure out how to create it in the best way possible. 

Agentic models also adapt to privacy rules, making sure synthetic data doesn’t reveal sensitive info. They can simulate complex real-world situations, like traffic or financial markets, with multiple agents interacting naturally — something traditional methods struggle with. 

By being goal-driven, self-improving, and privacy-aware, agentic systems make synthetic data generation faster, safer, and more useful. 

In short, agentic behavior brings intelligence to synthetic data creation. And that makes it a game-changer for the future of AI and synthetic data. 

Agentic Synthetic Data Tool: Syncora.ai 

Syncora.ai is a synthetic data generation tool that uses agentic AI to make real, practical datasets that are as good as real datasets.  

• Syncora.ai’s AI agents structure your raw data, spot missing parts in the data landscape, and fill gaps — all with minimal setup. 

• Data is production-ready in minutes, cutting weeks of prep and 60% of costs. 

• Every dataset generated is logged on the blockchain and meets HIPAA, GDPR, and other privacy standards. 

• A built-in feedback loop reduces bias and boosts accuracy (up to 20% better in early tests). 

• Agents validate the data they generate, so accuracy improves cycle by cycle. 

If your team needs synthetic datasets beyond what traditional synthetic tools offer, Syncora.ai’s agentic platform is all you need.  

To Sum It Up 

While traditional synthetic data helps create useful training datasets, it still relies heavily on manual setup and static models. Moving to Agentic synthetic data, you can automate most of the work and get a high-quality, diverse dataset that is privacy-compliant. AI agents can understand the data needs, fill gaps, and adapt on their own. This makes the process faster, more accurate, and scalable. So, if you’re looking to future-proof your AI models, choosing an agentic synthetic data generation approach is the better choice.