Author: Ajinkya Balapure

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)  

• LLM  

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.  

Data is the goldmine for AI models, and synthetic data is the key that opens it — safely, quickly, and at scale.  

Synthetic data is privacy-safe, scalable, and increasingly used to train machine learning models without exposing real user information. But here’s the catch: even synthetic data needs to be trusted.  

How do you know if synthetic data: 

• Was generated correctly? 

• Is privacy-safe? 

• Can be proven where it came from? 

To answer this, blockchain enters the picture.  

No, blockchain is not only about crypto and mining, but rather it holds a true value: transparency and security. By combining synthetic data generation with blockchain, we get a powerful foundation for trust, transparency, and automation in synthetic data workflows. 

In this blog, let’s talk about: 

• Why synthetic data needs a trust layer 

• How blockchain brings transparency, traceability, and fairness 

• How synthetic data generation tools like Syncora.aiuse the Solana blockchain to make all this happen in the real world 
   

Let’s start at the root of the problem. 

The “Trust Gap” in Synthetic Data Generation  

Synthetic data is fake data, but in a good way. It mimics real data so it can be used to train AI models, without containing any actual personal or sensitive information. 

But with traditional synthetic data tools, there’s a trust gap. You’re never fully sure how the data was generated, what logic was used, or whether it still carries hidden risks. Most tools operate like black boxes, offering little or no transparency or traceability. That makes it hard for teams to confidently use the data in high-stakes environments like healthcare or finance. 

There’s another problem with this. When synthetic data is bought, sold, or shared, people still ask: 

• How was this data created? 

• Can I trust its quality? 

• Is it really privacy-compliant? 

• Who owns it? 
   

If you’re a data scientist, a compliance officer, or even a contributor sharing data, trust is everything. But with traditional systems, this trust is often based on promises and paperwork, not provable facts. That’s where blockchain makes a big difference. 

Blockchain in Synthetic Data Generation 

Blockchain is a transparent, tamper-proof ledger that records every action permanently. In synthetic data generation, this means every transformation, privacy step, and data output can be verified and traced. Here’s how it helps synthetic data workflows: 

1. Transparency 

With blockchain, every step, whether it’s generating synthetic data, validating it, or licensing it, is recorded on a public ledger. That means anyone, from developers to regulators, can independently verify what happened and when.  

Blockchain ensures that there are no hidden processes or missing logs. During synthetic data generation, it gives a clear and open trail of actions that anyone can trust and audit. 

2. Auditability 

Blockchain creates a tamper-proof, timestamped audit trail. You can trace every synthetic dataset’s life cycle from the past to the present. This includes raw data ingestion to how it was anonymized, validated, and eventually licensed or shared.  

The blockchain provides complete visibility for enterprises and regulators. This helps prove compliance and reduce legal risks. 

3. Decentralized Validation 

One of the best things about blockchain is decentralization — and it can be applied to synthetic data generation! Instead of relying on a single party to review data, blockchain enables peer review.  

In this scenario, subject-matter experts or approved validators can assess the quality of synthetic datasets, and their reviews are transparently recorded. This crowdsourced feedback ensures data is trustworthy and accurate, with no hidden manipulation. 

4. Smart Contracts for Licensing 

Smart contracts are automated agreements on the blockchain. They can handle dataset licensing, payments, and permissions without the need for legal paperwork or manual intervention.  

Everything runs instantly, securely, and with predefined rules. This saves time and ensures fair usage terms. 

Syncora.ai: Where Blockchain Meets Synthetic Data 

Syncora.ai  is a platform that combines agentic synthetic data generation with the Solana blockchain to create a decentralized, transparent data marketplace.  

Why Solana? 

• High throughput: Can handle thousands of transactions per second 

• Low fees: Makes microtransactions (like per-dataset licensing) feasible 

• Fast finality: No lag between licensing and access 

• Scalable ecosystem: Easily integrates with other Solana-based tools and wallets 

With Solana, it becomes practical to log every action on-chain (whether small or big).  Here’s how Syncora.ai uses blockchain in synthetic data generation. 

1. Every Step is Logged On-chain 

From the moment you feed raw data into the system, Syncora.ai’s AI agents go to work. They  

• Structure the data 

• Apply privacy transformations 

• Generate synthetic records 

• Run validations 

Now, each of these steps is logged on the Solana blockchain. That means: 

• Contributors can prove how their data was used 

• Consumers can trace a dataset’s origins 

• Regulators can verify compliance with privacy laws 

Blockchain ensures traceability & transparency at every step.  

2. Smart Contracts Handle Licensing 

Traditionally, data licensing involves NDAs, legal teams, and a lot of communication back and forth. With Syncora.ai , this is replaced by ephemeral smart contracts. 

Here’s how it works: 

• A buyer picks a synthetic dataset from Syncora.ai’s marketplace 

• A smart contract checks if they have enough $SYNKO tokens (Syncora.ai’s utility token) 

• The contract automatically splits the payment between the dataset contributor, validators, and the platform in real time. 

• The contract then issues a cryptographic license proof and logs the transaction permanently on-chain. 

•  Ephemeral smart contracting happens in seconds and saves time as opposed to traditional methods of licensing.  

3. Validators Keep Data Honest 

Just like how online platforms rely on user reviews, the synthetic data uploaded in Syncora.ai’s marketplace relies on peer validators. This is to ensure data quality and fairness. 

Here, validators are domain experts (like healthcare or finance analysts) who: 

• Review samples of synthetic data 

• Run statistical checks 

• Rate quality and flag issues 
   

Their reviews are recorded on-chain, so they’re public and verifiable.  This builds a reputation system where high-quality datasets and validators rise to the top.  

Validators also stake $SYNKO tokens, which they can lose if they validate low-quality data dishonestly. That keeps everyone accountable. 

4. Transparent Token Rewards 

By using blockchain in Syncora.ai’s ecosystem, data contributors and validators can earn tokens every time their work is used or validated. 

For example: 

• Alyssa uploads transaction logs → synthetic dataset is generated → someone licenses it → Alyssa earns $SYNKO. 

• Bryan validates a medical dataset → it gets approved → Bryan earns a reward from the validator pool. 
   

These payments happen automatically via smart contracts, and there are no delays or middlemen. And the entire token flow is visible in Solana’s ledger. 

5. Compliance, Baked In 

As per a report, over 80% of GDPR fines in 2024 were due to insufficient security measures leading to data leaks. 

Privacy laws like GDPR, HIPAA, and others are strict and demand proof. You can’t just say “we anonymized this” or “we followed policy.” You need evidence. 

With blockchain, Syncora.ai makes this a reality: 

• Immutable logs of every privacy transformation 

• Proof that no raw data ever left secure environments 

• Auditable validation and licensing records 
   
   

To Sum This Up 

Synthetic data is one of the most promising solutions for privacy-safe AI training. But to truly scale its use across industries, countries, and ecosystems, we need more than just good algorithms. We need trust, traceability, and transparency. That’s what blockchain brings to the table, and platforms like Syncora.ai are leading the way. They are combining AI agents with blockchain-backed infrastructure to deliver privacy-safe, auditable, and incentivized synthetic data at scale.  

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.