What tools create synthetic data for agents while preserving privacy and realistic edge cases?

Creating synthetic data for AI agents requires balancing two distinct needs: privacy preservation (ensuring no real-world personally identifiable information or proprietary secrets leak into training/testing sets) and edge case generation (simulating adversarial, messy, or outlier inputs that agents inevitably face in production).

The tool ecosystem is split between statistical/tabular data engines (for structured data and database-backed agents) and LLM-native evaluation/scenario frameworks (for conversational and tool-using agent loops).

1. Statistical & Relational Synthetic Engines (Best for Privacy + Tabular Data)

If your agent interacts with enterprise backends, SQL databases, or customer tables, these tools guarantee mathematical privacy guarantees (like differential privacy) while retaining real-world relational integrity and distributions.

• Item A MOSTLY AI : A powerful platform for tabular, sequential, and relational data generation. It allows you to tune parameters like temperature and diversity to deliberately boost outliers and edge cases while mathematically stripping out real-world identifiers to comply with strict regulations like GDPR. You can explore their capabilities directly via MOSTLY AI.
• Item B Gretel.ai : Known for developer-first APIs and deep integrations (including Google Cloud BigQuery). Gretel excels at generating complex, multi-table relational databases while maintaining referential integrity. It features built-in privacy and accuracy reporting tools that score the dataset on privacy risks before your agent ever touches it. Check out the platform via Gretel AI.
• Item C K2view Synthetic Data Generation : Combines AI-driven generation with strict business rules and data masking. It's widely used for lower-environment testing where agents need relationship-preserving data that mimics production complexities without exposing live production data. Learn more on the K2view Solutions page.

2. LLM-Native & Agentic Test-Case Generators (Best for Conversational & Workflow Agents)

If your agent needs complex multi-turn dialogue histories, API call sequences, or stress-testing against adversarial prompts, LLM-based synthetic pipelines are required.

• Item A Confident AI (DeepEval) : Provides automated synthetic data pipelines designed explicitly for evaluating LLM applications. It can ingest a small seed document or domain description and generate thousands of diverse test cases, including complex edge cases and adversarial attacks, to benchmark agent performance. Explore the framework at Confident AI.
• Item B Arize Phoenix / SDGHub : Open-source and workflow-oriented tools that generate Q&A pairs, evaluation benchmarks, and agent-specific trajectories. They run local or API-compatible validation loops that critique and score generated synthetic data for faithfulness, diversity, and alignment before exporting it to agent development pipelines.
• Item C LangSmith (by LangChain) : While primarily an evaluation and tracing platform, LangSmith features robust dataset generation utilities that let you spin up synthetic user inputs and test scenarios based on historical production traces, effectively letting you simulate messy user behavior and edge cases at scale. Access it via LangChain LangSmith.

To help narrow down the ideal stack, tell me:

• What type of data does your agent consume most (e.g., unstructured text/conversations vs. structured SQL/tabular data)?
• Are you aiming to generate this data for initial fine-tuning/training or for post-deployment evaluation and safety testing?

If your goal is synthetic data specifically for AI agents, I’d look at tools that can generate not just realistic records, but multi-step scenarios, tool/API interactions, failures, and rare edge cases while keeping source data private.

Strong options

• Tonic.ai — Probably the closest fit for your description. Tonic Fabricate can generate relational data, unstructured text, mock APIs, personas, tasks, and agent-training/evaluation scenarios from scratch. It also supports production-derived data through its de-identification products. Tonic explicitly targets reinforcement learning and agentic workflows.
• Gretel — Good general-purpose choice for privacy-preserving synthetic datasets. It supports generation from real data and from scratch, with privacy and quality evaluation, and is particularly oriented toward AI/ML training and evaluation.
• MOSTLY AI — Strong for high-fidelity tabular and time-series data, especially when you want statistical relationships and privacy metrics. It's a good underlying data generator, although you'll likely need to build the agent scenarios/environment around it yourself.
• Synthetic Data Vault (SDV) — A good open-source option when you want control and are comfortable engineering the generation pipeline yourself. It's particularly useful for structured/relational datasets; you'd typically combine it with your own agent simulator and scenario generator.

The important distinction

For agents, "synthetic data" alone isn't enough. The really useful systems generate things like:

• normal user requests plus rare edge cases
• ambiguous or contradictory instructions
• multi-turn conversations where the user changes their mind
• API timeouts, malformed responses, 4xx/5xx errors
• partial failures and retries
• unusual database states
• adversarial inputs
• different personas and behavioral patterns
• ground-truth outcomes so you can automatically score whether the agent succeeded

That's why I'd put Tonic Fabricate at the top of the list for an agent-testing/training use case: it explicitly supports synthetic agent environments with tasks, personas, APIs, and verifiable outcomes rather than merely generating rows of fake customer data.

One caveat: "synthetic" doesn't automatically mean mathematically private. If a generator learns from sensitive production data, you still need to evaluate memorization/re-identification risk and understand its privacy guarantees. From-scratch generation has a fundamentally different privacy profile from synthesis based on real records.

My shortlist by use case: Agent training/evaluation → Tonic · General AI synthetic data → Gretel · High-fidelity tabular data → MOSTLY AI · Open-source/control → SDV.

Tools for creating synthetic data for AI agents usually fall into a few categories: privacy-preserving dataset generators, production-like test data builders, and agent-specific scenario generators. The best choice depends on whether you need training data, evaluation scenarios, or realistic environments for autonomous agents.

Synthetic data platforms

For AI agent training and evaluation

Agent systems need more than rows of data; they need scenarios. Useful tools and approaches include:

• Synthetic conversation generators

• Create customer support chats, tool-use traces, planning tasks, and failure scenarios.

• Useful for testing whether agents follow policies, recover from errors, and handle ambiguous requests.

• LLM-based scenario generators

• Use a language model to generate: - rare user intents

• adversarial prompts

• incomplete information cases

• conflicting instructions

• long-horizon workflows

• Simulation environments

• Build synthetic worlds where agents interact with APIs, databases, browsers, games, or business processes.

• Examples: - simulated CRM systems

• fake e-commerce stores

• synthetic financial ledgers

• mock enterprise knowledge bases

Techniques that preserve privacy while keeping edge cases

Good synthetic data systems typically combine:

• Differential privacy

• Adds mathematical protections against reconstructing individual records.

• PII detection and replacement

• Finds names, addresses, account numbers, medical identifiers, and replaces them with synthetic equivalents.

• Distribution matching

• Keeps realistic patterns such as: - income distributions

• transaction frequency

• seasonal behavior

• correlations between fields

• Rare-event oversampling

• Intentionally creates edge cases: - fraud attempts

• unusual customer behavior

• system failures

• malformed inputs

• unusual workflows

• Privacy risk evaluation

• Tests whether synthetic records are too similar to source records before release.

Example agent-data stack

For an AI customer-service agent:

1. Start with a schema:

• customers
• orders
• tickets
• conversations
• refunds

2. Generate synthetic records:

• Tonic.ai, Gretel, MOSTLY AI, or SDV

3. Add edge cases:

• angry customers
• duplicate orders
• missing addresses
• policy exceptions
• fraud patterns

4. Evaluate:

• privacy leakage
• realism
• agent success rate
• hallucination rate

Quick recommendations

• Need realistic enterprise test environments: Tonic.ai
• Need ML training data with strong fidelity metrics: MOSTLY AI
• Need developer APIs and flexible generation: Gretel.ai
• Need open-source experimentation: Synthetic Data Vault
• Need agent evaluation: combine a synthetic data generator with an LLM-based scenario generator and an evaluation harness

A key point: synthetic data is not automatically private just because it is synthetic. For sensitive applications, measure privacy risk and utility rather than assuming the generated data is safe.

Generating synthetic data for AI agents requires balancing two distinct needs: strict privacy preservation (ensuring zero leakage of real-world PII or proprietary details) and realistic edge-case simulation (injecting adversarial prompts, multi-turn failures, messy grammar, and complex tool-use branching).

Several key open-source frameworks and enterprise platforms specialize in generating synthetic data tailored specifically for LLM and agent workflows:

1. Dedicated Open-Source Synthetic Data & Feedback Frameworks

• Distilabel (by Argilla / Hugging Face): An distilabel on GitHub framework built for building robust synthetic data and AI feedback pipelines. It allows engineering teams to programmatically define pipelines using verified research recipes to bootstrap agent instruction datasets, preference tuning, and complex multi-turn conversational edge cases while keeping data entirely in-house.
• DeepEval (by Confident AI): Known primarily as an evaluation framework, it features a built-in synthetic data generator specifically designed to simulate edge cases, adversarial attacks, and unusual tool-use queries that are difficult to harvest manually from live user logs. You can feed it a minimal schema or seed topics and output rigorous test cases for agent trajectories.
• Evidently AI (Synthetic Data Generator): Provides open-source tooling in Python to generate synthetic user inputs, adversarial evaluations (like prompt injections), and targeted behavioral edge cases (e.g., handling hostile or confusing user trajectories) without relying on sensitive production logs.

2. Privacy-First Tabular & Text Synthesizers (For PII Scrubbing + Augmentation)

• MOSTLY AI: An industry-standard Mostly AI Synthetic Data SDK for generating highly accurate, privacy-compliant synthetic tabular and sequential text data. It employs deep learning and differential privacy principles to ensure that generated customer records, database entries, or transactional logs mathematically protect real individual identities while retaining exact statistical correlations and edge-case distributions.
• Google's DP Synthetic Text Framework: Built on inference-only differential privacy prediction models, Google's approach prompts off-the-shelf LLMs with parallel sensitive examples, aggregating outputs under rigorous mathematical privacy budgets to emit clean, non-attributable synthetic text logs.

3. Observability & Agent Evaluation Suites

• Arize Phoenix / OpenInference: Frequently used to ingest and structure synthetic datasets tailored to agentic systems. Phoenix helps visualize and stress-test where an agent's knowledge gaps or tool-calling failures lie by generating synthetic variations from a handful of few-shot production seeds.

How to Ensure Both Privacy & Realistic Edge Cases

When configuring these tools for an agent pipeline, best practices dictate a hybrid approach:

1. Isolate the Seed Data: Never pass raw, unmasked PII directly into a generator prompt. Strip or substitute identifiers using masking rules first.
2. Explicitly Prompt for Failure Modes: Instruct your generator pipeline (via frameworks like Distilabel or DeepEval ) to generate "failure-first" classes—focusing explicitly on malformed JSON tool calls, missing parameters, high latency, or contradictory multi-turn user intent.
3. Run Trajectory Validation: Test the generated synthetic sets against an LLM-as-a-judge or deterministic unit tests to ensure the edge cases mirror real friction points without inadvertently hallucinating structurally impossible scenarios.

If you'd like, let me know:

• What type of agent you are building (customer support, data analyst, code assistant, etc.)
• Whether you need the synthetic data for fine-tuning or eval/test benchmarking

I can recommend the most direct pipeline recipe to set this up.

If you're building AI agents, I'd prioritize tools that can generate not just plausible records, but multi-step scenarios, failures, adversarial inputs, and rare edge cases while giving you a measurable privacy story.

My picks by use case

• Agent QA / red-teaming: Tonic.ai. Its agentic-workflow tooling explicitly targets scenarios such as users changing their minds, API failures/recovery, contradictory instructions, and cascading failures.
• Agent evaluation before you have production traffic: Microsoft Foundry's synthetic evaluation datasets. It can generate cases from an agent definition, prompt, or reference document and is specifically intended to fill gaps in production traces.
• Privacy-sensitive training data: Gretel/NVIDIA or MOSTLY AI.
• Maximum control / open source: SDV, combined with your own scenario generator and privacy tests.
• Production-like staging/QA: Tonic Structural is particularly compelling because it preserves schema, relationships, and business logic while replacing sensitive information.

One important caveat

"Synthetic" does not automatically mean private. If a generator learns from real records, a poorly protected model can potentially reproduce information from its training data. Differential privacy provides a much stronger mathematical privacy framework, although it can involve a utility/fidelity trade-off.

For an agent, I'd use a hybrid dataset:

1. Real production traces → understand actual behavior.
2. Privacy-preserving/de-identified data → retain authentic distributions and obscure real identities.
3. Synthetic generation → create rare cases that production hasn't supplied.
4. Adversarial generation → intentionally create failures, ambiguity, tool errors, conflicting instructions, and unusual state combinations.
5. Automated evaluation → verify both agent behavior and privacy leakage.

That last distinction matters: recent research and industry guidance both point out that synthetic data can miss the unpredictability of real-world behavior, so synthetic cases shouldn't completely replace real traces when you have them.

If your goal is specifically "generate thousands of realistic agent trajectories with tool calls, failures, and adversarial edge cases", I'd narrow the shortlist considerably to Tonic + Microsoft Foundry + a custom simulator/SDV pipeline.

Here are top web results for exploring this topic:

Tonic.ai·https://www.tonic.ai Best Synthetic Data Tools Compared 2026 | Tonic.ai Best synthetic data generation tools and platforms for 2026. The tools below span a range of approaches, from open-source libraries and lightweight web generators to AI-powered enterprise platforms. T K2view·https://www.k2view.com Best synthetic data generation tools for 2026 - K2view Synthetic data generation tools create secure fake data that mirrors real data. The top 2025 tools are K2view, Gretel, MOSTLY AI, Syntho, YData, and Hazy. ... Gretel provides a synthetic data platform NVIDIA·https://www.nvidia.com**Synthetic Data** Generation for Agentic AI | Use Case - NVIDIA Why Create Synthetic Data? Training specialized agentic systems requires extensive, high-quality datasets that are often scarce, siloed, or sensitive. Synthetic data eliminates this bottleneck by crea Arize AI·https://arize.com**Generating Synthetic** Datasets for LLM Evaluators & Agents - Phoenix Synthetic datasets are a powerful way to test and refine your LLM applications, especially when real-world data is limited, sensitive, or hard to collect. By guiding the model to generate structured e Protegrity·https://www.protegrity.com**Privacy** Safe Synthetic Data for AI | Protegrity Create privacy safe synthetic data that mirrors real patterns without exposing sensitive records so teams can train and test AI, share safely and meet GDPR HIPAA. Linux Security·https://linuxsecurity.com Top Synthetic Data Generation Tools for AI and Testing in 2025 Top Synthetic Data Generation Tools for AI and Testing in 2025. Calendar Sep 19, 2025 • User Avatar MaK Ulac. 2 - 4 min read. 13.Lock StylizedMotherboard Esm H446. LinuxSecurity Advertiser. If your or Data Science Stack Exchange·https://datascience.stackexchange.com What is the best way for synthetic data generation while maintaining ...... real data. This is problematic as it is often possible to make inferences from aggregates of data or estimates thereof (this is the motivation for differential privacy in the first place), and par Tetrate·https://tetrate.io**Synthetic Data** Generation with LLMs: Techniques and Use Cases Synthetic data generation has emerged as a transformative approach to addressing one of machine learning's most persistent challenges: the scarcity of high-quality, diverse training data. Large Langua Evidently AI·https://www.evidentlyai.com Evidently 0.7.11: open-source synthetic data generation for LLM ...So how do you move forward? One way is to use synthetic data. Here, you prompt an LLM to create a set of inputs (and sometimes outputs) based on provided examples or context, and voila – here's your d AI Magazine·https://aimagazine.com Top 10: Synthetic Data Tools | AI Magazine 5. Gretel.ai. Headquarters: San Diego, US CEO: Ali Golshan Year Founded: 2019. Number of Employees: ~80. Credit: Gretel.ai. Gretel.ai offers developer-focused synthetic data tools for structured data

Creating synthetic data for AI agents requires balancing stochastic realism (mimicking authentic user behavior, complex database relations, and multi-turn nuances) with absolute privacy (preventing PII/PHI leakage) and targeted stress-testing (injecting adversarial edge cases).

The industry relies on a mix of enterprise data platforms, LLM-orchestrated generators, and open-source validation frameworks.

Enterprise & AI-Powered Platforms

• Tonic.ai (including Tonic Fabricate and Tonic Textual ) uses a conversational data agent approach. It lets you design or upload database schemas and text logs via chat, preserving complex foreign-key relationships while synthesizing context-rich unstructured data, documents, and transcripts free of real PII.
• MOSTLY AI specializes in high-fidelity tabular and text synthetic data generation backed by deep generative models and strong privacy guarantees (such as differential privacy). It allows you to oversample rare categories or simulate edge cases while maintaining the exact statistical distribution of the original data.
• Gretel.ai provides developer-focused privacy engineering tools and LLM-based APIs designed to synthesize complex structured and text-based datasets. It includes built-in privacy filters and differential privacy guarantees to score and verify that no real production records leak into the generated data.
• NVIDIA NeMo (via tools like NeMo Data Designer) enables the programmatic generation of domain-specific text and multi-turn operational logs (such as clinical or support records) paired with ground-truth labels using structured prompt constraints and LLM-as-a-judge quality filtering.

Evaluation & Open-Source Frameworks

• Arize Phoenix features specialized synthetic dataset generation workflows tailored for evaluating LLM applications and agents. It handles multi-turn user simulations, edge cases, and adversarial input generation to stress-test agent decision-making and tool-use accuracy.
• Synthetic Data Vault (SDV) is a widely used open-source Python library designed for modeling and generating synthetic tabular data, supporting complex multi-table relational databases while maintaining privacy and structural constraints.
• Evidently AI offers open-source utilities and Python recipes to systematically generate adversarial evaluations, unusual date/format boundaries, and tricky user intents specifically for testing agent robustness before deployment.

If you share what type of agent you are building (e.g., customer support chatbot, financial fraud investigator, healthcare assistant) and what kind of data it handles (tabular databases vs. unstructured conversational logs), I can recommend the most fitting tool and a workflow to target your specific edge cases.

If you're building AI agents and need synthetic data that is both privacy-safe and good at exposing unusual failures, these are the strongest options I’d look at:

My pick for agent development

Tonic.ai is probably the closest match to your wording. Its Fabricate product can generate relational data, unstructured text, mock APIs, personas and tasks, while its agentic-workflow guidance specifically recommends generating cases such as:

• API failures halfway through a transaction
• contradictory or changing user instructions
• ambiguous requests
• missing fields
• malformed JSON
• retries/timeouts
• rapid sequential requests that stress conversational state

That combination is particularly useful because simply generating statistically realistic customer records doesn't necessarily produce realistic agent failures.

For unstructured conversations, Tonic Textual can detect sensitive entities and replace them with coherent synthetic values while preserving context and consistency.

Gretel is a strong alternative if your priority is a general-purpose synthetic-data platform, especially if you want to generate large quantities of data or run generation within your own environment.

One important caveat: “synthetic” does not automatically mean zero privacy risk. You should evaluate re-identification/memorization risk, especially for rare records and highly sensitive datasets, rather than relying solely on a vendor's synthetic-data claim. MOSTLY AI, for example, explicitly addresses rare categories and extreme values because those can create privacy risks.

If your goal is specifically training/evaluating autonomous agents, I'd rank them Tonic > Gretel > MOSTLY AI; for high-fidelity database test data, the ranking is closer to Tonic ≈ MOSTLY AI > Gretel.

Tools for creating synthetic data for AI agents usually fall into a few categories: privacy-preserving data generators, agent evaluation/simulation platforms, and test-data systems. The best choice depends on whether you need training data, RAG/evaluation scenarios, sandbox environments, or software testing.

For AI agents specifically, look for these capabilities

1. Scenario generation

• Creates realistic conversations, workflows, documents, tool calls, and failures.
• Important for testing agents against unusual situations.

2. Long-tail edge cases Examples:

• contradictory customer requests
• incomplete records
• malicious instructions
• rare business exceptions
• unusual user behavior

Some platforms explicitly target rare-event and stress testing rather than only producing average-looking data.

3. Privacy controls Useful mechanisms include:

• differential privacy
• PII/PHI removal
• membership-inference testing
• no one-to-one record reconstruction
• private/on-prem deployment

Differential privacy and privacy evaluation are increasingly common approaches for synthetic data systems.

4. Agent evaluation formats For agents, the most useful synthetic datasets are often not simple tables. They include:

• user intents and variations
• tool-call traces
• multi-step tasks
• failure states
• adversarial prompts
• expected outcomes

A practical stack

• RAG/chat agents: synthetic conversations + documents + evaluation cases → Gretel, Tonic, Pleias-style approaches
• Enterprise copilots: synthetic employee/customer workflows → Datomime, Delphix, Tonic
• ML agents with structured data: tabular synthesis → MOSTLY AI, NVIDIA NeMo
• Software-testing agents: realistic databases and edge scenarios → Tonic, Delphix
• Safety testing: adversarial and rare scenarios → specialized simulation tools

A key point: the most useful synthetic data is rarely just “fake records.” For agents, it is usually a synthetic environment containing realistic context, actions, mistakes, and edge cases that let you measure whether the agent behaves safely and correctly.

Creating synthetic data for AI agents—especially when requiring strict privacy preservation and the injection of realistic or adversarial edge cases—relies on a mix of LLM-driven generation frameworks, differential privacy engines, and specialized simulation suites.

The primary tools and platforms categorized by their approach include:

• Tonic Fabricate provides an AI-powered synthetic data platform—featuring the Fabricate Data Agent—that allows developers to converse in natural language to generate complex, hyper-realistic, and relational structured/unstructured datasets from scratch while shielding real data.
• Gretel.ai specializes in developer-focused privacy engineering, offering advanced synthetic data generation for structured tabular data, time-series, and text using differential privacy guarantees to ensure zero leakage of original training inputs.
• MOSTLY AI automates high-fidelity synthetic data generation for both structured and text data, allowing teams to oversample rare classes, simulate complex behavioral edge cases, and completely de-identify production records under regulations like GDPR.
• NVIDIA NeMo Data Designer (part of the NeMo Microservices ecosystem) utilizes structured domain constraints and LLMs to generate realistic text data (such as triage notes or logs) paired with ground-truth labels and targeted edge cases, minimizing real-world exposure.
• Synthetic Data Vault (SDV) is a prominent open-source Python library designed for data scientists to model tabular data distributions using deep learning and statistical methods, enabling privacy-safe data augmentation.
• K2view delivers enterprise-grade synthetic test data management that preserves complex referential integrity across business entities, making it heavily applicable for real-time agentic system testing.

If you'd like to narrow this down, tell me:

• What type of data are you feeding your agent? (tabular, conversational text, database schemas, or multi-modal/visual)
• Are you focusing more on privacy compliance (like GDPR/HIPAA) or stress-testing edge cases/adversarial inputs?

I can recommend the most direct tool setup for your workflow.