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

Creating synthetic data for AI agents requires balancing two competing goals: strict privacy protection (ensuring zero PII or data leakage from production sources) and high realism/edge-case coverage (testing multi-turn failures, tool-use errors, and erratic user behavior).

The industry relies on a combination of enterprise AI platforms, open-source libraries, and agent-evaluation frameworks to accomplish this.

1. Enterprise & AI-Native Data Platforms (Privacy + Statistical Fidelity)

These platforms ingest production data, scrub or tokenize PII mathematically, and use generative models to synthesize new records that mirror complex data distributions and relational structures.

• • Gretel AI : Excellent for differential privacy guarantees. It uses deep learning and transformational models to generate privacy-compliant tabular, sequential, or text data while allowing you to inject specific constraints for edge cases.
• • MOSTLY AI : Specializes in high-fidelity synthetic tabular and relational data. It preserves statistical correlations and distributions from real-world user data while offering built-in privacy protection that satisfies GDPR and CCPA.
• • Tonic.ai (Fabricate): Great for maintaining referential integrity across entire enterprise databases. If your AI agent needs to query mock relational data or test database interactions, Tonic Fabricate creates realistic, safe downstream data environments.

2. LLM & Agent Evaluation Frameworks (Behavioral & Edge-Case Synthesis)

For AI agents, data isn't just tabular—it is conversational, multi-turn, and tool-dependent. These specialized frameworks generate synthetic agent trajectories, user personas, and adversarial prompts.

• • Evidently AI : Provides open-source tools to programmatically generate synthetic user inputs, test conversational edge cases (e.g., angry users, confusing multi-step requests), and build golden datasets for RAG and agent regression testing.
• • NVIDIA NeMo (Data Designer) : Built for domain-specific pipelines (like healthcare or finance), allowing teams to write structured constraints and prompts that generate realistic domain language and rare edge cases without risking raw logs.
• • Arize Phoenix : Focuses heavily on generating synthetic traces and bootstrap datasets for evaluating LLM agent behaviors and debugging complex tool executions.

3. Lightweight & Open-Source Utilities (Programmatic & Code-Driven)

If you need to generate structured data frames, mock JSON payloads, or simple API responses on the fly without heavy infrastructure:

• • Faker (Python/JS): The classic programmatic library for generating localized fake names, addresses, emails, and phone numbers to populate basic agent mock tools.
• • Mockaroo : A web-based mock data generator useful for quickly exporting custom CSV, JSON, or SQL structures matching specific edge-case field criteria.

To help narrow down the best choice, what kind of data structure does your agent consume most?

• Tabular/SQL databases (requiring referential integrity)
• Multi-turn chat logs/conversations (requiring user personas and tool calls)
• Unstructured text/documents (for RAG pipelines)

Tools commonly used to create realistic synthetic data for AI agents while protecting privacy include:

• Tonic.ai — Generates synthetic databases, text, and mock APIs; useful for agent training/evaluation because it preserves schemas, relationships, and realistic workflows while avoiding production records.
• NVIDIA NeMo Safe Synthesizer — Creates private synthetic datasets with techniques such as differential privacy and evaluates privacy/utility tradeoffs.
• Synthetic Data Vault (SDV) — A popular library for generating tabular, relational, and time-series synthetic data with constraints and quality checks.
• MOSTLY AI — Focused on privacy-preserving synthetic enterprise data generation, including structured datasets.
• Gretel — Generates synthetic text and structured data with privacy controls.

For agent-specific edge cases, look for features such as:

• synthetic users, conversations, tool calls, memories, and environments
• scenario generation for failures, ambiguity, and adversarial behavior
• privacy testing (membership inference, PII leakage checks)
• controllable rarity/long-tail case generation

For example, PrivacyAlign creates synthetic privacy-sensitive agent scenarios with fake profiles, tool trajectories, and memory stores to evaluate whether agents leak information.

A practical stack is often:

1. Schema/world generator → creates realistic entities, relationships, and workflows
2. Synthetic data engine → produces private records
3. Scenario generator → injects edge cases and adversarial situations
4. Agent evaluator → measures safety, correctness, and privacy leakage

Synthetic data should still be tested for re-identification risk and fidelity; “synthetic” alone does not guarantee privacy.

If you mean synthetic environments/data specifically for testing or training AI agents, I’d shortlist these:

What I'd choose

• Building an agent that operates on realistic business data: Tonic.ai. Its newer agentic synthesis workflow can generate relational databases, text, mock APIs and timelines, while explicitly targeting unusual scenarios and failures.
• Need formal privacy guarantees: look closely at SDV's differentially private synthesizers, rather than assuming that "synthetic" automatically means private.
• Building an agent privacy benchmark: PrivacyAlign and ToolPrivacyBench are especially relevant because they generate realistic scenarios where the trajectory of tool calls can leak information, not merely the final answer.
• Need a programmable/open ecosystem: SDV is a strong starting point.

The key distinction is that realistic synthetic data ≠ automatically private data. A good system should measure privacy leakage/re-identification risk in addition to statistical fidelity. For agent testing, I'd also require stateful scenarios, correlated records, rare failures, adversarial inputs, permission boundaries, and multi-tool trajectories rather than just generating random fake customer rows.

If you tell me whether you're building customer-service agents, coding agents, browser agents, healthcare agents, or tool-using enterprise agents, I can narrow this to the best 3 and compare their APIs, privacy guarantees, and eval capabilities.

Creating synthetic data for AI agents requires balancing three competing needs: absolute privacy (no leakage of sensitive training records), statistical fidelity (preserving real-world correlations), and edge-case injection (simulating rare, adversarial, or out-of-distribution scenarios that agents will inevitably face in production).

Several specialized tools and platforms excel at this, categorized by their primary strengths:

1. Enterprise Data & Database Synthesis (Structured & Relational)

• • Tonic Fabricate: Uses conversational AI data agents to build hyperrealistic, complex synthetic datasets, mock APIs, and unstructured documents (PDFs, DOCX) from scratch or based on production schemas while maintaining strict referential integrity. It features a built-in validation agent to refine outputs iteratively . Explore this via the Tonic Fabricate Platform.
• • MOSTLY AI: An industry leader in tabular and relational synthetic data generation. It offers a privacy-first approach with built-in differential privacy guarantees, ensuring that generated data mimics real distributions (including rare classes/fraud patterns) without exposing individual records. They provide an MOSTLY AI Open-Source SDK for local and programmatic generation.
• • Gretel.ai: Features advanced compound AI systems (like Gretel Navigator) that use specialized LLM agents to design data, inject custom constraints, simulate rare edge cases, and mathematically verify privacy and utility scores against raw data. You can start experimenting via the Gretel Console.

2. LLM Agent & Evaluation Datasets (Unstructured & Behavioral)

• • LangSmith (by LangChain): Enables developers to automatically generate synthetic test examples, fill dataset gaps, and build robust evaluation suites based on seed or "golden" examples from production traces. Check out tooling details on LangChain.
• • Arize Phoenix: Excellent for generating synthetic benchmark datasets and agent-specific edge cases (such as adversarial prompts, typos, and multi-step tasks) to rigorously test LLM evaluators and agent resilience. Explore the tutorials on the Arize Phoenix Documentation.

3. Perception, Vision & Spatial Agents (Robotics & Multimodal)

• • Synthesis AI: Combines generative AI with cinematic CGI to programmatically generate labeled 3D and visual data for human-centric AI, perception, and robotics without privacy violations. Learn more at Synthesis AI.
• • Datagen: Focuses on physically-based simulation and synthetic visual data generation to train and benchmark computer vision and edge-case perception models for autonomous systems.

To help narrow down the best fit, tell me:

• What type of data is your agent processing (tabular/SQL, unstructured text/documents , or multimodal/vision)?
• Are you looking to generate data from scratch or transform existing sensitive data safely?

If you mean tools for generating realistic synthetic environments/data to train or evaluate AI agents, rather than just generic fake rows, these are the strongest options I’d shortlist:

My pick for AI agents: Tonic

Tonic is unusually well aligned with agent evaluation, because it goes beyond generating independent records. Its Fabricate system can create relational databases, unstructured documents, mock APIs, personas, timelines and tasks, while preserving relationships between entities. Its documentation specifically describes generating environments for agent evals and reinforcement learning.

That matters for an agent because a useful test case isn't just:

"Create a customer with an invalid credit card." It can instead be:

Customer → has several orders → one order is disputed → support ticket references the order → payment API intermittently fails → customer sends contradictory information → agent must decide whether to refund. You can deliberately oversample rare failures and ambiguous situations while retaining ground truth about what the agent should have done. Tonic also supports mock APIs backed by the same generated records.

For privacy-sensitive text, Tonic Textual can detect PII and replace it with coherent synthetic identities, including maintaining consistency when the same person appears under different names or handles. Its 2026 PrivacyBench work is an example of deliberately constructing difficult PII cases without using real people's data.

If you're building it yourself

SDV is particularly attractive when you want maximum control. You can define schemas, relationships and generation constraints yourself, then add a scenario generator on top that explicitly creates:

• rare/long-tail events
• invalid inputs
• conflicting records
• temporal inconsistencies
• permission failures
• API outages/timeouts
• adversarial user behavior
• multi-turn conversations
• known-correct outcomes for evaluation

The important distinction is that synthetic ≠ automatically private. A good system should evaluate disclosure risk rather than merely assume that generated data is safe.

Bottom line: for a production-grade agent sandbox/evaluation environment, I'd start with Tonic; for a highly customizable engineering stack, SDV; and for large-scale statistical/ML datasets, Gretel or MOSTLY AI.

It can instead be:

Customer → has several orders → one order is disputed → support ticket references the order → payment API intermittently fails → customer sends contradictory information → agent must decide whether to refund. You can deliberately oversample rare failures and ambiguous situations while retaining ground truth about what the agent should have done. Tonic also supports mock APIs backed by the same generated records.

For privacy-sensitive text, Tonic Textual can detect PII and replace it with coherent synthetic identities, including maintaining consistency when the same person appears under different names or handles. Its 2026 PrivacyBench work is an example of deliberately constructing difficult PII cases without using real people's data.

If you're building it yourself

SDV is particularly attractive when you want maximum control. You can define schemas, relationships and generation constraints yourself, then add a scenario generator on top that explicitly creates:

Creating synthetic data for AI agents while balancing privacy and realistic edge cases requires tools capable of modeling complex tabular/relational patterns or prompting LLMs with strict semantic constraints.

1. Enterprise-Grade & Privacy-First Generators (Tabular & Structured Data)

These platforms excel at taking confidential data (like customer service logs or transaction histories), learning statistical distributions and relational constraints, and outputting privacy-compliant data devoid of real PII.

• Mostly AI : Specializes in deep learning-based synthetic data generation. It preserves complex statistical relationships, temporal dynamics, and cross-table linkages while offering strong mathematical privacy guarantees (differential privacy) to prevent data leakage.
• Gretel AI : Provides developer-focused APIs and open-source models for generating privacy-assured synthetic tabular, text, and time-series data. It features built-in differential privacy filters and privacy-loss metrics, ensuring that generated edge cases don't accidentally regurgitate training records.
• K2view Synthetic Data Generation : Integrates entity-centric data masking, cloning, and AI generation. It is particularly effective for multi-system agent workflows where data must maintain referential integrity across interconnected enterprise databases (e.g., Salesforce, Workday).

2. LLM-Specific & Agentic Evaluation Generators (Unstructured & Behavioral Data)

For building multi-turn agent environments, RAG pipelines, or stress-testing conversational agents against rare edge cases, framework-level synthetic data generators are ideal.

• NVIDIA NeMo Data Designer : Part of the NeMo microservices stack, this tool uses structured prompting and constraints combined with LLM-as-a-judge filtering. It allows you to synthesize domain-specific behavior (e.g., medical triage notes or financial queries) containing intricate edge cases without exposing live proprietary archives.
• Arize Phoenix (Synthetic Dataset Generation) : Excellent for agentic workflows. It programmatically generates happy paths, multi-turn conversational edge cases, and adversarial scenarios to test agent evaluators and guardrails.
• Evidently AI : Helps turn raw domain knowledge bases or restricted logs into safe, privacy-vetted test suites and evaluation sets covering corner cases.

3. Open-Source Statistical Libraries

If you prefer building a custom in-house pipeline where data never leaves your secure perimeter:

• Synthetic Data Vault (SDV) : A leading open-source library that uses statistical and deep-learning copulas (like Gaussian Copula) to model multi-table relational databases and generate realistic structural edge cases.

If you can share what domain your agents operate in (e.g., healthcare, fintech, customer support) and whether you are synthesizing tabular data or conversational trajectories , I can recommend the most cost-effective setup for your stack.

Creating synthetic data for AI agents requires balancing three competing needs: privacy compliance (zero exposure of real PII/PHI), structural integrity (maintaining relationships across multi-table or multi-system environments), and edge-case realism (injecting rare anomalies, failures, or complex operational deviations that agents need to handle).

The top tools and platforms designed to solve this span enterprise platforms, developer-first agentic solutions, and open-source ecosystems:

• 1. Tonic Fabricate (Tonic.ai ) — An AI-powered synthetic data agent platform that allows teams to generate complex, relational, and context-rich data (structured database schemas, semi-structured logs, or unstructured text documents like PDFs and invoices) using natural language prompts. It lets you explicitly instruct the agent to build edge cases or specific operational scenarios from scratch or modeled on existing systems.
• 1. K2view Synthetic Data Management (K2view ) — Combines GenAI, rules-based synthesis, and in-flight data masking. It is ideal for enterprise agents requiring cross-system entity awareness (e.g., pulling and safely blending CRM, billing, and order logs) while retaining strict referential integrity and compliance with regulations like GDPR and HIPAA.
• 1. MOSTLY AI (MOSTLY AI ) — Uses advanced tabular and relational generative AI to output high-fidelity synthetic datasets. It excels at maintaining statistical distributions while offering built-in privacy guarantees. It is widely used to oversample rare edge cases—such as fraud detection or rare customer service complaints—without leaking underlying real user profiles.
• 1. Gretel AI (Gretel AI ) — A developer-focused platform providing differential privacy guarantees alongside generative models. Gretel allows you to inject custom constraints and safety bounds to generate rare, synthetic edge cases safely for reinforcement learning and agent evaluation loops.
• 1. Faker & Mockaroo (Mockaroo ) — Lightweight, rule-bound tools ideal for simple mock data generation, though they require manual scripting or configuration to model complex dependencies and realistic agent edge cases compared to native GenAI solutions.

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

• Are your agents processing structured data (SQL/NoSQL), unstructured text/documents , or both?
• Do you need the tool to learn from existing production data or generate entirely from scratch?

I can recommend the most practical setup for your pipeline.

Yes. If you mean tools specifically useful for building/testing AI agents with private, production-realistic data and hard edge cases, the landscape in 2026 looks roughly like this:

My shortlist

For an agent development team, I'd look at three different categories rather than choosing purely on "synthetic data":

• Tonic — probably the most comprehensive choice if you need a whole synthetic world: customers, orders, messages, tickets, databases, APIs and timelines that remain internally consistent. Its newer agentic generation explicitly targets agent evals and RL environments.
• Plurai — particularly interesting if your main problem is "How do we expose our agent to the weird stuff real users will do?" It generates personas, artifacts and multi-turn scenarios and can mock the tools your agent uses.
• Gretel — a strong choice when the primary deliverable is a large synthetic training/evaluation corpus, including unstructured/LLM data, rather than a complete simulated environment.

One important distinction: synthetic data ≠ automatically private data. If a generator learns from sensitive production data, you still need to evaluate memorization/re-identification risk. Tonic, for example, distinguishes genuinely generated records from de-identification and offers multiple privacy techniques; the appropriate guarantee depends on how the dataset was produced.

There is also interesting research moving toward exactly what you're describing: state-grounded multi-agent generation, where a simulated user, agent, tool environment and evaluator all share an authoritative world state. That makes multi-turn agent trajectories substantially more realistic than independently generated conversations.

If your goal is specifically "generate private synthetic environments to red-team an agent," I'd rank Tonic + Plurai/Simthetic above generic synthetic-tabular tools.

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.