Synthetic data for agentic workflows: A guide

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January 16, 2026

Agentic AI systems—autonomous agents that reason through problems and execute multi-step tasks—demand realistic, diverse test data to ensure that they’ll function reliably when put to work on real-world requests. The challenge is that agentic workflows are inherently complex: agents operate in loops where each action changes system state and affects subsequent decisions. Unlike traditional applications where you can test against production database snapshots, agents need scenarios that don't exist in your databases:

conversational flows where users change their minds mid-task
sequences of API calls where services fail then recover
edge cases where the agent must reason through ambiguous instructions or contradictory constraints

Real-world data rarely captures these multi-step failure modes in sufficient volume. Production logs show what happened when things worked, not the thousands of variations where an agent might break. You're left waiting for customers to uncover failure scenarios organically—meaning bugs surface in production, not during development.

Synthetic data solves this by letting you generate the exact scenarios your agents need to handle on demand, at whatever scale and complexity level you require. Create thousands of test variations and adversarial cases designed to break your agent before customers do. Rather than waiting for production traffic to reveal weaknesses, you proactively engineer the test data that prepares agents for real-world complexity.

What are agentic workflows?

Agentic workflows are AI-driven processes where autonomous agents—typically powered by large language models—execute multi-step tasks with minimal human intervention. Unlike traditional automation that follows predefined rules, agentic systems reason through problems dynamically, adapt when obstacles arise, and coordinate complex action sequences to achieve specific goals.

A well-known example of an agentic workflow is a customer support agent. One of these agents might receive tickets, analyze complaints, query internal APIs for account status, draft responses based on company policies, and either resolve issues or route them to human specialists. They rely on data at every decision point, such as:

training examples to parse support tickets accurately and generate responses matching your company's tone,
API response samples—both successful calls and failure modes—to handle real-world interactions,
edge-case scenarios for managing unexpected inputs, contradictory requests, and cascading failures.

Without representative datasets, these agents can fail subtly: misinterpreting user intent, breaking on unseen data formats, or making poor decisions because they've never practiced handling production failure modes. Acquiring these datasets through traditional means—waiting for production traffic, manually annotating examples, navigating privacy reviews—creates bottlenecks that delay releases.

The challenge of agentic AI training data

You may run into several significant bottlenecks in training your AI agents if you rely exclusively on real production data. These issues can create compounding delays.

Privacy laws and regulations

The EU General Data Protection Regulation (GDPR) and California's Consumer Privacy Act (CCPA/CPRA) impose strict controls on personal data use. Using customer support logs or internal communications for training or testing requires a documented legal basis, user consent in many cases, and comprehensive audit trails.

Beyond that, each experiment, architecture test, or dataset shared with contractors triggers another round of legal review. You must negotiate data processing agreements, validate transfer mechanisms, and document retention policies. For fast-moving AI projects where rapid iteration is essential, these approval cycles add weeks or months to timelines.

Risk of data leaks

Every copy of production data creates exposure, but these risks compound when it comes to autonomous agents:

They may execute real operations during testing like booking reservations, modifying customer records, or triggering payment transactions.
Their reasoning chains could expose proprietary logic like system prompts, decision trees, and tool-use patterns.
A single agent interaction might touch customer data, internal APIs, and third-party services, multiplying leak surfaces.
They require testing against hostile scenarios, which can't be safely executed against production systems.

Lack of existing data or insufficient diversity

Agentic systems need diverse scenarios to develop robust reasoning, but production logs rarely capture the full spectrum. If you're building a billing agent but 95% of support tickets are password resets, you have almost no examples of complex disputes or payment failures. Your agent will excel at the common cases but fail when customers present the nuanced problems it was meant to solve.

Production data also doesn’t necessarily define what a “correct” scenario looks like. Often, the action that should have been taken (or was taken) is subjective, which can be difficult for the agent to understand and replicate.

Synthetic data for agentic workflows: how it’s used

Synthetic data solves these problems by creating entirely new records from scratch or deriving examples that preserve production patterns while eliminating identifiable details.

Using synthetic data for agentic workflows addresses all three challenges simultaneously:

Data scarcity: Generate millions of examples when production contains hundreds, or create data for features before customers use them.
Security and compliance: Share datasets with external partners or offshore teams without data processing agreements or tracking sensitive information through approval workflows.
Cost and time efficiency: Skip manual collection, annotation, and legal reviews.
Complex requirements: Simulate multi-step API responses, error conditions, and edge cases that rarely appear in production.

By simulating both typical and edge-case scenarios, synthetic data ensures your agents see the full spectrum of inputs they might encounter.

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Using synthetic data in agentic AI workflows

Synthetic data simplifies and speeds your entire agent development lifecycle. From initial prototyping through production optimization, it solves different data challenges at each stage. Here's how to apply it strategically:

Development stage	What synthetic data provides	Key benefit
Training & fine-tuning	Labeled examples for domain-specific scenarios	Rapid iteration without privacy risk
Validation & testing	Adversarial test cases and boundary conditions	Catch failures before production
Performance benchmarking	High-volume stress test scenarios	Optimize latency and cost early
Continuous augmentation	Fresh data matching system evolution	Keep agents current as APIs change

Training and fine-tuning

The challenge: Your LLM hasn't seen your domain-specific language, error messages, or API patterns during pre-training. It needs examples showing how your company handles support tickets, interprets internal system responses, and structures multi-turn conversations.

The synthetic solution: Synthetic data for agentic workflows lets you create the exact training examples your agent needs, tailored to your specific domain. Generate labeled dialogue datasets demonstrating how your support team parses tickets, the language patterns customers use when describing problems, and the terminology your company uses in responses.

You can build comprehensive API training sets covering successful calls, error responses (400-series for bad requests, 500-series for server failures), timeout scenarios requiring retry logic, and edge cases like malformed JSON to ensure your agent understands not just the happy path, but the messy reality of production system behavior.

Validation and testing

The challenge: Organic production usage won't expose all your agents' weaknesses until customers encounter them. You need to stress-test against scenarios that may not occur for months or that you actively want to prevent from reaching production in the first place.

The synthetic solution: Build adversarial test suites designed to break your agent before customers do. Generate interaction sequences testing boundary conditions:

APIs that succeed initially, then fail mid-transaction, requiring rollback
users who change their minds mid-workflow, expecting the agent to adapt
contradictory instructions where earlier and later messages conflict
ambiguous inputs with multiple plausible interpretations
missing required fields that should trigger graceful errors, not crashes
rapid-fire sequential requests testing whether the agent maintains proper conversation context across turns

These adversarial test cases reveal failure modes that might take months to surface through organic production usage.

Performance benchmarking

The challenge: You don't know if your agent can handle production scale until production scale hits—and discovering performance problems under load means customer-facing degradation.

The synthetic solution: Generate millions of synthetic interactions mirroring production characteristics and run them through your agent pipeline. Create concurrent request streams, testing whether agents handle multiple parallel conversations without state leakage, maintain acceptable response times as load increases, and degrade gracefully when underlying services slow down.

When the run is complete, measure key metrics—median and 95th percentile latency, cost per interaction, error rates—using synthetic workloads that mirror your expected distribution of simple versus complex requests, typical conversation lengths, and frequency of expensive operations.

Continuous data augmentation

The challenge: Your systems evolve constantly—new API endpoints launch, error message formats change, business rules update, and payment methods get added. If your training data stays static, your agent's knowledge drifts out of sync with current reality, leading to confusion and failures.

The synthetic solution: Regenerate synthetic training data proactively whenever your systems change. When you add a new payment method, immediately generate thousands of synthetic transactions. When error message formats change, create fresh examples so agents learn the new patterns. This keeps your agent's training current without waiting for production traffic to accumulate organically in the new patterns, ensuring agents understand system changes before encountering them with real users.

Best practices for synthetic data in agentic AI workflows

To generate synthetic data for agentic workflows that genuinely improves agent robustness, follow these best practices.

Seed with reality

Start from real-world examples, when available. If you have a handful of sanitized support tickets, use them as templates. A real ticket like "I can't access my invoice from last month" becomes a seed for variations.

Feed this to an LLM with prompts like "Generate 500 variations, varying the specific problem (invoices, receipts, statements), time period (last week, Q2), and user tone (polite, frustrated, confused)." This produces examples anchored to actual user language patterns and common request structures.

Evolve complexity iteratively

Build synthetic datasets through layered iteration.

Start simple: "Generate 100 API request-response pairs for user authentication, covering successful logins."

Then add complexity: "Now generate 100 with authentication failures and proper error codes (401 for invalid credentials, 403 for locked accounts)."

Then: "Add examples where users retry after failure—some succeed, others fail again with different errors."

This evolutionary approach validates each complexity layer before adding the next, ensuring synthetic data remains realistic rather than devolving into nonsensical combinations.

Mock the complete environment

Agents invoke APIs, query databases, and call external services. Your synthetic data must model this complete operational environment. Generate API responses for every service your agent calls:

success payloads
error responses with proper codes
edge cases like empty results or unexpectedly large responses

Synthesize realistic timing behavior. Some requests complete in milliseconds, others take seconds, occasional calls timeout. Generate scenarios where services are temporarily unavailable (503 errors), intermittently flaky, or degraded (slow but eventually succeeding).

Inject realistic noise and ambiguity

Production users make typos, use ambiguous phrasing, change minds mid-conversation, and provide contradictory information. Your synthetic data must include this messiness.

Generate dialogues where intent is genuinely ambiguous, and agents must ask clarifying questions. When a user says "I can't get in," do they mean they forgot their password, their account is locked, or something else? Synthetic data for agentic workflows should include these scenarios alongside appropriate agent follow-ups.

Validate synthetic quality before training

Before using synthetic data for training, validate both statistical properties and practical utility. Run distribution comparisons between synthetic data and real production data. Do synthetic user messages have similar length distributions? Do error rates match production frequencies? Significant mismatches indicate synthetic data may not prepare agents for real conditions.

How Tonic.ai fuels agentic workflows

With Tonic.ai, you can use an AI agent to generate synthetic data for your AI agents.

To generate fully synthetic datasets, Tonic Fabricate's Data Agent enables you to chat your way to net-new synthetic data. Tell the agent what you need—schema structure, volumes, distributions, relationships, text files—and it leverages the vast domain expertise of LLMs paired with Tonic.ai's synthetic data generators under the hood to produce fully relational databases and unstructured datasets in minutes.

For sensitive unstructured text—support transcripts, internal documents, logs—Tonic Textual detects and redacts sensitive entities using proprietary Named Entity Recognition models, then optionally synthesizes realistic replacements. Textual's context-aware synthesis maintains document coherence and referential consistency, helping you build safe, domain-rich unstructured datasets without exposing real PII or PHI.

And for sensitive structured data, Tonic Structural securely and realistically de-identifies production databases at enterprise scale to give you sanitized data that looks, feels, and acts like production. By leveraging deterministic approaches and techniques like format-preserving encryption, Structural preserves referential integrity to maintain your data’s underlying business logic while fully removing sensitive information before it ever finds its way into an agentic workflow.

Support agentic AI workflows with synthetic data from Tonic.ai

Synthetic data for agentic workflows removes barriers that slow agentic AI development: privacy restrictions, data scarcity, and leak risks.

Ready to accelerate your agentic AI projects? Connect with us and see how easy it is to generate safe, high-quality synthetic data for every stage of your workflow.