Synthetic data

Generate realistic, multi-agent synthetic trace datasets — from your own data, a built-in industry scenario, or a saved definition — in Stratix Premium.

Available in Stratix Premium. This surface is part of the logged-in workspace at stratix.layerlens.ai. Stratix Public users can browse the catalog but cannot use this feature.

Synthetic data generation creates realistic agent traces so you can build evaluation datasets without waiting to collect production traffic. Use it to:

• Bootstrap — get a full dataset when you have no traces yet
• Expand / vary — turn a handful of real traces into a larger, more diverse set
• Build golden datasets — domain-specific traces for testing judges, scorers, and evaluations

Start from Catalog → Datasets → New Dataset → Generate synthetic traces. The wizard is a two-step flow: pick a source, then configure & generate.

Step 1 — choose where the synthetic data is seeded from.

Step 1 — Choose a source

Start from existing data

Vary or expand traces and datasets you already have. Pick individual traces or a whole dataset as the seed; the generator produces synthetic variants that preserve intent while adding diversity. Best for golden datasets and for growing a small real set.

Seed from existing traces or a dataset.

Start from industry scenario

Pick an industry and a scenario from the built-in registry to generate domain-specific traces — 14 industries (healthcare, financial services, legal, insurance, retail, media, manufacturing, energy, real estate, travel, government, education, telecom, crypto) spanning 30 scenarios. Best when you have no traces yet or want coverage for a specific domain.

Pick an industry, then a scenario, each with a description and ground truth.

Start from saved definition

Re-run a previously saved generation definition with one click. A definition captures the source, shaping prompt, quality, count, and destination so a run is reproducible. (This card is disabled until you've saved at least one definition — see Save as a reusable definition.)

Step 2 — Configure & Generate

Configure the run and preview cost before generating.

• Shaping prompt (recommended) — free-text guidance that steers the run (e.g. "focus on cardiology cases involving patients over 65 with multiple comorbidities"). Up to 10,000 characters.
• Generation quality — a three-stop slider (see the table below).
• Number of traces — 1–10,000, with quick presets (1, 10, 25, 50, 75, 100, 1,000). Default 10.
• Destination — where the results land:

  • Create new dataset — a new dataset (name required).
  • Append to existing dataset — add to a dataset (schema must match).
  • Keep as traces — store loose in the Traces list, no dataset.
• Cost estimate — a live panel showing cost per trace, total credits (and ~USD), available credits, and estimated generation time; it updates as you change the count and warns before an expensive run.

Generation quality tiers

Tier	Model class	Quality floor	Use it for
Fastest	Fast (haiku-class)	0.60	High-volume runs where volume matters more than per-trace nuance; lowest cost.
Balanced (recommended)	Fast (sonnet-class)	0.75	Most runs — quality, cost, and speed all matter. The default.
Highest quality	Frontier (opus-class)	0.85	Production-grade datasets and sensitive evaluations where precision leads.

Cost scales with the tier (roughly 5× from Fastest to Highest per trace); the cost panel always shows the exact estimate before you commit.

Preview a test trace

Before launching the full run, click Generate test trace to produce a single preview scored on a four-part rubric — correctness, coverage, plausibility, safety (0–1 each) — so you can sanity-check quality and shaping before spending credits on the whole batch.

Save as a reusable definition

Toggle Save as reusable definition, give it a name, and the run's configuration is saved. It then appears under Start from saved definition for one-click reruns.

Generating

Clicking Generate enqueues an asynchronous job and opens a live progress screen (traces generated, credits consumed, estimated time remaining). Generation runs server-side — you can navigate away or close the page; Stratix emails you when it finishes.

• Cancel generation stops the run and preserves everything generated so far; the dataset is marked partial.
• On completion you land on a terminal screen: Open dataset (or View traces for a keep-as-traces run), or Generate more.

A finished generation job.

What you get

Generated traces are full, structured multi-agent traces — not flat text. They render in the trace inspector as an agent graph (distinct agents, hand-offs, tool calls) with a per-event waterfall, exactly like ingested production traces, and are immediately usable in evaluations.

A generated trace — a real multi-agent graph (4 agents, 18 events) with waterfall timing.

Cost & limits

• Generation consumes credits; the cost panel shows the estimate up front.
• A single job is capped at 1,000 credits; runs above ~100 credits show a soft warning.
• Trace count is capped at 10,000 per run.
• Submitting a generation job requires an Owner or Admin role in the organization.

Where to next

• Datasets — manage, version, and evaluate the results
• Trace evaluations — score a generated dataset
• Guide: Build a trace set for agentic evals