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Seed data populates simulation environments with realistic records before the agent runs. There are two layers of seeding:

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Baseline Seed Data

When an environment starts (simlab env up), each tool server is seeded with baseline data — the standing records that exist independent of any task. For example, an HR environment might include:
  • Employee records in HRIS
  • Company policies and org charts
  • Existing calendar events and email threads
Baseline seed data is defined by the scenario template and runs automatically via each tool’s seed services.

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Task-Specific Seed Data

Each task injects additional data on top of the baseline to set up the specific scenario the agent must solve. This is what makes the task solvable — the agent discovers the seeded data through normal tool use. Examples of task-specific seed data:
  • An email from a hiring manager requesting an interview be scheduled
  • A calendar event the agent needs to reschedule
  • A chat message with a candidate question that needs a response
Task-specific seed data is defined in the task configuration and injected fresh at the start of each rollout, ensuring clean state across attempts.

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Why Seed Data Matters

Seed data is what makes simulation environments realistic rather than empty sandboxes. It provides:
  • Grounding — Tasks reference real data present in the environment, so agents must read and reason over actual records.
  • Reproducibility — The same seed data produces the same starting conditions across rollouts, enabling fair comparison between agents or models.
  • Isolation — Each rollout starts from a known state, so results from one run don’t leak into the next.