> ## Documentation Index
> Fetch the complete documentation index at: https://docs.collinear.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Seed Data

> Domain-specific data injected into environments to create realistic starting conditions

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

## 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.

## 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.

## 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.