| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - code |
| - Text |
| - agent |
| pretty_name: Manage tasks with agents |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # Agent Task Management Dataset |
|
|
| **5,000,000 compact synthetic examples for agent task execution.** |
|
|
| Repository: `Lelonthecodeur/agent-task-management-dataset` |
|
|
| ## Purpose |
|
|
| This dataset is designed to train general-purpose agents to plan, |
| coordinate, execute, verify and report complex tasks involving |
| multiple tools and domains. |
|
|
| ## Domains |
|
|
| - email_writing |
| - document_creation |
| - app_development |
| - terminal_operations |
| - backend_engineering |
| - financial_analysis |
| - data_analysis |
| - project_management |
| - research |
| - workflow_automation |
| - software_testing |
| - ui_ux_design |
| - file_management |
| - decision_making |
| - database_management |
| - api_integration |
| - debugging |
| - cross_domain_execution |
|
|
| ## Scenario types |
|
|
| The dataset includes: |
|
|
| - Successful task completion. |
| - Failed attempts. |
| - Error detection and recovery. |
| - Impossible or unsupported tasks. |
| - Clarification and ambiguity handling. |
|
|
| ## Example structure |
|
|
| Each Parquet record contains: |
|
|
| - `id` |
| - `task` |
| - `messages` |
| - `domains` |
| - `difficulty` |
| - `outcome` |
| - `available_tools` |
| - `constraints` |
| - `expected_outputs` |
| - `execution_plan` |
| - `tool_trace` |
| - `error` |
| - `clarification` |
| - `verification` |
| - `final_result` |
| - `scenario_family` |
| - `split` |
| - `synthetic` |
| - `generator_version` |
|
|
| The `messages` field contains a compact JSON conversation |
| with system, user and assistant messages. |
|
|
| ## Dataset splits |
|
|
| The dataset uses approximately: |
|
|
| - 80% training |
| - 10% validation |
| - 10% test |
|
|
| Splits are sampled independently. Scenario families are not |
| strictly held out across splits, so this dataset should not be |
| treated as a leakage-proof benchmark. |
|
|
| ## Synthetic-data disclosure |
|
|
| All examples are programmatically generated. |
|
|
| Tool traces are simulated representations. No real terminal |
| commands, emails, financial transactions, API requests, |
| database changes or deployments were performed. |
|
|
| The examples do not establish that a task was actually |
| completed in an external environment. |
|
|
| The generator uses structured templates and randomized |
| combinations. Consequently, some examples may be repetitive, |
| simplified or less realistic than expert-written trajectories. |
|
|
| Difficulty labels are assigned by the generator and are not |
| independently benchmarked. |
|
|
| This dataset is a synthetic training resource, not a substitute |
| for verified real-world agent execution data. |
|
|
| ## Intended use |
|
|
| - Agent planning and task decomposition. |
| - Multi-domain workflow training. |
| - Tool-selection reasoning. |
| - Error handling and recovery. |
| - Clarification behavior. |
| - Output verification. |
| - Structured task reporting. |
|
|
| ## Limitations |
|
|
| Do not interpret simulated execution traces as real execution |
| evidence. Verify model behavior independently before deploying |
| systems trained on this dataset. |
|
|
| ## Generation details |
|
|
| - Examples: 5,000,000 |
| - Format: Parquet |
| - Compression: Zstandard |
| - Generator version: 1.0.0 |
| - License: MIT |
|
|
| ## License |
|
|
| MIT License. See the LICENSE file. |
