| # Datasheet for AgentDisruptBench |
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| _Following the [Datasheets for Datasets](https://arxiv.org/abs/1803.09010) framework (Gebru et al., 2021)._ |
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| ## Motivation |
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| ### For what purpose was the dataset created? |
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| AgentDisruptBench was created to study **evaluation as a scientific object** in the context of AI agent resilience under runtime tool-call disruptions. Existing benchmarks measure *whether* agents can use tools correctly, but assume tools behave perfectly — an unrealistic assumption in production environments. This project provides an evaluation methodology, consisting of structured tasks paired with a Disruption Engine, to rigorously measure how we assess agent reliability, recovery strategies, and graceful degradation using the $R(k, \epsilon, \lambda)$ surface. |
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| ### Who created the dataset and on behalf of which entity? |
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| AgentDisruptBench Contributors, as part of academic research into AI agent robustness. |
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| ### Who funded the creation of the dataset? |
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| <!-- Fill in: grant numbers, institutional support, etc. --> |
| [To be specified] |
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| ## Composition |
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| ### What do the instances that comprise the dataset represent? |
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| Each instance is a **benchmark task** defined in YAML with: |
| - A natural language task description (the prompt given to the agent) |
| - Required tools (from a set of 30 deterministic mock tools) |
| - Ground truth: expected outcome, required tool calls, evaluation rubric with weighted criteria |
| - Metadata: domain, difficulty (1-5), task type |
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| ### How many instances are there in total? |
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| **100 tasks** organized as follows: |
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| | Domain | Standard | Adversarial | Impossible | Handover | Total | |
| |------------|:--------:|:-----------:|:----------:|:--------:|:-----:| |
| | Retail | 20 | 2 | 2 | 1 | 25 | |
| | Travel | 20 | 2 | 2 | 1 | 25 | |
| | Finance | 20 | 2 | 2 | 1 | 25 | |
| | DevOps | 20 | 2 | 2 | 1 | 25 | |
| | **Total** | **80** | **8** | **8** | **4** | **100** | |
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| ### Does the dataset contain all possible instances or is it a sample? |
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| The dataset is a curated collection of synthetic tasks. It is not a sample from a larger population. Task difficulty levels are distributed as D1=8, D2=16, D3=24, D4=20, D5=12 tasks across standard tasks, following a roughly bell-shaped distribution centered on medium difficulty. |
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| ### What data does each instance consist of? |
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| Each task instance contains: |
| - `task_id`: Unique identifier (e.g., `retail_001`) |
| - `title`: Short human-readable title |
| - `description`: Full prompt given to the agent (natural language) |
| - `domain`: One of {retail, travel, finance, devops} |
| - `difficulty`: Integer 1-5 |
| - `task_type`: One of {standard, adversarial, impossible} |
| - `required_tools`: List of tool names needed |
| - `expected_tool_call_depth`: Expected number of tool calls under clean conditions |
| - `ground_truth`: Structured evaluation criteria including: |
| - `expected_outcome`: Description of success |
| - `required_tool_calls`: Tools that must be called |
| - `forbidden_tool_calls`: Tools that must NOT be called |
| - `correct_final_answer`: Exact expected answer (if applicable) |
| - `evaluation_rubric`: Criterion → weight mapping (sums ≈ 1.0) |
| - `disruption_sensitive_tools`: Tools where failure is most impactful |
| - `recovery_actions`: Expected recovery behaviours |
| - `trap_description`: (adversarial only) the trap the agent should avoid |
| - `impossibility_reason`: (impossible only) why no valid solution exists |
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| ### Is any information missing from individual instances? |
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| No. All fields are populated for every task instance. |
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| ### Are relationships between individual instances made explicit? |
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| Tasks within the same domain share tools and mock data (e.g., retail tasks all use the same product catalog and customer database via deterministic mock tools). Cross-domain relationships do not exist. |
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| ### Are there recommended data splits? |
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| No train/test split is provided because this is a benchmark, not a training dataset. All 100 tasks are intended for evaluation. However, researchers may partition by domain, difficulty, or task type for focused analysis. |
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| ### Does the dataset contain data that might be considered confidential? |
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| No. All data is synthetic. No real personal data, API keys, or proprietary information is included. |
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| ### Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? |
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| No. Tasks involve routine operations (e-commerce, travel booking, financial transactions, DevOps operations) with no offensive content. |
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| ## Collection Process |
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| ### How was the data associated with each instance acquired? |
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| All tasks were authored manually by the benchmark contributors. They are synthetic, designed to cover a range of realistic tool-use scenarios across difficulty levels. |
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| ### What mechanisms or procedures were used to collect the data? |
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| Task design followed a structured process: |
| 1. Define 4 domains based on common LLM agent use cases |
| 2. Design 8 mock tools per domain (30 total) |
| 3. Author 20 standard tasks per domain with increasing difficulty |
| 4. Author adversarial tasks with designed trap patterns |
| 5. Author impossible tasks with clear impossibility reasons |
| 6. Author handover tasks requiring human escalation |
| 7. Validate all ground truth against mock tool outputs |
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| ### Who was involved in the data collection process? |
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| The benchmark authors. |
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| ### Over what timeframe was the data collected? |
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| March-April 2026. |
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| ### Were any ethical review processes conducted? |
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| <!-- Fill in if applicable --> |
| [To be specified] |
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| ## Uses |
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| ### What are the intended uses of the dataset? |
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| 1. **Primary**: Evaluate AI agent resilience under runtime tool-call disruptions using the provided disruption profiles (clean → hostile_environment) |
| 2. **Secondary**: Compare agent frameworks (LangChain, OpenAI, AutoGen, CrewAI) on reliability metrics |
| 3. **Tertiary**: Study recovery strategies and failure patterns in LLM agents |
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| ### What are some tasks/uses that the dataset should not be used for? |
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| - **Not for training**: The benchmark should be used for evaluation only. Training on the tasks would compromise benchmark validity. |
| - **Not for real tool testing**: Mock tools are deterministic simulations, not real API integrations. |
| - **Not for safety/security evaluation**: The disruptions model reliability failures, not adversarial attacks. |
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| ### Is there anything about the composition of the dataset or the way it was collected that might impact future uses? |
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| - Tasks are in English only |
| - Mock tools simulate US-centric services (USD currency, US dates, etc.) |
| - Ground truth rubrics use string-matching heuristics which may not capture all valid agent responses |
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| ## Distribution |
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| ### How will the dataset be distributed? |
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| - **Primary**: GitHub repository at [https://github.com/Kavirubc/AgentDisruptBench](https://github.com/Kavirubc/AgentDisruptBench) |
| - **Persistent**: HuggingFace Datasets at [To be created] |
| - **Metadata**: Croissant JSON-LD file included in the repository |
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| ### When will the dataset be distributed? |
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| The dataset is publicly available as of the initial release. |
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| ### Will the dataset be distributed under a copyright or other intellectual property (IP) license? |
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| MIT License. |
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| ### Have any third parties imposed IP-based or other restrictions on the data? |
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| No. |
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| ## Maintenance |
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| ### Who is supporting/hosting/maintaining the dataset? |
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| AgentDisruptBench Contributors via the GitHub repository. |
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| ### How can the owner/curator/manager of the dataset be contacted? |
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| Via GitHub Issues at the repository. |
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| ### Will the dataset be updated? |
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| Yes. Planned updates include: |
| - Additional task variants for ε-robustness testing |
| - New domains |
| - Track B: Network-layer disruption injection via Envoy proxy |
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| ### If the dataset relates to people, are there applicable limits on the retention of the data? |
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| N/A — the dataset is entirely synthetic with no real personal data. |
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| ### Will older versions of the dataset continue to be available? |
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| Yes, via Git tags and versioned releases. |
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| ### If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for doing so? |
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| Yes. See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on contributing new tasks, domains, tools, and disruption types. |
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