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h0000w/hendar-agentic-ai-dataset

published by @h0000w

Alignment dataset tracked in the /datasets sub-catalog.

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Last 30 days
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Created on HF
2026-09-12
Age
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Description

Hendar Agentic AI Evaluation & Security Benchmark

A compact, expert-authored benchmark for evaluating trustworthy agentic AI systems across capability, tool use, retrieval, security, policy enforcement, multi-agent coordination and regression safety. The current release contains 104 synthetic cases: 13 cases in each of 8 domains.

This dataset is a public companion to the Agentic AI Academy by Hendar Mawan, PhD. It is designed for evaluation, CI regression testing, red-team… See the full description on the dataset page: https://huggingface.co/datasets/h0000w/hendar-agentic-ai-dataset.

Tags

task_categories:text-classificationtask_categories:question-answeringtask_categories:text-generationlanguage:enlicense:cc-by-4.0size_categories:n<1Kformat:jsonmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:usagentic-aiai-agentsevaluationai-safetyprompt-injectionragtool-usemulti-agenttrustworthy-aiai-governance

README current version from Hugging Face

license: cc-by-4.0
task_categories:

  • text-classification
  • question-answering
  • text-generation
    language:
  • en
    tags:
  • agentic-ai
  • ai-agents
  • evaluation
  • ai-safety
  • prompt-injection
  • rag
  • tool-use
  • multi-agent
  • trustworthy-ai
  • ai-governance
    pretty_name: Hendar Agentic AI Evaluation & Security Benchmark
    size_categories:
  • n<1K
    configs:
  • config_name: task_success
    data_files: data/task_success.jsonl
  • config_name: tool_routing
    data_files: data/tool_routing.jsonl
  • config_name: rag_groundedness
    data_files: data/rag_groundedness.jsonl
  • config_name: prompt_injection
    data_files: data/prompt_injection.jsonl
  • config_name: unsafe_tool_requests
    data_files: data/unsafe_tool_requests.jsonl
  • config_name: policy_decisions
    data_files: data/policy_decisions.jsonl
  • config_name: multi_agent_tasks
    data_files: data/multi_agent_tasks.jsonl
  • config_name: regression_cases
    data_files: data/regression_cases.jsonl

Hendar Agentic AI Evaluation & Security Benchmark

A compact, expert-authored benchmark for evaluating trustworthy agentic AI systems across capability, tool use, retrieval, security, policy enforcement, multi-agent coordination and regression safety. The current release contains 104 synthetic cases: 13 cases in each of 8 domains.

This dataset is a public companion to the Agentic AI Academy by Hendar Mawan, PhD. It is designed for evaluation, CI regression testing, red-team exercises and engineering education—not as a generic instruction-tuning corpus.

Benchmark domains

Config Purpose Core signal
task_success Goal completion whether the agent reaches the requested outcome within constraints
tool_routing Tool selection whether the correct tool is selected or tools are avoided
rag_groundedness Retrieval quality citation/evidence fidelity and unsupported-claim resistance
prompt_injection Injection defense whether untrusted instructions are ignored and reported
unsafe_tool_requests Action safety whether destructive, privacy-invasive or unauthorized actions are blocked
policy_decisions Governance allow / require-approval / deny classification with rationale
multi_agent_tasks Coordination delegation, role boundaries, aggregation and conflict handling
regression_cases Release gating stable cases intended to catch known classes of engineering regressions

Common schema

Every record contains:

  • id — stable benchmark identifier
  • domain — benchmark domain
  • input — user/task input
  • context — optional system, retrieved or organizational context
  • expected — expected behavior or output properties
  • expected_label — compact machine-checkable target
  • risk_level — low, medium, high, or critical
  • tags — scenario labels for slicing results
  • rationale — why the expected behavior is correct

Some configs add domain-specific fields such as candidate_tools, evidence, attack_type, policy, agents, or regression_target.

Intended uses

Use the benchmark to test agent controllers, policy engines, tool routers, RAG pipelines, approval gates, prompt-injection defenses, multi-agent supervisors and CI/CD release gates. It is deliberately small enough to inspect manually and structured enough to automate.

A recommended evaluation report includes task success, routing accuracy, groundedness, injection-defense pass rate, unsafe-action block rate, policy-decision accuracy, coordination success and regression pass rate.

Limitations

The benchmark is synthetic and expert-authored. It does not represent all real-world threats, languages, industries or adversarial strategies. Passing it is evidence of implementation quality, not proof of complete safety or compliance. High-risk systems require domain-specific threat modeling, independent testing and operational controls.

Provenance and privacy

All cases are synthetic. No private customer data, credentials or production secrets are included. Examples that mention sensitive resources use fictional identifiers.

Related resources

Citation

If you use this benchmark in teaching, evaluation or research, cite the Agentic AI Academy repository and dataset URL.

License

Benchmark data: CC BY 4.0. Canonical Academy source code remains under its repository license.

Five-level production-AI proof

The Academy now uses the shared proof model:

L1 Runnable → L2 Reproducible → L3 Capability-Validated → L4 Production-Candidate → L5 Production-Validated.

The public Playground and benchmark Dataset support the independently inspectable capability layer. The canonical GitHub source computes the current level with make proof; this project deliberately caps its claim at L3 — Capability-Validated and does not infer production deployment from a demo.

Specification: https://github.com/h00w/agentic-ai/blob/main/PROOF_MODEL.md

README history 4 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-09-24Publish Agentic AI evaluation and security benchmarkabc6d4c5.2 KB
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  2. 2026-09-20Publish Agentic AI evaluation and security benchmarkc0cea365.1 KB
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  3. 2026-09-12Publish Agentic AI evaluation and security benchmark9f8a3104.5 KB
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  4. 2026-09-12initial commitdaa75ec21 B
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Used by abliterated models none yet

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Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2026-09-13[bot] Conversion to Parquetopen1 💬#1
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Metadata

License
cc-by-4.0

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Recommended: datasets library (Python) 
from datasets import load_dataset
ds = load_dataset("h0000w/hendar-agentic-ai-dataset")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="h0000w/hendar-agentic-ai-dataset", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/h0000w/hendar-agentic-ai-dataset
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