---
license: mit
task_categories:
  - text-generation
  - question-answering
language:
  - en
tags:
  - hermes-agent
  - nous-research
  - generative-ui
  - agent-evals
  - interactive-micro-apps
  - human-in-the-loop
  - active-learning
pretty_name: Agent Generative UI Evaluation Suite
size_categories:
  - n<1K
---

# Hermes Agent — Generative UI (Interactive Micro-Apps) Evaluation Suite

## Dataset Summary

**`hermes-genui-evals`** is an **Eval-Driven Development (EDD)** benchmark designed to evaluate **Bidirectional Generative UI (Interactive Micro-Apps)** plugins for [**Hermes Agent**](https://github.com/NousResearch/hermes-agent) by **Nous Research**.

While Hermes Agent excels at backend execution, persistent memory (`MEMORY.md`), cron scheduling, and multi-channel messaging, complex cognitive workflows—such as **studying a technical topic in depth**, **what-if parameter simulations**, and **Human-in-the-Loop (HITL) batch triage**—suffer when constrained to static walls of Markdown text.

This evaluation suite tests an agent's ability to:
1. **Recognize when to trigger (or abstain from triggering) an interactive micro-app** across varying levels of prompt explicitness (`explicit_ui`, `semi_guided`, and `implicit_intent`).
2. **Ground the generated UI** in real tool outputs (`read_file`, `web_search`, `execute_command`, `memory_read`) without hallucinating data.
3. **Generate self-contained, error-free HTML/JS micro-apps** that execute cleanly in a headless browser (Playwright).
4. **Maintain a bidirectional state contract** via `window.hermes.sendAction(payload)`, feeding user interactions (e.g., quiz diagnostic weak spots, confirmed file deletions, cron schedules) back into Hermes Agent's execution loop.

---

## Evaluation Methodology (Anthropic Agent Evals Framework)

This dataset is structured following the nomenclature and best practices from Anthropic's paper [*"Demystifying evals for AI agents"*](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents):

* **Agent Harness:** Hermes Agent equipped with the Generative UI plugin (`render_micro_app` tool + `SKILL.md`).
* **Evaluation Harness:** An automated Python + Playwright runner that executes $k$ **Trials** per **Task**, captures the **Transcript** (tool call trace) and the **Outcome** (rendered DOM state + bidirectional callback payload), and runs three tiers of **Graders**:
  1. **Code-Based Graders (Deterministic):**
     * *Transcript Check:* Verifies `render_micro_app` invocation (`expected_tool_trigger`) to measure both **undertriggering** and **overtriggering**.
     * *Outcome DOM Check:* Loads the generated micro-app in headless Chromium, asserts zero runtime console/page errors (`zero_js_errors`), verifies data grounding (`must_contain_strings`), and checks interactive controls (`required_selectors`).
     * *Outcome Callback Check:* Executes `outcome_interaction_steps` in Playwright and validates the resulting `window.hermes.sendAction(...)` payload against `expected_callback_schema` (JSON Schema).
  2. **Model-Based Graders (Binary `PASS`/`FAIL` Rubrics):**
     * Instead of noisy 1–5 Likert scales, an LLM judge evaluates the rendered UI and code against strict **binary (`PASS`/`FAIL`) rubrics** (`model_grader_rubrics`) assessing pedagogical scaffolding, active recall enforcement, and autonomous UI selection.
  3. **Reference Solutions:**
     * Every task includes a verified `reference_solution` that passes 100% of the deterministic and model-based graders to guarantee task solvability and grader calibration.

### Target Metrics: `pass@k` vs `pass^k`
* **`pass@k`** ($1 - (1 - p)^k$): Measures the agent's capability ceiling (at least 1 successful trial out of $k$).
* **`pass^k`** ($p^k$): Measures production reliability and consistency ($k$ out of $k$ consecutive trials succeeding without a single JS error or broken callback).

---

## Dataset Taxonomy & Distribution (v1 — 20 Tasks)

### 1. By Suite Type (`suite_type`)
* **`capability` (10 tasks):** Challenging tasks testing multi-step interactive simulations, literature contradiction matrices, and autonomous pedagogical UI initiative (`implicit_intent`).
* **`regression` (10 tasks):** Core functional tasks (6 positive `explicit_ui` tasks + 4 `negative_control` tasks) that must maintain a ~100% pass rate across plugin iterations.

### 2. By Functional Category (`category`)
* **`topic_study` (11 tasks — Core Focus):** Interactive learning environments coupled with Hermes's long-term memory and cron scheduler:
  * *Explorable Explanations & Mathematical Simulators* (e.g., `pass@k` vs `pass^k`, Softmax Temperature, Neural Network Backpropagation).
  * *Active Recall & Gap Diagnostics* (e.g., Flashcards & quizzes with hidden answers that send `weak_concepts` back to `MEMORY.md`).
  * *Concept Graph Explorers* (e.g., Distributed consensus Raft state & Pre-Vote exploration).
  * *Literature Comparison Matrices* (e.g., Cross-examining RAG evaluation papers like RAGAS vs ARES).
  * *Spaced Repetition Planners* (e.g., Turning memory weak spots into automated Telegram review crons).
* **`human_in_the_loop` (2 tasks):** Safe destructive file cleanup (`~/Downloads`) and batch email triage with editable draft replies.
* **`what_if_simulation` (1 task):** Cloud GPU (RunPod A100) vs Local Hardware (Mac Studio) break-even calculator.
* **`ops_monitoring` (1 task):** Visual process manager (`ps aux`) isolating runaway processes while protecting production databases.
* **`workflow_builder` (1 task):** Visual Cron & delivery channel configurator for automated ArXiv digests.
* **`negative_control` (4 tasks):** Simple factual questions, CLI lookups, and unambiguous single-step commands where spawning a micro-app is over-engineering (`expected_tool_trigger: false`).

### 3. By Prompt Explicitness (`prompt_explicitness`)
A critical dimension of this benchmark is evaluating whether the agent only builds UIs when explicitly told which widgets to render, or whether it can diagnose a user's cognitive bottleneck on its own:
* **`explicit_ui` (7 tasks):** The user explicitly requests an interactive component (tests pure code & callback implementation).
* **`semi_guided` (6 tasks):** The user expresses an intent to practice, compare, or triage without naming specific UI widgets (tests archetype mapping).
* **`implicit_intent` (3 positive tasks + 4 negative controls):** The user states a raw problem or learning bottleneck with **zero mention of UI** (e.g., *"I have my midterm tomorrow and I still get confused about Raft split votes..."*). Tests true agentic initiative vs. overtriggering.

---

## Schema & Column Definitions (13 Columns)

| Column | Type | Description |
| :--- | :--- | :--- |
| **`task_id`** | `string` | Unique identifier for the task (e.g., `study_cap_06`, `hitl_reg_01`, `neg_reg_01`). |
| **`suite_type`** | `string` | Evaluation suite classification: `"capability"` or `"regression"`. |
| **`category`** | `string` | Functional domain (`"topic_study"`, `"human_in_the_loop"`, `"what_if_simulation"`, `"ops_monitoring"`, `"workflow_builder"`, `"negative_control"`). |
| **`prompt_explicitness`** | `string` | Level of UI guidance in the prompt: `"explicit_ui"`, `"semi_guided"`, or `"implicit_intent"`. |
| **`acceptable_ui_archetypes`** | `string` (JSON list) | List of valid UI patterns for the task (allows flexibility on `implicit_intent` prompts, or `["none"]` for negative controls). |
| **`user_prompt`** | `string` | The natural language prompt sent to Hermes Agent. |
| **`mock_tool_context`** | `string` (JSON list) | Simulated outputs from prior Hermes tools (`read_file`, `web_search`, `execute_command`, `memory_read`) that must ground the micro-app. |
| **`expected_tool_trigger`** | `bool` | **Transcript Grader:** `true` if the agent should invoke `render_micro_app`, `false` for negative controls. |
| **`outcome_dom_assertions`** | `string` (JSON dict) | **Outcome Grader (Playwright):** Deterministic checks (`zero_js_errors`, `must_contain_strings`, `required_selectors`, `min_interactive_elements`). |
| **`outcome_interaction_steps`** | `string` (JSON list) | Sequential Playwright actions (`click`, `set_range`) executed to test reactivity and trigger the callback. |
| **`expected_callback_schema`** | `string` (JSON Schema) | **Bidirectional Outcome Grader:** JSON Schema validating the payload sent via `window.hermes.sendAction(payload)`. |
| **`model_grader_rubrics`** | `string` (JSON list) | **Model-Based Grader:** Strict binary (`PASS`/`FAIL`) rubrics evaluated by an LLM judge. |
| **`reference_solution`** | `string` | Verified self-contained HTML/JS micro-app (or plain text response for negative controls) that passes 100% of the task's graders. |

---

## Quick Usage

```python
import json
from datasets import load_dataset

# Load the dataset from Hugging Face Hub
dataset = load_dataset("Agenticaa/genui_benchmark", split="train")

for task in dataset:
    print(f"[{task['task_id']}] ({task['suite_type']} | {task['prompt_explicitness']})")
    print(f"Prompt: {task['user_prompt']}")
    dom_checks = json.loads(task["outcome_dom_assertions"])
    rubrics = json.loads(task["model_grader_rubrics"])
    print(f"Expected Trigger: {task['expected_tool_trigger']} | Rubrics: {[r['id'] for r in rubrics]}\n")