---
license: apache-2.0
task_categories:
  - image-text-to-text
  - visual-question-answering
  - document-question-answering
language:
  - en
pretty_name: Multimodal Knowledge Agent (MKA) Showcase Examples
---

# Multimodal Knowledge Agent (MKA) — 10 Showcase Examples

**Multimodal Knowledge Agent (MKA)** is a benchmark from Google DeepMind for evaluating autonomous AI agents on complex, multi-step, real-world visual knowledge work across professional domains.

Unlike text-extractable benchmarks where attachments can be solved via plain-text extraction or OCR alone, every MKA task contains a multi-file workspace with load-bearing visual artifacts—such as architectural CAD blueprints, electrical and RF schematics, geospatial rasters, seasonal dependency graphs, biochemical gel and spectrophotometric plates, and multi-axis charts—that require native multimodal perception, cross-document synthesis, tool orchestration (code execution, file inspection), and standalone deliverable generation (`.xlsx`, `.pptx`, `.docx`, `.json`, or structured technical reports).

---

## 10 Showcase Tasks (10 Unique Domains & Verticals)

| # | Vertical | Domain | Role | Task ID |
| :-: | :--- | :--- | :--- | :--- |
| **1** | **Logistics & Mapping** | Surveying & GIS | GIS Analyst | `task_4308eaef4d504328988c1cb5dfd7db77` |
| **2** | **Construction** | Architecture | Architect | `task_a46a6c3cebd54b82b9ad5e66d1e14e48` |
| **3** | **Engineering** | Electrical & RF Engineering | RF IC Design Engineer | `task_143a5623f26c404baaeb63ccd891e196` |
| **4** | **Engineering** | Mechanical & Control Systems | Mechanical Engineering Instructor | `task_dd94dc222db74864a13fcb7c6c2844a2` |
| **5** | **Science** | Applied Sciences (Biochemistry) | Biochemist | `task_47d262f6d58c4e5293a22623f0d711e5` |
| **6** | **Business** | Workforce Planning & HR Analytics | Workforce Planning Analyst | `task_fea07f327cfd4f4c91af0bf78da197d4` |
| **7** | **Finance** | M&A / Management Consulting | Investment Analyst | `task_c9ab91021dc14eab926dfea0a208108a` |
| **8** | **Education** | History & Cartography | Historian | `task_9e3bdd2bdccd4164bcc792c914f59925` |
| **9** | **Law & Government** | Public Policy | Policy Analyst | `task_5a0212833c224c85b79960604679eb84` |
| **10** | **Software & Data** | Data Science | Data Scientist | `task_e49e96e764424db08531c39fb685ed70` |

---

## Repository Structure

```text
tasks/
└── task_<id>/
    ├── metadata.json    # Task ID, prompt, domain, role, input file list, task goal, and economic rationale
    ├── rubrics.json     # Fine-grained grading criteria and criterion weights
    └── input/           # All source input files provided to the agent in the workspace
```

Each `metadata.json` contains:
- `TaskID`: Unique task identifier (`task_<32-hex>`)
- `Prompt`: Full verbatim instruction prompt given to the agent
- `All Task Files`: Relative path to the task input workspace (`tasks/task_<id>/input/`)
- `Rubric Criteria`: Relative path to the task rubric file (`tasks/task_<id>/rubrics.json`)
- `Input_files`: Comma-separated list of input filenames inside `input/`
- `Vertical`: Top-level industry vertical
- `Domain`: Professional domain
- `Role`: Target professional role
- `Task Goal`: Summary of the deliverable goal
- `Economic Rationale`: Real-world professional time and value context

---

## License

Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).