# AI Agents - VectorCat

> Build and deploy AI agents for enterprise data governance. Automate compliance, metadata enrichment and AI-assisted research — all within your security boundaries.

Source: https://www.sci2sci.com/ai-agents

# SAFE AI Agents for Enterprise Data

Build domain-specific AI applications by safely integrating proprietary data with any AI model. Customize and deploy agents right on your data with instant access to organizational context.

[### Development Platform Build and visualize agent workflows](#platform)[### Governance Agents Protect, organize and enrich your data](#pipeline)[### User-Facing Agents Search, extract and document with AI](#user-facing)

## Agent Development Platform

VectorCat offers a powerful AI development environment while supporting familiar tools like Python and its libraries. All agents inherit the user's [SAFE](https://www.sci2sci.com/safe-principles) workspace — ensuring security, auditability and governance are part of the architecture. Our fleet of data management assistants was built on top of it.

### Agents as Code

Define agents entirely in code for easy versioning, change management and collaboration. Includes a built-in testing framework supporting unit, integration and regression tests — ensuring agent quality at every stage of development.

### Workflow Visualization

Automatically visualizes complex DAG-based flows for both developers and business users, making it easy to test, validate and ensure the quality of each step — enabling enterprise-grade agent implementations.

### Distributed Execution

Leverages a distributed execution mechanism where client machines control the flow, reducing server infrastructure costs and complexity while improving responsiveness and interactivity in agentic workflows. It's a "write once — run everywhere" framework, which runs both in backend sandboxes and in the browser via WebAssembly.

### Model-Agnostic Approach

Supports seamless integration with any model from any provider, enabling rapid performance testing and effortless upgrades when better models become available.

## Data Governance Agents

These agents work continuously in the background to protect, organize and enrich your data as it flows through the platform.

### Data Protection Agent

Automated compliance enforcement

Automatically enforces data protection policies across your entire data landscape.

-   **Scan** — Multi-stage SPI detection: Safe Harbor, GDPR, GxP, quasi-identifiers, composite identifiers and custom policies
-   **Quarantine** — Immediately block any detected SPI or confidential data from unauthorized access
-   **Govern** — Review with stewards from DPO/Compliance to accept policy and quarantine decisions

### Data Stewardship Agent

Domain understanding & organization

Helps governance teams discover structure, create ontologies and organize data assets semantically.

-   **Discover** — Assist your governance team in discovering structure and creating an ontology
-   **Map** — Map ontologies to ground truth. Work with semantic assets, not files
-   **Describe** — Generate descriptions for discovered assets. Collaborate and validate with domain stewards

### Data Annotation Agent

Metadata enrichment & transformation

Automatically enriches data with business metadata and transforms between structured and unstructured formats.

-   **Enrich** — Assign business metadata automatically at any level of granularity
-   **Transform** — Convert unstructured data to structured and vice-versa
-   **Compose** — Prepare domain-specific knowledge graphs for downstream consumption

## User-Facing Agents

Once data is governed and enriched, these agents provide direct value to scientists, analysts and domain experts.

### AI Research Assistant

Searches organizational knowledge to answer domain-specific questions

-   **Search** — Runs autonomous searches across organizational resources to find relevant documents and data assets
-   **Summarize** — Provides summaries with references to all findings, grounded in source material
-   **Reason** — Answers questions like "what experiments have we performed with compound N?"

### Data Extraction Agent

Extracts and harmonizes data from unstructured sources into consistent formats

-   **Extract** — Pulls data from structured or unstructured documents (lab notes, protocols, log files) into user-defined formats
-   **Normalize** — Converts languages, units (2 mL → 2000 µL), and infers information from context
-   **Validate** — Outputs can be manually corrected by the user before validation. Reduces curation time from hours to minutes

### Documentation Agent

Generates and maintains documentation from data asset context

-   **Generate** — Creates comprehensive documentation for data pipelines, experimental protocols and analytical workflows
-   **Contextualize** — Understands relationships between assets within the same project or workflow
-   **Maintain** — Keeps documentation consistent across teams, for both wet lab scientists and computational teams

## Ready to try our agents or develop yours?

See how VectorCat's agent framework can accelerate your data workflows.

[Book a Call](https://www.sci2sci.com/enroll) [Learn about SAFE Environment](https://www.sci2sci.com/safe-principles)