***

title: Overview
description: >-
AISquare is an audit, trust, and governance infrastructure for agentic
systems.
--------

AISquare is an audit, trust, and governance infrastructure for agentic systems.

It enables organizations to **observe**, **explain**, and **control** AI agent decisions, making them safe, defensible, and ready for enterprise deployment.

***

## Why AISquare exists

As organizations adopt AI agents, a critical challenge emerges:

> AI systems make decisions that cannot be easily explained, audited, or trusted.

This creates three major risks:

* **Regulatory risk** → no audit trail for AI decisions
* **Legal risk** → inability to explain outcomes
* **Reputational risk** → black-box failures becoming public

Without governance, AI adoption slows down or stops entirely.

***

## What AISquare does

AISquare introduces a missing layer in modern AI systems: **a system of record for AI decisions**.

Just like:

* Financial systems track transactions
* HR systems track people

**AISquare tracks and governs AI decision-making.**

***

## Core capabilities

AISquare provides a complete governance layer for AI agents.

### Durable decision record

A tamper-evident log of every agent action and reasoning path. This ensures decisions can be audited and defended when needed.

### Human-in-the-loop governance

Structured workflows allow humans to review, approve, or intervene in agent decisions.

### Decision-level policy enforcement

Policies can be applied directly to agent reasoning to:

* **Allow**
* **Deny**
* **Escalate**
* **Modify** decisions

### No-code audit interface

Non-technical users can inspect and replay decisions without engineering support.

***

## How AISquare works

AISquare integrates into your existing AI systems and provides visibility into how decisions are made.

A typical flow:

1. **Connect your agents** — Integrate agents from any framework using AISquare SDK
2. **Navigate decisions** — Visualize decision flows and reasoning paths
3. **Audit reasoning** — Understand claims, assumptions, and evidence
4. **Apply policies** — Add human feedback and governance controls

***

## The reasoning and memory graph

At the core of AISquare is a **persistent reasoning graph**.

This graph:

* Records **why** decisions are made
* Preserves logic and context
* Stores human interventions
* Acts as audit-ready evidence

It turns AI decision-making into a traceable, durable artifact.

***

## What you can build

AISquare works with any:

* AI framework
* Model
* Workflow

You can:

* Build agents internally
* Work with partners
* Deploy using AISquare

AISquare ensures every agent is:

* **Auditable**
* **Explainable**
* **Governed**
* **Scalable**

***

## Why developers use AISquare

AISquare helps teams:

* Ship AI agents faster
* Debug and optimize decision flows
* Reduce risk with policy controls
* Deliver enterprise-grade AI systems

It enables teams to move from experimental agents to production-ready systems.

***

## What this API enables

The AISquare API allows you to:

* Retrieve and analyze agent decisions
* Access structured experience and resource data
* Manage collections and workflows
* Build applications on top of governed AI systems

***

## Next steps

To get started:

* Follow the [Quickstart](/docs/getting-started/quickstart) to make your first request
* Review [Core Concepts](/docs/getting-started/core-concepts) to understand AISquare entities
* Set up [Authentication](/docs/getting-started/authentication) for your integration
* Build your [first integration](/docs/integration-guides/build-your-first-ai-studio-integration)
* Explore [Collections](/docs/product/collections) and [Activity](/docs/product/activity-and-personalization) APIs
* Browse the [API Reference](/docs/api-reference) for all available endpoints