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AISquare is a system of record designed for AI decisions, providing visibility, governance, and accountability for organizations deploying AI agents. It addresses the need for explainable, auditable, and policy-compliant AI by capturing, documenting, and managing every decision made by AI agents. The platform is aimed at engineers who debug AI decisions, compliance teams responsible for enforcing controls, and organizational leaders requiring defensible AI operations.

Key features include a Reasoning Graph that reveals the claims, evidence, assumptions, and tools behind each AI decision, enabling teams to understand, explain, and audit outcomes. The Policy Gates feature allows compliance rules to be set and enforced at the decision level, with every enforcement logged as part of the decision record. Correction Loop functionality lets users fix erroneous decisions in context and ensures those corrections persist in future runs, eliminating the need to modify traces, prompts, or redeploy code. The Enterprise Memory component grounds agent behavior in an organization’s knowledge, policies, and trusted sources, making every decision, correction, and approval reusable across large language models, frameworks, and workflows.

AISquare integrates with existing agent stacks through SDKs and APIs, requiring minimal setup and no retraining or redeployment of agents. It operates independently of specific models or frameworks, supporting a portable and compounding memory layer for AI agents. The platform is positioned as suitable for a range of workflows, including secure coding, agent debugging, contract review, financial advisory, and customer support.

The service is available for both developers and enterprise teams, offering a workflow that spans from mapping AI decisions and identifying risks to deploying governed production workflows and scaling across teams. AISquare Studio is the main product interface, capturing and managing decision records for both human and AI agent actions.