AISquare is the system of record for AI decisions. It captures the reasoning behind every AI agent decision as a persistent graph, enforces decision-level policies, lets humans correct behavior in place without redeploys, and keeps an auditable record of what happened.

What can I do with AISquare?

Four things: Understand decisions with a Reasoning Graph, Prevent bad actions with Policy Enforcement, Fix behavior with a Correction Loop, and Remember context with Enterprise Memory that travels across models and frameworks.

Which AI agent frameworks does AISquare work with?

AISquare works with the agent stacks teams already build on, including Agno, LangChain, LlamaIndex, and CrewAI, and model providers such as OpenAI, Claude, Gemini, and Mistral, via the aisquare-explainability Python SDK and the AISquare API.

Is AISquare free to start?

Yes. AISquare has a free tier with no credit card required, plus usage-based paid plans (Starter, Pro, Teams, and Enterprise).

Engineers who debug agent decisions, compliance teams who enforce controls, and leaders who need defensible, auditable AI.

Blogs

Notes from building trustworthy AI.

Explainability, agentic governance, and the engineering behind AI you can actually put in front of a customer.

LatestAgentic Governance

Agentic Governance Platform:
How to Choose One for AI Agents

Choosing an agentic governance platform? Compare the three types of tools, the eight capabilities that matter, and the questions to ask vendors.

October 15, 2026·3 min read

Omkar Shendge

Senior Growth Manager

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Agentic Governance

Oct 15, 2026 · 4 min read

Agentic Governance Framework: A Practical Template

A practical agentic governance framework for AI agents: owners, autonomy levels, controls and evidence, mapped to the EU AI Act, NIST AI RMF and ISO 42001.

Omkar Shendge

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Explainability

Oct 15, 2026 · 3 min read

Explainable AI Agents: Explaining Decisions, Not Just Models

Classic explainable AI explains model features. AI agents need more: what they claimed, what evidence backed it, and which rules they were checked on.

Omkar Shendge

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Agentic Governance

Oct 9, 2026 · 4 min read

Agentic Governance: How to Get Agents Into Production

Agentic governance is what gets agents past risk and compliance. Learn the five controls risk teams ask for, and how to prove agents belong in production.

Omkar Shendge

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Guardrails

Oct 9, 2026 · 3 min read

AI Agent Guardrails: What They Catch and What They Miss

AI agent guardrails block bad inputs and outputs, but miss unbacked claims and broken business rules. Here’s what a complete guardrail stack needs.

Omkar Shendge

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Observability

Oct 9, 2026 · 3 min read

Agent Observability: Why Traces Are Not Enough

Agent observability shows what your AI agent did. It can’t show what the agent believed or whether your data backed it. Here’s how to close that gap.

Omkar Shendge

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Engineering

Mar 4, 2026 · 3 min read

Creating an AI knowledge layer on top of your project repository

What if your codebase could explain itself? Here's how to build an AI-powered knowledge layer that turns your repository into a living, queryable resource for your entire team.

Zahwah Jameel

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Developer Experience

Feb 18, 2026 · 3 min read

Learn new developer tools faster by turning official docs into an AI mentor

Official documentation is comprehensive but overwhelming. Here's how to transform any tool's docs into a personalized AI mentor that teaches you at your own pace.

Zahwah Jameel

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Research

Feb 3, 2026 · 4 min read

How explainable AI reduces hallucinations in domain-specific systems

Hallucinations aren't just a model problem — they're a transparency problem. Here's how explainability techniques can dramatically reduce false outputs in specialized AI systems.

Zahwah Jameel

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