# AISquare: Make every agent as trusted as your best expert. > Product overview: AISquare is the collective intelligence platform for agent trust. It connects a company's knowledge, policies, and expert judgment to every agent reasoning, so agents explain themselves, follow the rules, and improve with every correction. - **URL:** https://aisquare.studio/ - **Audience:** Anyone evaluating AISquare for the first time - **Topics:** agent lifecycle, grounding for AI agents, context for AI agents, agent traces, AI agent observability, agent reasoning, explainable AI, policy enforcement, AI guardrails, continual learning, learning signals, agent trust, collective intelligence, AI governance, AI audit, AI bill of materials, reasoning graph ## What AISquare is Make every agent as trusted as your best expert. AISquare connects your company's knowledge, policies, and expert judgment to every agent reasoning, so agents explain themselves, follow your rules, and improve with every correction. That is collective intelligence. Collective Intelligence Platform for Agent Trust. Agent reasoning (explained), enterprise context (reusable), policies and exceptions (policy approved) and expert judgement (human corrected) meet in a governed system of record: every decision, approval, and correction becomes shared memory that people and agents reuse. The result: better agent reasoning, sharper human judgement, and reusable organizational learning. CTAs: Start for Free (signup) and Book a Demo. ## The trust gap Capability is no longer the bottleneck; trust is. 95% accuracy per step becomes 36% over twenty steps, so trust has to be engineered, not assumed. - 17% have agentic AI in production, though 75% say they are adopting it (Forrester 2026, Gartner CIO Survey 2026). - 40% of enterprises will demote or decommission their AI agents by 2027 over governance gaps found after production incidents (Gartner, May 2026). - 1 in 5 Global 1000 firms face lawsuits, fines, or CIO dismissals by 2030 over inadequate agent controls (IDC FutureScape 2026). - 21% have a mature governance model for agentic AI (Deloitte, 2026). ## The agent trust blueprint The pathway an agent takes to earn real work, the same way a person does: grounded in your business, transparent in its reasoning, held to your rules, always improving, and provable to anyone who asks. One trust layer (the Collective Intelligence Graph, RML and explainability, the Policy Enforcement Engine, the Learning Layer, and Audit and AIBOM) works with any agent, any model, in your cloud. The trust layer, part by part: **Collective Intelligence Graph**: knowledge, context, and reasoning from your systems and people, fed to agents at run time. **RML and Explainability**: Reasoning Markup Language: claims, assumptions, and evidence, checked. **Policy Enforcement Engine**: in-the-run enforcement of every policy: block, improve, warn. **Learning Layer**: feedback, fixes, and learnings shared across the fleet. **Audit and AIBOM**: a cryptographically signed record of every decision and what led to it. What you get: get agents to production, prove they belong in production, compound learnings, and optimize cost and latency. - **Ground (Collective Intelligence + Studios)**: it knows how your business actually works: grounded in your own knowledge, past decisions, and rules; knows which policy overrides which and when a request is escalated; a Studio per domain, built from the systems you already use. - **Explain (Reasoning Graph)**: it shows you why it did what it did: every decision broken into claims, assumptions, and evidence, each claim checked against your own data, unsupported claims flagged before they become actions. - **Enforce (Policy Enforcement Engine)**: it stops before it crosses a line: every decision checked inside the run, before a tool call or reply goes out; allowed, blocked, or escalated to a human in the moment; any rule you can state, deterministic or model-judged. - **Learn (Learning Layer)**: it fixes once and never repeats the mistake: correct a decision where it happened, and once an expert signs off the fix becomes a standing rule every agent in the fleet inherits. - **Prove (Audit + AI BOM, scored by the Trust & Safety Institute)**: it proves all of it, to whoever asks: every action, tool call, model, and data reference signed as it happens and assembled into an AI Bill of Materials. ## Follow one decision through the system A single refund request, from the first question to the rule it leaves behind: AISquare connects your systems and your people's judgment (docs, tickets, CRM, chat, and past human decisions) into one graph; a Studio holds the slice each job needs; rule 4.2 caps refunds at $500, so a $1,250 refund is blocked and escalated; the blocked decision opens into claims, assumptions, and evidence and is signed; a manager approves the exception and records why; the approval becomes precedent and a rule, so the next $900 request clears on its own. The system learns four ways: humans teach agents, agents teach agents, agents inform humans, and humans teach humans, all through the same graph. ## What each team walks away with - **Head of AI · CIO**: agents that make it to production, and stay there. You get your rules enforced on every decision, so agents stay trusted. - **Risk · Compliance**: proof the moment you ask, not two weeks later. You get a signed record of every agent reasoning, ready to export. - **Operations**: agents learn from every correction, so the same mistake never happens twice. You get corrections that become fleet-wide rules. - **Finance · Platform**: fewer tokens, faster answers. You get answers served from the graph, not rebuilt from scratch every run. ## Same platform, every high-stakes workflow The same reasoning, policy, and memory across every high-stakes workflow your agents run, from code to contracts to customer refunds. - **Customer support: Know which policy the agent used.** A support agent approves a refund that should have been reviewed. Outcome: attach the policy, the evidence, the customer context, and the escalation path. - **Secure coding: Block the risky merge, with the reason attached.** An AI coding assistant slips a hardcoded secret into a pull request. Outcome: AISquare blocks the merge and attaches the policy, evidence, owner, and the fix. - **Agent debugging: Replay the run, find the exact break.** A refund agent approves the wrong refund at 2am and no one knows why. Outcome: replay the trace, isolate the failed control point, and fix it before the next run. - **Contract review: Show why the clause changed.** A contract agent redlines an indemnity clause and the partner asks why. Outcome: show the playbook rule, the precedent, and the suggested redline in one view. - **Financial advisory: Give advisors a defensible answer.** A fiduciary agent recommends a portfolio shift and the client asks why. Outcome: return the reasoning, the evidence, and the policy check behind every recommendation. ## Why existing tools are not enough Observability reports what an agent did, after it ran. Eval platforms score outputs in the lab. Guardrails block bad words in and out. None of them shows the reasoning, enforces the policy at run time, and learns from the fix. AISquare explains the agent reasoning, enforces the rule, and learns from the fix, inside every run, before the action lands, across every agent. Collective intelligence is built on reasoning and learning: reasoning establishes trust (you cannot trust what you cannot inspect), and learning grows it, so agent trust rises and cost per run falls as context is cached and decisions are reused. ## Your people get sharper too Studios keep experts in the loop instead of rubber-stamping: experts add files, playbooks, and past decisions; Studios turn them into cited expert notes anyone can learn from; new teammates take on real work in weeks, not years. ## For AI engineers Plug into the agents you already run. The SDK or proxy for production agents, the CLI for coding agents. Every run lands in the same reasoning graph. - **SDK and proxy**: wrap an agent with the SDK, or point your provider's base URL at the AISquare proxy with zero code change; every run gets traces, reasoning, policy verdicts, and a signed audit record. Install: `pip install "aisquare[explainability]"`, then `sdk.init_from_env()` and `GovernedAgent.from_agno(my_agent, rule_book="refunds", enforce=True)`. - **AISquare CLI**: run parallel Claude Code sessions as one team: a planner that files tasks, coders that claim them, and a runner that verifies, on a live board with shared memory. Install: `pipx install 'aisquare-cli[tui]'`, then `aisquare agents connect claude-code`, `aisquare doctor`, and start a role with `AISQUARE_ROLE=planner claude`. - **Works with your stack**: Anthropic, OpenAI, Azure OpenAI, and Gemini through the proxy; Agno, LangChain, and custom agents through the SDK; Claude Code and Codex from the command line; data and tools such as Google Drive, OneDrive, Notion, Jira, GitHub, and n8n. ## From pilot to governed production AISquare works with your team to turn high-value agent workflows into a repeatable system for reasoning, policy, review, and audit. - **Build with Partners:** Bring AISquare into your existing consulting or implementation motion. - **Build with AISquare:** Work with our forward-deployed team on your first governed AI workflow. - **Build Internally:** Use AISquare docs, SDKs, and APIs with your own engineering team. - **01 Discover:** Map AI decisions, identify risk, define human review. - **02 Build:** Reasoning graphs, policy checks, approvals, decision records. - **03 Deploy:** Launch with workflow team, monitor real decisions, close the loop. - **04 Scale:** Repeatable playbook across agents, across models, across teams. CTA: Talk to the Forward Deployed team (book a demo). Ready to ship AI you can trust in production? AI moves fast. Human judgment makes it trustworthy. AISquare connects agents, context, policy, and people so every decision can be understood, guided, and defended. CTAs: Start free and Request a pilot (book a demo). ## Links - [Create a free account](https://home.aisquare.studio/signup) - [Book a demo](https://calendly.com/d/cyp5-wv4-sgs/aisquare-studios-demo) - [Documentation](https://docs.aisquare.studio/docs/getting-started/overview) - [Research: The Future of the Organization with Collective Intelligence](https://aisquare.studio/research/collective-intelligence) --- **Machine-readable AISquare** - Site index for LLMs: https://aisquare.studio/llms.txt - Agent instructions (when to use AISquare): https://aisquare.studio/agents.md - OpenAPI specification: https://aisquare.studio/openapi.json - MCP endpoint (Streamable HTTP, POST): https://aisquare.studio/mcp - Sitemap: https://aisquare.studio/sitemap.xml - Blog feed (RSS, full posts): https://aisquare.studio/blogs/rss.xml - Any page on this site also serves markdown: append `.md` to its path (the homepage is `/index.md`) or send `Accept: text/markdown`.