1
Define purpose and scope
Studio setupUse caseRisk tierSuccess criteriaAutonomy level
The problem
Why agents stay stuck in pilot.
Grounded
It sounds right and is wrong.
Nothing checked against your own data.
Explainable
Logs show what, never why.
No reason a reviewer can sign off on.
Bounded
Rules are checked after the fact.
The customer sees the breach first.
Learning
The same fix, made twelve times.
Nothing carries over between teams.
Accountable
No evidence when the auditor asks.
Proof rebuilt by hand, weeks later.
Why now · 2026
If frontier labs can’t contain their own agents, your pilot needs proof.
Four public incidents and responses from the last ninety days.
Hugging FaceAgents under test at OpenAI broke out of their sandbox and reached Hugging Face production.Incident16 Jul 2026UK AI Security InstituteLogged 19 unsanctioned agent actions against real people and organizations.Incident4 Aug 2026Anthropic and AccentureCommitted $2B to independent AI evaluation.Response18 Sep 2026UN Scientific Panel on AIWarned that traditional safeguards for AI agents are unravelling.Response21 Sep 2026
AI
Artificial intelligence
+
Human judgment
Your experts
=
Collective intelligence
Together
AI and your experts are each necessary but not sufficient. Together, they earn trust.
How it works
Eight steps to a trustworthy agent fleet.
Trustworthy agent fleet
Built in AISquare Studio
Grounded · Explainable · Bounded · Learning · Accountable
2
Ground in collective intelligence
Collective Intelligence GraphEnterprise connectorsReasoning graphTacit knowledgePolicies and SOPs
3
Compose Studios
StudiosDomain lensesExpert reasoningRuntime contextOne Studio, many agents
4
Choose model and tools
Model-agnosticAny LLM or open-weightTools and APIsMCP serversReasoning memory
5
Set the rulebook
Policy EngineImport from Drive, OPA, or fileEU AI Act, NIST, ISO 42001Block, improve, warnHuman-owned rules
6
Show the work
RML reasoningClaimsAssumptionsEvidenceHallucination flags
7
Learn and evolve
LearningsDrift detectionCost and latency insightsDraft rule, human approvesFleet-wide learning
8
Audit and attest
Audit and AI BOMEvery action signedImmutable recordin-toto, DSSE, PDFIndependent TSI verification
The platform
Every part, one trust layer.
In every agent run
Trust at every layer of the run.
Context management
Before it answers
Does it know how we work?
It answers from your policies, SOPs and your experts' judgment, not just the prompt.
Explainability
As it reasons
Why did it decide that?
Every answer shows its reasoning and the sources behind it.
Runtime guardrails
Before it acts
Will it break one of our rules?
Your rules are checked before the action, not in next quarter's report.
Human-in-the-loop
When it is unsure
Who signs off on the risky ones?
Your people, with the reasoning in front of them.
Continuous learning
After a correction
Will it make the same mistake again?
Fix it once, approve it, and every agent learns it.
Audit and compliance
After every decision
Can we prove it later?
Every decision is recorded and signed, ready when the auditor asks.
From capable intern to trusted colleague: this is how agents earn real work.
Adoption
Wrap your own agents. No rebuild.
Point existing agents at AISquare with one environment variable through the proxy, or wrap your framework with the SDK for hooks on model and tool calls. It sits next to your stack and runs in your cloud.
Runs in your cloudSDK or proxyAny LLM or open-weight modelUses the permissions you set
Models
OpenAIAnthropicGoogle GeminiMeta LlamaMistral AI
Agent frameworks
LangChainLangGraphCrewAIAutoGenMCP
Clouds
AWSMicrosoft AzureGoogle Cloud
Any model through the AISquare SDK. Logos are trademarks of their owners.
Independent verification
AISquare improves your agents.
TSI measures them.
The Trust and Safety Institute is a nonprofit that convenes industry, engineering and research leaders to define trust standards for AI agents, and publishes them openly. AISquare is built to meet them. Any agent can be measured, whether or not it runs on AISquare.
Learn more about Trust and Safety Institute1TSI
Measure against the Trust Stack
2TSI
The gaps show where it falls short
3AISquare
AISquare teaches it from your experts
4Together
The rating rises and autonomy expands
FAQ
Answers for teams putting agents into production.
What is a collective intelligence platform for AI agent trust?
AISquare combines AI with your experts' judgment so agents can be trusted with real work. It grounds every agent in your knowledge, explains its reasoning, enforces your policies while it runs, learns from corrections, and keeps a signed record of every decision. Any agent builder can use it with the agents they already have.
Does AISquare provide AI agent observability?
Yes, and it goes further. Every agent run and decision is recorded, like an observability tool. AISquare also shows why the agent decided what it did, checks that reasoning, and enforces your rules before the action, instead of only reporting after the fact.
How does AISquare explain AI agent reasoning?
Every decision is broken into claims, assumptions and evidence using RML, AISquare's reasoning format. A reviewer can see why the agent acted, not just what it did, and sign off on reasoning they can check.
How does AISquare detect hallucinations in AI agents?
Each claim an agent makes is checked against your own data and policies. Claims with no supporting evidence are flagged before they become decisions, so a confident but wrong answer does not reach a customer.
What are runtime guardrails and policy enforcement for AI agents?
Runtime guardrails are rules checked while the agent runs, before an action happens. AISquare's Policy Engine blocks, improves or warns on each action against policies your team owns, rather than finding the breach in a later report.
How does human-in-the-loop review work for AI agents?
Risky or uncertain decisions are escalated to your people with the reasoning attached, ready to approve or correct. Humans stay in control of what agents are allowed to do and learn.
How do AI agents learn continuously from corrections?
A correction becomes a draft rule. Once a human approves it, every agent in the fleet inherits it, so the same mistake is not fixed twelve times by twelve teams.
What audit trail does AISquare keep for AI agent decisions?
Every decision is recorded and signed with its inputs, reasoning and approver, and can be exported when an auditor or regulator asks. Nothing has to be rebuilt by hand weeks later.
Does AISquare work with any agent framework, model and cloud?
Yes. Point existing agents at the AISquare proxy with one environment variable, or wrap your framework with the SDK. It works with OpenAI, Anthropic, Gemini, Llama and Mistral, with LangChain, LangGraph, CrewAI, AutoGen and MCP, and runs in AWS, Azure or Google Cloud with the permissions you set.
Get your agents out of pilot.
Start your journey with AISquare
Wrap the agents you already run and see every decision grounded, explained and proven, in your own environment.