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's collective intelligence.
Agent Reasoning
ExplainedEnterprise Context
ReusablePolicies and Exceptions
Policy ApprovedExpert Judgement
Human CorrectedGOVERNED SYSTEM OF RECORD
Every decision, approval, and correction becomes shared memory that people and agents reuse.
Better Agent Reasoning
Sharper Human Judgement
Reusable organizational learning
95% accurate per step becomes 36% over twenty. Trust has to be engineered, not assumed.
have agentic AI in production, though 75% say they're adopting it.
Forrester 2026 · Gartner CIO Survey 2026
of enterprises will demote or decommission their AI agents by 2027, over governance gaps found after production incidents.
Gartner, May 2026
Global 1000 firms face lawsuits, fines, or CIO dismissals by 2030 over inadequate agent controls.
IDC FutureScape 2026
have a mature governance model for agentic AI.
Deloitte, 2026
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.
What trust requires
Collective Intelligence Platform
Trust layerWhat you get
Reading your docs isn't the same as knowing your business. An agent's answers can look right and still be wrong.
A single refund request, from the first question to the rule it leaves behind.
Connect
Docs, tickets, CRM, chat, and the decisions humans have already made.
Graph
Context, expertise, policy, and tacit knowledge, linked and kept current.
Studios
Each Studio holds the curated slice a job needs, for your agents and your team.
Run
Rule 4.2 caps refunds at $500, so the $1,250 refund is blocked and escalated.
Reasoning
The blocked decision opens into claims, assumptions, and evidence, then gets signed.
Review
The manager approves the exception and records why.
Learn
The approval becomes precedent and a rule. The next $900 request clears on its own.
Loops
Every exchange between people and agents feeds the same graph, and flows back out.
Humans teach agents
Corrections and approvals become enforced rules.
Agents teach agents
One agent's learning reaches the whole fleet.
Agents inform humans
Failure clusters, drift, and cost surface to the team.
Humans teach humans
Expertise captured once, reused by every team.
Agents that make it to production, and stay there.
You get
Your rules enforced on every decision, so agents stay trusted
Proof the moment you ask, not two weeks later.
You get
A signed record of every agent reasoning, ready to export
Agents learn from every correction, so the same mistake never happens twice.
You get
Corrections that become fleet-wide rules
Fewer tokens. Faster answers.
You get
Answers served from the graph, not rebuilt from scratch every run
AISQUARE IN ACTION
The same reasoning, policy, and memory across every high-stakes workflow your agents run, from code to contracts to customer refunds.
CUSTOMER SUPPORT
A support agent approves a refund that should have been reviewed.
OUTCOME
Attach the policy, the evidence, the customer context, and the escalation path.
Observability stops at tokens and latency. Evals score outputs. Guardrails block. All useful, but none of them shows the reasoning, enforces the policy at run time, and learns from the fix.
Shows what the agent did, after it ran.
Reports after the fact. Doesn't tell you why, or stop anything.
Scores the output, in the lab.
Grades quality, not the cause behind a decision.
Blocks bad words, in and out.
Can't enforce the outcome. Can't nudge it to the right policy and output.
AISquareExplains the agent reasoning. Enforces the rule. Learns from the fix.
Inside every run, before the action lands, across every agent.
You cannot trust what you cannot inspect. A decision you can't break into claims, assumptions and evidence is one you are taking on faith.
An agent that carries nothing forward never earns more responsibility than it had on day one, and neither does the organization running it.
Agent trust
Without Collective IntelligenceIllustrative
Cost per run
Without Collective IntelligenceIllustrative
When agents do the work, experts can drift into rubber-stamping. Studios keep them in the loop. What one expert knows becomes an expert note the whole team can learn from, grounded in the same graph your agents run on.
An expert adds what they know
Priya, Support lead
added a file to the Studio
The Studio turns it into an expert note
When an outage causes a refund request
Confirm the SLA breach, then approve above the cap with a recorded rationale.
Cited: playbook p.4 · rule 4.2a
A new teammate learns from it
Sam, joined two weeks ago
handles an outage refund the way Priya would
The SDK or proxy for production agents, the CLI for coding agents. Every run lands in the same reasoning graph.
$ pip install "aisquare[explainability]"
import aisquare.explainability as sdk
from aisquare import GovernedAgent
sdk.init_from_env()
agent = GovernedAgent.from_agno(
my_agent, rule_book="refunds", enforce=True
)Wrap your 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.
View docs$ pipx install 'aisquare-cli[tui]'
$ aisquare agents connect claude-code
$ aisquare doctor
$ AISQUARE_ROLE=planner claude
$ aisquare board -wRun 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.
View docsAnthropic, OpenAI, Azure OpenAI, and Gemini through the proxy. Agno, LangChain, and custom agents through the SDK. Claude Code and Codex from the command line. Everything flows into one graph and back out to Studios, MCP, and the API.
View docsModels
Claude
OpenAI
Azure OpenAI
GeminiAgent frameworks
Agno
LangChainCustom agentsCoding agents
Claude CodeCodexData & knowledge
Tools & systems
AISquare works with your team to turn high-value agent workflows into a repeatable system for reasoning, policy, review, and audit.
Bring AISquare into your existing consulting or implementation motion.
Work with our forward-deployed team on your first governed AI workflow.
Use AISquare docs, SDKs, and APIs with your own engineering team.
AI moves fast. Human judgment makes it trustworthy. AISquare connects agents, context, policy, and people so every decision can be understood, guided, and defended.
