HyperMindQ
Praetor by HyperMindQ · accountability for AI agents

Every action an agent takes, answered for.

Praetor discovers the AI acting in your name, encodes what each agent may do, enforces it before anything executes, detects what goes wrong and explains every decision to the people who must answer for it.

01 Discover02 Govern03 Enforce04 Detect05 Explain
Praetor · payments agentSample data
Mandate · family office
  • M-2.1Payments up to S$50,000 to approved counterparties
  • M-2.2Above S$50,000: CFO approval within 4 hours
  • M-3.1New counterparties: screening and approval first
  • TD-7.1Trust T-02 distributions need trustee consent
  • G-1Agents never change their own mandate
The agent attempts
Checks before execution
    Illustrative demonstration. Names, rules and amounts are examples.Allowed 0 Held 0 Blocked 0
    The problem

    Agents now act. Accountability has not caught up.

    AI has moved from answering questions to taking actions: paying invoices, moving data, changing records. Each step up in autonomy multiplies the actions taken without a person, while the number of people available to review them stays the same.

    Today ACCOUNTABILITY GAP Actions taken without a person What people can review Runtime oversight MoreLess
    Actions taken by AI without a personHuman capacity to reviewOversight built into the runtime
    Conceptual illustration, not to scale. It shows a direction, not a forecast or measured data.

    Why this matters on the path to AGI →

    One platform

    Discover. Govern. Enforce. Detect. Explain.

    Five functions in one system, so the record of what an agent was allowed to do and the record of what it did are the same record.

    01

    Discover

    What AI is acting in our name?

    Builds a live inventory of the agents and AI-enabled tools operating across the organisation, including AI inside vendor software, with an owner for each.

    02

    Govern

    What may each agent do?

    Turns policy, delegation maps, trust deeds and mandates into machine-readable rules: what an agent may execute, what it may only recommend, and what it must never touch.

    03

    Enforce

    Is this action within the mandate?

    Praetor sits between agents and the systems they act on. Every action is checked before it executes: allowed, held for a named person, or blocked.

    04

    Detect

    Is something going wrong?

    Watches behaviour across agents for patterns no single rule would catch: repeated near-limit actions, unusual timing, attempts to widen scope, and signs of manipulated instructions.

    05

    Explain

    Who decided, on what basis?

    Every decision, including every refusal, produces a record a trustee, auditor, board or regulator can read: the action, the rule applied, the outcome and the accountable person.

    What Detect and Explain reveal feeds back into Govern, so mandates improve with use.

    How each function works →

    Architecture

    Between your agents and everything they touch

    Praetor sits in the path of every action. Agents keep their speed. People keep their authority.

    AI AGENTSSYSTEMS OF RECORD Payments agentReporting agentAI inside vendor softwareYour own agentsBanks and custodiansAccounting and ERPDocument and data storesEmail and messaging PraetorDISCOVERIdentity and inventoryGOVERNMandate engineENFORCEPre-execution gateDETECTBehaviour monitoringEXPLAINDecision records People: approvals, owners, trustees HOLDDECIDE Boards, auditors, regulators Policies and delegation maps

    Simplified view. Integrations and deployment are agreed for each client environment.

    Who it serves

    Built for the people who answer for AI

    Boards and directors

    Know which decisions have been delegated to AI, within what limits, and who answers for each.

    Chief operating and risk officers

    Let agents take routine work without losing control of what they can touch.

    Trustees and family offices

    Keep automated actions on family and trust assets inside the deed, the mandate and the consents.

    Auditors and regulators

    Read a complete, linked record of what each agent did and why it was allowed.

    How it was built

    From brain-inspired research to a runtime for accountability

    HyperMindQ grew out of research into brain-inspired, event-driven computing, and from advisory work on how organisations should delegate decisions to AI. Spiking neural networks respond to each event as it happens. Praetor applies the same principle to oversight: every action is checked when it occurs, not in a review weeks later.

    Follow how the idea was conceived and built →

    Briefings

    See Praetor on your own workflows

    A confidential briefing for your board, risk or operations team. We walk through one workflow and show what Praetor would allow, hold and block.