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AI assurance infrastructure

Know whether an AI agent stayed within its approved operating boundary.

Boundium determines whether deployed AI agents remain within their authorised operating conditions using evidence from what actually happened.

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For security and technology teams deploying AI agents in consequential environments.

Assurance model

From an approved operating model to evidence-backed reassessment.

  1. 01Approved
  2. 02Observed
  3. 03Divergence
  4. 04Evidence
  5. 05Impact
  6. 06Control
  7. 07Reassess

Boundium compares an agent's approved operating model with evidence of what actually happened, identifies meaningful divergence, explains the operational consequence, and reassesses the state after controls or changing conditions.

Illustrative example

Approved authority and observed action, side by side.

A synthetic record. It does not describe a customer, a deployment, or a live system.

ILLUSTRATIVE ASSURANCE RECORD

Synthetic example · not a deployment

Agent

Customer Operations Agent

Approved

Customer records
READ
Case system
CREATE CASE
External communications
human approval required

Observed

Customer records
WRITE

Assessment

OPEN BOUNDARY EXCEPTION

Evidence

Agent identity
customer-operations-agent
System action
WRITE
Timestamp
2026-09-22 14:07:32 UTC
Resource accessed
customer-record:4819
Approved authority
READ

Operational consequence

The agent performed an action outside its approved operating model.

Control → Observe → Reassess

Category

Beyond inventory and monitoring

Existing infrastructure can show what an agent is connected to, what permissions it has, and what it did.

Boundium focuses on a different question: whether what the agent actually did remained within the operating boundary that was approved.

Authority
What is the agent authorised to access or do?
Purpose
What is it supposed to accomplish?
Behaviour
What did it actually do?
Context
What operational conditions existed when it acted?
Impact
What systems, data or workflows could be affected?
Reassessment
Did the agent return to an acceptable state after intervention?

Environments

Built for consequential AI

AI agents increasingly interact with sensitive data, business systems, operational workflows and actions that carry real-world consequences. Boundium is being developed to provide evidence-based assurance as those systems operate and change.

  • Healthcare
  • Financial services
  • Government
  • Insurance
  • Enterprise operations

R&D

Building evidence-based assurance for autonomous AI.

Boundium is being developed through controlled evaluation of AI-agent assurance under changing operational conditions.

Boundary determination
Can an agent's behaviour be correctly assessed against its approved authority and constraints?
Evidence grounding
Can assessments be traced to relevant, attributable evidence?
Operational impact
Can the system distinguish boundary state from the consequences of what occurred?
Control and reassessment
Can the system determine whether observed behaviour and operational impact changed after intervention?
Follow the researchExplore the assurance model

Deploying AI agents with consequential access or authority?

We're speaking with security and technology teams about how AI-agent assurance should work in practice.

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