Enterprise AI agent assurance

A complete assurance system for enterprise AI agents

AI Agent Certify connects agent scope, evaluation, evidence and human decisions so enterprises can review a release and maintain assurance as the system changes.

For: AI leaders, engineering, security, risk, compliance and business owners

Confirm supported coverage for your engagement. The enterprise workspace is available; integrations and private connections require technical approval. Policy imports remain a future option.

Know what you are approving

  1. A clear release boundary

    Identify the version, intended use and permissions behind the decision. Reviewers can see what the assessment covers and what remains unresolved.

  2. Reviewable evidence

    Connect findings to selected requirements and evaluation cases. Challenge conclusions without reconstructing the entire run.

  3. Responsible change

    Preserve historical results while new signals, decisions and versions receive their own records.

Different roles, one accountable process

  1. Business owners

    Define permitted outcomes and consequences of a mistake. Own the decision about whether the use case should proceed.

  2. Engineering and security

    Describe the system, prepare controlled evaluation access and investigate technical findings.

  3. Risk, compliance and reviewers

    Confirm scope, inspect evidence and record decisions or conditions. The platform does not replace their authority.

Start with the obligations that apply

An Assurance Plan links selected requirements to controls and evaluation criteria. Future policy imports support review and mapping; requirements are not silently adopted.

  1. Company managed policies

    Bring approved uses, prohibited actions, data boundaries and escalation requirements. Assign each requirement an owner and version.

  2. External requirements

    Select applicable regulations, major standards and industry requirements with qualified reviewers. Inclusion does not establish conformity.

  3. Red team practices

    Choose adversarial scenarios that exercise actual tools, permissions and operating context.

From registration to reassessment

  1. Register the agent and exact version

    Identify code, configuration, model dependencies, tools and the release owner.

  2. Define assurance scope

    Record intended users, tasks, environments and exclusions.

  3. Apply policies and external requirements

    Confirm which requirements apply and why.

  4. Prepare evaluation criteria

    Specify expected behaviour, evidence needs and escalation conditions before testing.

  5. Evaluate and red team behaviour

    Collect observations from approved cases and adversarial scenarios.

  6. Review evidence and decisions

    An authorized person considers failures, gaps and operating conditions.

  7. Produce the Assurance Scorecard

    Summarize results, findings, coverage, limitations and canonical evidence.

  8. Publish an eligible Agent Passport

    Select a public safe projection after the appropriate review and publication decision.

  9. Monitor material change and drift

    Compare relevant signals with the approved operating context.

  10. Investigate or reassess

    Determine whether remediation, new evaluation or an updated artifact is required.

Evidence informs a decision; people authorize it

An evaluation answers a bounded question about observed behaviour. It does not decide whether a legal duty has been met or whether an enterprise should accept a risk. Reviewers need criteria, observations, known gaps and potential consequences.

The intended evidence model connects findings to the exact version, Assurance Plan, case and human decision. Private evidence follows agreed access boundaries. Review responsibility cannot be delegated to a dashboard label.

Two artifacts with different audiences

Illustrative artifact structure. No operational results are shown.

  1. Assurance Scorecard

    The assessment summary for reviewers: results, findings, coverage, limitations and evidence references.

  2. Agent Passport

    A shareable record with approved scope and relevant status. It excludes private evidence and does not turn the assessment into statutory certification.

Keep historical assessment truth intact

A new signal may reveal a changed prompt, new population, failed dependency or revised policy. Investigate its significance before changing the current operating decision. A historical Scorecard records what was assessed at that time.

The same assurance engine governs work started through the workspace, API, CI/CD or approved private connection. Access changes the client or execution boundary, not the evaluation authority.

Adapt the policy engine without losing history

Treat coverage as versioned selections. When a requirement changes, review applicability, map it to controls and evaluations, and decide which agents need reassessment. Keep earlier mappings available to explain previous decisions. Future policy imports require review before use.

One assurance engine, five enterprise delivery choices

Access changes where work starts and which environment it reaches. It does not create a different evaluator. Confirm availability and deployment boundaries during scoping.

01

Enterprise workspace

Bring scope, Assurance Plans, evidence and human review into one managed workspace.

current
02

CI/CD and GitHub assurance gate

Connect assurance decisions to the version your team intends to release.

available on request
03

Customer VPC connection

Connect customer environments with agreed access and evidence boundaries.

available on request
04

Private deployment with backend connector

Keep approved execution in a private environment through a backend connector, with agreed evidence boundaries.

available on request
05

Private deployment through API

Connect a privately deployed agent through an approved API. The same assurance engine governs evaluation and evidence.

available on request

The enterprise workspace is available. Other delivery options require engagement-specific technical approval before deployment. Policy imports remain a future option.

Questions about enterprise ai assurance platform

Do API and GitHub requests use different evaluators?

No. The intended architecture uses the shared assurance engine. GitHub is a client of the Gate API; access methods do not create separate evaluation rules.

Can assessment automatically approve a legal obligation?

No. Evaluation supports review. Legal interpretation, risk acceptance and deployment authority remain with the appropriate people.

Put your next agent decision on a clear evidence basis

Discuss the agent, its operating boundaries and the assurance work your enterprise needs.

Request a demo