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Engineered.

AI Agent Reliability Assessment

For teams running AI-agent workflows whose decisions or tool actions can affect a business process.

We assess evidence, tool permissions, human approvals and failure recovery.

Route a decision. A demonstration of the rule, not a live system.

Illustrative request:

Contract clause flagged

Rejected and recorded
A person decides
A person approves

All three proven

ActionThe system proceeds and records its evidence.

Contract clause flagged. The action has an external effect, so a person approves it before it runs.

No decisions routed on this visit.

    Illustrative decision route. A request proceeds only through evidence, consistency and permission gates. A person approves any external effect. The examples and states are fictional; this is not a customer system, assessment result or production activity.

    A bounded first engagement.

    For the people responsible

    One workflow. Clear boundaries.

    For teams whose AI-agent decisions or tool actions can affect a real business process.

    AI Agent Reliability Assessment

    $1,000 per agreed workflow.

    Fixed scope. Typical delivery in 3–5 business days after agreed materials are available.

    Request a private briefing
    We examine
    Evidence, decision boundaries, tool permissions, human approvals and failure recovery.
    The assessment

    Find where the workflow can be wrong, unsafe or difficult to verify.

    One agreed workflow, assessed using supplied specifications, representative examples and, when available, a bounded test environment.

    What you receive
    Map
    Workflow, inputs, decisions, tools and approval points.
    Evidence
    Documented scenarios, observed findings, severity, confidence and reproduction steps where possible.
    Priorities
    Limitations stated plainly; engineering remedies ordered by risk.
    Scope and limits

    Not a penetration test, security certification, regulatory approval or guarantee against incidents. The initial assessment does not require unrestricted production access.

    Evidence, not borrowed claims.

    A reproducible, fictional assessment exercise shows how a workflow is tested against an explicit policy.

    It is method evidence, not a client result or a benchmark of a commercial AI system.

    • Testable scenarios and expected outcomes.
    • Observed mismatches are separated from hypotheses.
    • Unknowns stay explicit until tested.
    Review the 30-case synthetic example

    Systems and research.

    The assessment is the initial offer. Engineering, implementation and research are separately scoped.

    REPRODUCIBLE / SYNTHETIC

    Insurance workflow reliability lab

    30 fictional cases compare an incomplete toy simulator with an independent oracle. Planted faults demonstrate the method, not customer or model performance.

    How an assessment proceeds.

    1. 1

      Agree the boundary

      Choose one workflow, decision and available evidence.

    2. 2

      Map and challenge

      Trace inputs, decisions, tools and approvals against bounded scenarios.

    3. 3

      Report what was observed

      Separate evidence, inference and untested areas; prioritize practical remedies.

    Any production tests require explicit authorization and a separately agreed scope.

    Start with one workflow.

    A short private briefing establishes whether the assessment fits the system and the evidence available.

    Describe the workflow at a high level. Do not send credentials, records or client data before an agreed secure process.

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