Evidence state: Framework. Methodology framework only. No completed HoopAI benchmark, customer result, model comparison, or empirical performance finding is reported. Illustrative examples are not observed results.
Assurance status. This is a proposed pre-launch protocol. No completed HoopAI release test, reliability result, security assessment, rollback proof, or customer-impact finding is reported.
The protocol asks for evidence that a specific workflow can enter a bounded launch state. It does not attempt to certify a model or eliminate operational uncertainty.
Release evidence is therefore conditional, versioned, and open to later operational challenge.
Write the launch claim in one sentence
The claim should name the workflow, eligible users, environment, inputs, outputs, actions, review state, and traffic boundary proposed for release.
An example claim might allow reviewed draft summaries for one internal team. It should not silently include autonomous messages, record changes, or other regions.
The research question follows: can that exact workflow handle representative and failure cases while preserving permissions, review, observability, and recovery?
Build an assurance map
Diagram the trigger, data intake, source retrieval, model call, tool use, output, review, downstream action, log, monitor, pause control, and recovery path.
For each node, record its owner, version, expected evidence, failure signal, and containment action. Include third-party services and manual steps.
Mark trust boundaries where data, identity, or authority changes. A response test cannot reveal a permission error at an integration boundary.
List assumptions that the launch depends on, such as source freshness, vendor limits, reviewer coverage, and destination-system behavior.
Organize testing into four lanes
Lane one: component behavior
Test source selection, prompt logic, policy checks, permissions, tool parameters, output parsing, and user-interface states independently where isolation is possible.
Component checks should establish clear oracles. They are diagnostic evidence, not a substitute for an end-to-end rehearsal.
Lane two: integration contracts
Verify identities, scopes, schemas, timeouts, retries, idempotency, and destination states using controlled records. Confirm that failures do not create duplicate actions.
Exercise expired credentials, unavailable services, malformed responses, rate limits, and partial writes. The expected outcome may be a safe stop rather than graceful completion.
Lane three: scenario rehearsal
Run complete task cases through an isolated environment. Include routine, incomplete, conflicting, sensitive, prohibited, multilingual, and high-volume conditions.
Review the artifact and the actual state transition. A correct explanation is not enough if the wrong owner, message, field, or permission follows.
Lane four: recovery exercise
Trigger agreed failure conditions and ask operators to detect, pause, locate affected records, correct state, and resume only after approval.
Record the evidence available at each step. A rollback control that exists but cannot be found or interpreted under pressure is not operationally demonstrated.
Sample across the workflow risk surface
Use a matrix of input condition, user role, permission, source state, action type, integration status, and consequence. Map every test to the launch claim.
Include negative tests for users, sources, tools, and actions outside scope. A system should fail closed where the release contract requires it.
Keep a versioned regression suite plus unseen scenarios. Regression proves known behavior; fresh scenarios probe whether the test set has become too familiar.
Use synthetic or dedicated test records unless governed production-like data is necessary and approved. Prevent test outputs from reaching real customers.
Define the oracle and severity model
Each case should specify permitted outputs, forbidden outputs, expected action, required log, reviewer state, alert, and recovery step.
- Stop-ship: unauthorized communication, restricted-data exposure, impermissible record change, or inability to pause a harmful path.
- Launch-limiting: missed escalation, hidden failure, unreliable source trace, or recovery that requires undocumented expertise.
- Correctable: bounded quality defects with no material action or data consequence.
Severity should follow consequence and reversibility, not visual prominence. A subtle wrong field can be more serious than an obviously weak paragraph.
Require accountable human sign-off
Assign reviewers for product, operations, security, privacy, legal or compliance, and the affected business process according to the actual risk.
No single aggregate score should override a stop-ship gate owned by an accountable reviewer. Record acceptance, rejection, conditions, and unresolved limitations.
Have a second reviewer inspect evidence for critical cases and witness the pause or recovery exercise. Screenshots of a control are not proof it changed system state.
Prepare an evidence package
The package should include the launch claim, system map, version manifest, case inventory, results, logs, reviewer decisions, known issues, rollback steps, and monitoring plan.
Record deviations from the protocol and tests not run. Missing evidence should remain visible rather than converted into a pass.
NIST's SSDF provides secure-development context, while the AI RMF and Generative AI Profile frame AI risk. None is a HoopAI release certificate.
Translate evidence into a bounded release
A pass permits only the tested boundary. If drafting passes but action control fails, release can remain draft-only or be deferred.
Set post-launch sampling, alert thresholds, owners, incident routes, and automatic pause conditions before exposure. Pre-launch testing cannot cover every future input.
Reopen the gate after material changes to model, prompt, data, retrieval, permissions, tools, integrations, review, or traffic assumptions.
Residual uncertainty
Test environments differ from production volume, timing, dependencies, and adversarial pressure. Rare event combinations may not appear in planned cases.
A successful recovery exercise does not guarantee every incident can be reversed. Third-party behavior and source data can change after sign-off.
This protocol does not establish security, compliance, reliability, or customer benefit. Those claims require evidence matched to their own definitions.
Disclosure for future publication
Any later report must identify the release boundary, environment differences, sample, versions, oracles, reviewers, skipped tests, failures, mitigations, and residual risks.
This paper remains a proposed gate with no completed HoopAI findings. Readers should not infer that described controls exist until verified product documentation says so.
Methodology
Map the trigger, input, source retrieval, model call, output, review, downstream action, logging, monitoring, and rollback. Build controlled test records for routine, incomplete, conflicting, sensitive, and prohibited cases. Test components separately, then test the end-to-end path in an isolated environment. Record versioned expected outcomes, owner sign-off, known limitations, and explicit pause conditions.
Score data handling, source fidelity, output quality, action correctness, permission enforcement, escalation, observability, and recovery. Treat unintended outbound communication, unauthorized record change, or hidden failure as critical. A second reviewer should inspect critical-case evidence and confirm that rollback or disable behavior was actually exercised.
Limitations
- Test environments can differ from production integrations, volume, timing, and permissions.
- Rare combinations of events may not appear in the release set.
- A successful rollback exercise does not guarantee recovery from every incident.
- This protocol does not certify security, reliability, compliance, or customer impact.
Sources
- NIST SP 800-218, Secure Software Development Framework 1.1: Primary secure-development reference for release evidence and supplier questions. It is not a security assessment of HoopAI or any vendor.
- NIST AI Risk Management Framework: Voluntary risk-management reference. It informs terminology and review questions; it does not validate HoopAI or any result in this framework.
- NIST AI 600-1, Generative AI Profile: Public generative-AI risk reference. It provides no evidence of a HoopAI implementation or satisfied control.
Notes
Methodology framework only. No completed HoopAI benchmark, customer result, model comparison, or empirical performance finding is reported. Illustrative examples are not observed results.