AUTO_PROCEEDLow-risk and verified
Source-backed inputs and deterministic checks pass inside a pre-authorized boundary.
SYNAPSE AUTOMATE / PUBLIC OPERATING STANDARD / 2026
Reliable AI workflows do not hide uncertainty.
A public operating standard for source-backed, rule-tested, human-governed enterprise AI workflows. The standard defines ten control domains, four decision states, and the evidence required before consequential automation is allowed to expand.
Core principle: A workflow is not reliable because its output looks complete. It is reliable when it can show what it knows, what it does not know, where critical values came from, which rules were applied, who owns authority, and when the system must stop.
DECISION SYSTEM
Four states. One explicit authority model.
Human oversight is not a generic “review everything” step. The workflow chooses a defined state from evidence, risk and authority boundaries.
HUMAN_REVIEWAmbiguous or exceptional
Progress pauses while evidence, duplicate conditions or non-consequential exceptions are inspected.
HUMAN_APPROVALConsequential action
The system prepares the action, but an authorized human retains execution authority.
STOP_ESCALATEEvidence or policy gap
Critical missing/conflicting evidence or lack of a safe rule blocks progression.
TEN CONTROL DOMAINS
Reliability is a system, not a model score.
Each control has a minimum evidence requirement and a fail condition. A workflow cannot pass by compensating for one missing critical control with strengths elsewhere.
R1
Source integrity & provenance
Critical fields are traceable to an approved source or remain explicitly unknown.
FAIL: unsupported critical value is presented as fact.
R2
Unknown preservation
Absence of evidence survives extraction, validation and UI display without plausible substitution.
FAIL: missing evidence becomes a fabricated value.
R3
Structured output & parse gate
Machine-consumed output must pass a declared schema and bounded retry policy.
FAIL: invalid output reaches downstream systems.
R4
Deterministic validation
Rules that can be deterministic are implemented and tested as deterministic rules.
FAIL: model judgment replaces an enforceable business rule.
R5
Decision authority & human gate
Consequential actions expose an owner and explicit approve/edit/reject/escalate control.
FAIL: consequential external action bypasses required authority.
R6
Least agency & tool boundary
AI components receive only the minimum functions, permissions and write scope required.
FAIL: unnecessary write/delete/broad external capability is available.
R7
Observability & decision log
Source, extracted value, rule result, state, human action and final disposition can be reconstructed.
FAIL: the team cannot explain why the workflow advanced or stopped.
R8
Regression & adversarial testing
Normal, malformed and adversarial cases are versioned with expected outcomes and visible failures.
FAIL: behavior changes without rerunnable evidence.
R9
Incident stop & recovery
The workflow defines stop triggers, incident ownership, containment and evidence required to resume.
FAIL: execution continues after a known critical control failure.
R10
Approved-result measurement
Success is measured at the approved business result: time, critical errors, rework and cost.
FAIL: automation percentage is the only success metric.
PUBLIC PROOF BASELINE
Evidence is labeled by what it actually proves.
These results are synthetic and reproducible. They demonstrate control behavior, not production accuracy, regulatory compliance or ROI.
10/10deterministic cases
5/5critical rule families
10/10structured extraction
10/10ingestion cases
50/50provenance cases
100/100regression cases
5/5mutations detected
Local verification run: 18 Sep 2026. All datasets are synthetic/non-sensitive. Public proof packages are designed to be rerunnable.
EVIDENCE LADDER
No marketing shortcut across evidence levels.
Public language is constrained by the highest evidence level actually achieved.
| Level | Evidence | Allowed public wording |
|---|---|---|
| E0 | Concept only | No result claim. |
| E1 | Synthetic demonstration | “Synthetic example” / “demo”. |
| E2 | Reproducible public test | “Re-runnable public test” with scope and limitations. |
| E3 | Controlled pilot | Measured pilot result with scope, period, sample and limitations. |
| E4 | Production customer result with permission | Case result with methodology and explicit permission. |
PILOT MINIMUM
Start with one workflow, not an AI transformation promise.
Expansion is earned after one workflow passes both the reliability gate and the business gate.
01
One outcome
A measurable approved result and baseline.
02
One source boundary
Approved sources and explicit unknown behavior.
03
One authority boundary
Who can review, approve, reject or escalate.
04
One stop rule
A fail condition agreed before implementation.
Commercial metric: optimize for time to approved result + critical error risk, not automation percentage.
REFERENCE ALIGNMENT
Informed by recognized public frameworks. Not disguised certification.
The standard uses external references as risk/governance inputs. Synapse does not claim ISO certification, NIST approval or OWASP endorsement.
NIST AI Risk Management FrameworkVoluntary AI risk-management framework and current revision context.NIST AI 600-1 - Generative AI ProfileCross-sectoral GenAI risk profile, including provenance and trustworthiness considerations.ISO/IEC 42001:2023 - AI management systemsManagement-system approach to AI governance, risk, accountability, transparency and continual improvement.OWASP GenAI / LLM Top 10Security risks including improper output handling and excessive agency.
TÜRKÇE ÖZET
Güvenilirlik, “AI doğru cevap verdi mi?” sorusundan daha büyüktür.
Ne biliyor?
Kritik bilgi kaynağa bağlı mı, yoksa bilinmiyor olarak mı kalıyor?
Hangi kural uygulandı?
Deterministik kural model yorumundan ayrılmış mı ve test edilmiş mi?
Yetki kimde?
Sonuç doğuran eylemde insan onayı gerçekten işlemi durdurabiliyor mu?
Ne zaman duruyor?
Eksik/çelişkili kanıt veya güvenli politika yoksa akış görünür şekilde duruyor mu?
ASSESS ONE WORKFLOW
Turn one consequential process into a measurable reliability pilot.
Define the source boundary, deterministic rules, human authority and approved-result metric before implementation.