ms-ai-architect/skills/ms-ai-governance/references/responsible-ai/continuous-improvement-feedback-loops.md

Continuous Improvement and Feedback Loops - Iterative Governance

Last updated: 2026-06-19 Status: GA Category: Responsible AI & Governance

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Introduksjon

Continuous improvement through feedback loops er et kjernekonsept i moderne AI-governance. Dette handler om systematisk innsamling, analyse og anvendelse av tilbakemeldinger fra produksjonssystemer, brukere og domeneksperter for å forbedre AI-kvalitet, sikkerhet og alignment over tid.

Hvorfor dette er kritisk:

• AI-modeller degraderer over tid (model drift) grunnet endringer i data og brukeradferd
• Feedback fra reell bruk identifiserer problemer som ikke fanges i testing
• Iterative forbedringer basert på produksjonsdata bygger mer pålitelige AI-systemer
• Compliance og etiske standarder utvikler seg og krever kontinuerlig tilpasning

Microsofts tilnærming: Microsoft implementerer feedback loops gjennom hele AI-livssyklusen – fra utvikling med evaluation datasets til produksjonsmonitoring med automated scorers og human review. Målet er å skape en lukket syklus der hver interaksjon bidrar til systemforbedring.

Kjerneprinsipp:

"Every production interaction becomes an opportunity to improve" – Microsoft MLflow Documentation

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Kjernekomponenter

1. Production Data Collection

Tracing og logging:

• MLflow Traces / MLflow 3 GenAI: Fanger detaljerte execution traces med inputs, outputs og alle mellomsteg for hver interaksjon. (Verified MCP 2026-04)
  • MLflow 3 GenAI introduserer ny Assessment-datamodell med to typer:
    • Feedback assessments: evaluerer faktisk output (ratings, kommentarer — "Var agentens svar bra?")
    • Expectation assessments: definerer ønsket/korrekt output (ground truth — "Hva burde ha blitt produsert"); brukes til å bygge evalueringsdata
  • Tre innsamlingskilder: utvikler (dev), domeneekspert (via Review App), sluttbruker (produksjon)
  • mlflow.log_feedback() API for å knytte bruker-rating og kommentarer til spesifikke traces
  • Ny kapabilitet: Genie Code for naturspråk-analyse av trace-data
  • Integrert tracing for Databricks agentic applikasjoner
• Azure Monitor & Application Insights: Logger operational metrics, latency, error rates
• Model Data Collector: Automatisk innsamling av production data for ML-modeller
• Azure AI Content Safety logs: Sporer content moderation events

Hva samles inn:

• User prompts og model completions
• Confidence scores og metadata
• Latency og performance metrics
• Error logs og exception traces
• User feedback (thumbs up/down, ratings)

Confidence: Verified – MLflow Tracing, Azure Monitor

2. Automated Quality Monitoring

LLM-judge based scorers: Microsoft bruker automated scorers (LLM judges) for kontinuerlig kvalitetsvurdering av produksjonstrafikk:

Scorer Type	Hva den måler	Threshold Eksempel
Groundedness	Faktisk forankring i kildedokumenter	Pass rate ≥ 70%
Relevance	Relevans til brukers spørsmål	Pass rate ≥ 70%
Coherence	Logisk sammenheng i svar	Pass rate ≥ 70%
Fluency	Språklig flyt og naturlighet	Pass rate ≥ 70%
Safety	Deteksjon av harmful content	Pass rate ≥ 95%

Continuous evaluation:

• Schedulert evaluering (f.eks. daglig via CronTrigger)
• Real-time scoring av sampled production traffic
• Automated alerts ved threshold violations
• Integration med Azure AI Foundry evaluation tools

Confidence: Verified – Generation Quality Monitoring

3. Human Feedback Integration

Tre typer feedback:

a) End-user feedback:

• Explicit feedback: Thumbs up/down, ratings, rapporterte feil
• Implicit signals: Follow-up spørsmål, avbrutte samtaler, session abandonment
• Feedback attachet til MLflow traces for traceability

b) Domain expert review:

• Manuell labeling av problematic traces via Review App
• Kvalitetsvurdering mot business-specific criteria
• Alignment av automated scorers med human judgment

c) Human-in-the-loop (HITL):

• Approval mechanisms for high-impact decisions
• Reviewer training på AI behavior og vulnerabilities
• Secure review interfaces med Azure Logic Apps / Power Automate

Confidence: Verified – Human Feedback, HITL Security

4. Evaluation Datasets

Curated eval datasets: Feedback loops bygger evaluation datasets fra produksjonsdata:

• Problematic traces: Low-scoring eller user-reported issues
• High-quality traces: Validated positive examples (preservere det gode)
• Edge cases: Sjeldne scenarios som avdekkes i prod
• Regression test sets: Sikre at nye versjoner ikke forverrer ytelse

Golden datasets: Benchmark datasets med kjent kvalitet for consistent testing og model validation.

Confidence: Verified – Evaluation Datasets

5. Model Retraining & Versioning

Retraining triggers:

• Performance degradation under defined KPIs
• Scheduled retraining (high-risk workloads: månedlig; low-risk: kvartalsvis)
• Significant data distribution changes
• New compliance requirements

Versioning best practices:

• Track code, parameters, evaluation metrics per version
• MLflow version management for reproducibility
• Rollback mechanisms for underperforming models
• A/B testing av nye versjoner mot baseline

Confidence: Verified – Model Management

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Arkitekturmønstre

Mønster 1: MLflow Continuous Improvement Cycle (Microsoft-anbefalt)

10-stegs syklus for GenAI apps:

1. 🚀 Production App – Deployed app genererer MLflow traces
2. 👍 👎 User Feedback – End users gir feedback attachet til traces
3. 🔍 Monitor & Score – Automated LLM judges scorer traces kontinuerlig
4. ⚠️ Identify Issues – Trace UI avdekker mønstre i low-scoring traces
5. 👥 Domain Expert Review – Optional: Eksperter labeler problematic traces
6. 📋 Build Eval Dataset – Kuratér problematic + high-quality traces
7. 🎯 Tune Scorers – Align automated scorers med human judgment
8. 🧪 Evaluate New Versions – Test improved versions mot eval datasets
9. 📈 Compare Results – Sammenlign evaluation runs på tvers av versjoner
10. ✅ Deploy or Iterate – Deploy ved forbedring, ellers iterer videre

Verktøy:

• Azure Databricks MLflow 3
• Azure AI Foundry Agent Service
• MLflow Tracing & Scorers

Confidence: Verified – MLflow Continuous Improvement

Mønster 2: AI Builder Feedback Loop (Power Platform)

For custom document processing models:

1. Power Automate cloud flow kjører AI Builder model på production documents
2. Condition check: Hvis confidence score < threshold (f.eks. 70%) → add to feedback loop storage
3. Feedback loop storage: Microsoft Dataverse table "AI Builder Feedback Loop"
4. Model improvement: Data fra feedback loop brukes til retraining
5. Retrain & redeploy: Oppdatert model promoteres til production

Use case: Ideal for document understanding scenarios der low-confidence predictions indikerer behov for mer training data.

Confidence: Verified – AI Builder Feedback Loop

Mønster 3: Platform Engineering Feedback Loop

For infrastruktur og platform-tjenester:

1. Developer feedback: Samle inn pain points (deployment times, tool integration issues)
2. Post-Incident Reviews (PIRs): Root cause analysis etter incidents
3. Prioritize improvements: Agile sprints for iterative enhancements
4. Implement changes: Optimize CI/CD pipelines, integrate developer-friendly tools
5. Monitor impact: Track developer productivity metrics
6. Regular platform reviews: Data-driven assessment av platform health

Observability-Driven Development (ODD): Alle nye services instrumenteres for monitoring/logging fra dag 1, slik at feedback er tilgjengelig umiddelbart.

Confidence: Verified – Observability & Continuous Improvement

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Beslutningsveiledning

Når bruke hvilke feedback mechanisms?

Scenario	Anbefalt Approach	Rationale
Conversational AI (chatbots, copilots)	MLflow Continuous Improvement Cycle + end-user feedback	Høy interaksjonsfrekvens, stor variasjon i queries, behov for human alignment
Non-conversational agents (classification, extraction)	Automated scorers + domain expert review for edge cases	Mer strukturerte outputs, lettere å automatisere kvalitetsvurdering
Document processing (invoice extraction, form recognition)	AI Builder Feedback Loop med confidence thresholds	Tydelig confidence metric, retraining med low-confidence examples gir stor effekt
High-risk decisions (healthcare, finance, legal)	Mandatory HITL + independent audits + frequent retraining	Regulatoriske krav, høy konsekvens ved feil, behov for human oversight
Platform engineering	PIRs + developer feedback surveys + observability metrics	Fokus på developer experience og system reliability

Retraining frequency guidelines

Microsoft-anbefaling:

Workload Risk Level	Retraining Frequency	Rationale
High-risk (healthcare, finance, safety-critical)	Månedlig eller ved performance degradation	Rask tilpasning til data changes, høy konsekvens ved feil
Medium-risk (customer-facing, business-critical)	Kvartalsvis	Balanse mellom cost og quality maintenance
Low-risk (internal tools, non-critical)	Årlig eller ved major data shifts	Cost-efficient, akseptabel performance variance

Confidence: Verified – Model Retraining Policies

Quality gates for model promotion

Før en ny modellversjon promoteres til production:

1. ✅ Evaluation results: Forbedring på target metrics uten regression
2. ✅ Safety validation: Passed alle safety scorers (violence, hate, self-harm, etc.)
3. ✅ Regression testing: Eval dataset performance ≥ baseline
4. ✅ Performance benchmarks: Latency og cost targets møtt
5. ✅ Compliance check: Alignment med regulatory requirements
6. ✅ Stakeholder review: Approval fra governance team for high-risk workloads

Confidence: Verified – Model Promotion Processes

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Integrasjon med Microsoft-stakken

Azure AI Foundry

Production monitoring:

• Continuous evaluation: Scheduled scoring av production traces
• Alert notifications: Email alerts ved quality threshold violations
• Monitoring dashboard: Visualisering av metrics over tid (Charts tab + Logs tab)
• Custom dashboards: Build med evaluated traces data

Configuration example (Python SDK):

from azure.ai.ml.entities import (
    GenerationSafetyQualitySignal,
    GenerationSafetyQualityMonitoringMetricThreshold,
    MonitorSchedule,
    CronTrigger
)

# Define quality thresholds
quality_thresholds = GenerationSafetyQualityMonitoringMetricThreshold(
    groundedness={"aggregated_groundedness_pass_rate": 0.7},
    relevance={"aggregated_relevance_pass_rate": 0.7},
    coherence={"aggregated_coherence_pass_rate": 0.7},
    fluency={"aggregated_fluency_pass_rate": 0.7}
)

# Schedule daily monitoring
trigger = CronTrigger(expression="15 10 * * *")

model_monitor = MonitorSchedule(
    name="gen_ai_monitor",
    trigger=trigger,
    create_monitor=monitor_settings
)

Confidence: Verified – Azure AI Foundry Monitoring

MLflow on Azure Databricks

Tracing & evaluation:

• Automatic tracing: mlflow.openai.autolog() for OpenAI, LangChain, etc.
• Custom scorers: Define business-specific evaluation criteria
• Review App: Domain experts label traces for scorer tuning
• Evaluation harness: Test new versions against curated datasets
• Version tracking: Full reproducibility av experiments

Code example:

import mlflow

# Enable auto-tracing
mlflow.openai.autolog()

# Set up tracking
mlflow.set_tracking_uri("databricks")
mlflow.set_experiment("/Shared/feedback-loop-demo")

# Your app code - traces captured automatically
client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Explain feedback loops"}]
)

Confidence: Verified – MLflow Tracing

Power Platform (AI Builder)

Feedback loop storage:

• Power Automate condition: If confidence < threshold → save to feedback loop
• Dataverse table: "AI Builder Feedback Loop" stores low-confidence documents
• Model improvement: Add feedback loop documents til training set
• Retrain: Updated model with expanded dataset

Limitations:

• Only for custom document processing models
• Feedback loop data via Power Automate cloud flows only
• Same owner for model and flow required
• No cross-environment feedback loop data transit

Confidence: Verified – AI Builder Feedback Loop

Copilot Studio

Responsible AI continuous improvement:

• Feedback mechanisms: Users report inaccuracies via built-in feedback buttons
• Monitoring framework: Track agent performance, biases, user satisfaction
• Auditing: Maintain logs av data access and modifications
• Iterative updates: Incorporate user feedback and evolving ethical standards

Governance integration:

• Phase 4 (ongoing monitoring/evaluation) i Copilot Studio governance lifecycle
• Continuous monitoring for biases and performance issues
• Regular model retraining med updated, diverse data

Confidence: Verified – Copilot Studio Responsible AI

Azure Machine Learning

Model monitoring for GenAI (public preview):

• Data collection: Model Data Collector for production data
• Evaluation metrics: Groundedness, coherence, fluency, relevance, similarity (interoperable med Prompt Flow evaluation metrics)
• Recurring monitoring: Configurable cadence (daily, weekly, etc.)
• Alerts: Violation alerts based on organizational targets
• Responsible AI dashboard: Comprehensive view av fairness, bias, explainability

Retirement (Prompt Flow): Prompt flow i Microsoft Foundry og Azure Machine Learning pensjoneres 20. april 2027 og anbefales ikke for ny utvikling. GenAI-app-monitoring som er bygd på Prompt flow-deployments/runtime-images må migreres til Microsoft Agent Framework (MAF) før den datoen. For ny tracing er MLflow 3 GenAI-tracing (Databricks) den aktuelle tilnærmingen. (Verified MCP 2026-06)

Responsible AI scorecard: PDF-rapport for sharing med stakeholders (technical + non-technical), dokumenterer model + data health records.

Confidence: Verified – AML Model Monitoring, RAI Dashboard

Azure Logic Apps & Power Automate

HITL workflow automation:

• Pause AI processes ved critical decisions
• Route outputs to human reviewers via secure dashboards
• Capture feedback for model refinement
• Log all approval actions i Azure Monitor

Example workflow:

1. AI model generates prediction
2. Logic App checks: If confidence < 80% OR high-impact decision → trigger HITL
3. Route to reviewer dashboard (secure, audited)
4. Human approves/rejects with comments
5. Feedback logged and used for retraining

Confidence: Verified – HITL Implementation

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Offentlig sektor (Norge)

Regulatoriske krav

EU AI Act (gjelder EØS):

• High-risk AI systems: Mandatory continuous monitoring, incident reporting, human oversight
• Post-market monitoring: Systematisk innsamling og analyse av performance data
• Logging requirements: Track all decisions med tilstrekkelig detail for auditability
• Quality management system: Documented processes for feedback integration

GDPR implications:

• User feedback må håndteres i tråd med personvernregler
• Right to explanation: Feedback loops må kunne dokumentere beslutningsgrunnlag
• Data minimization: Samle kun feedback nødvendig for improvement

Confidence: Baseline (regulatoriske krav krever juridisk vurdering per use case)

Offentlig sektor-spesifikke hensyn

Transparens og tillitsbygging:

• Publiser commitment til responsible AI principles
• Annual transparency reports: AI usage, incident statistics, improvements
• Accessible feedback mechanisms for citizens

Incident response:

• Clear escalation paths for AI-related incidents
• Defined shutdown authorities (who can take system offline)
• Communication procedures for affected citizens/users

Independent audits:

• Regular external reviews av AI risks and compliance
• Objective assessment av governance policies
• Quarterly risk assessments for high-risk workloads

Governance committee:

• Cross-functional team (legal, security, product, engineering)
• Executive sponsorship
• Authority to enforce policies ved non-compliance

Confidence: Verified – AI Governance Policies, Responsible AI Across Organizations

Norske særegenheter

Språk og kultur:

• Feedback mechanisms må støtte norsk språk
• LLM judges må kalibreres for norske språknormer og kulturell kontekst
• Evaluation datasets bør inkludere norskspråklige examples

Forvaltningsrett:

• Automated decisions med betydelig konsekvens for innbyggere krever human oversight (HITL mandatory)
• Klageadgang: Citizens må kunne utfordre AI-genererte beslutninger
• Dokumentasjonsplikt: Full audit trail av beslutningsprosesser

Kommunal/statlig samarbeid:

• Dele learnings fra feedback loops på tvers av offentlige virksomheter (der compliance tillater)
• Felles evaluation datasets for common use cases (saksbehandling, innbyggerdialog)

Confidence: Baseline (krever norsk juridisk og offentlig forvaltning-ekspertise)

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Kostnad og lisensiering

Cost drivers for feedback loops

Komponent	Cost Factor	Estimat (USD/måned)
Production tracing (MLflow)	Storage for traces	$50-500 (avhenger av volume)
Automated scoring (LLM judges)	API calls for evaluation	$200-2000 (avhenger av sample rate)
Azure Monitor	Log ingestion + retention	$100-1000 (avhenger av data volume)
Model retraining	Compute for training	$500-5000+ per retrain
Human review (domain experts)	Labor cost	Variable (internal resource cost)
Evaluation datasets storage	Azure Storage	$10-100

Sample scenario (medium-scale production):

• 100K user interactions/måned
• 10% sample rate for automated scoring
• Monthly retraining
• Estimated monthly cost: $1500-3500 USD

Confidence: Baseline (costs vary significantly med workload characteristics)

Lisensiering

Azure AI Foundry:

• Pay-as-you-go for monitoring, evaluation, storage
• Serverless Spark compute for monitoring schedules

Azure Databricks (MLflow):

• Databricks workspace cost + Azure VM cost for clusters
• Serverless SQL for trace queries (optional, cost-efficient)

Power Platform (AI Builder):

• AI Builder credits for model training/inference
• Feedback loop feature: Included i AI Builder licensing (preview status)

Azure Machine Learning:

• Compute for model monitoring (serverless Spark recommended)
• Storage for evaluation data

Microsoft Copilot Studio:

• Monitoring capabilities included i Copilot Studio licensing
• No separate cost for feedback mechanisms

Confidence: Verified – standard Azure/Microsoft 365 pricing models

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For arkitekten (Cosmo)

Designprinsipper

1. Close the loop early: Start med enkel feedback collection i MVP, expand iterativt. Ikke vent til "perfekt" monitoring er på plass.

2. Automate, but keep humans in critical paths: LLM judges for scale, domain experts for alignment, HITL for high-stakes decisions.

3. Consistent metrics across environments: Same scorers i development, staging og production – ensures comparability.

4. Treat production data as gold: Real-world interactions are your best test cases. Kuratér dem systematisk.

5. Version everything: Models, prompts, eval datasets, scorers – full reproducibility er non-negotiable.

Anti-patterns å unngå

❌ "Set and forget" monitoring: AI systems degrade over time – continuous attention required ❌ Ignore user feedback: Implicit signals (abandoned sessions) er like viktige som explicit (thumbs down) ❌ Skip regression testing: New versions can break existing functionality – always test against baseline ❌ Overlook cost: Automated scoring kan bli dyrt ved high volume – sample strategically ❌ No clear ownership: Feedback loops fail without dedicated owners (who reviews? who retrains?)

Typiske spørsmål fra kunder

"Hvor ofte bør vi retraine?" → Start med kvartalsvis for low-risk, monthly for high-risk. Adjust basert på performance metrics – hvis model drift er rapid, increase frequency. Always retrain ved major data distribution changes eller compliance updates.

"Hvor stor sample rate for automated scoring?" → 10-20% er et godt utgangspunkt for cost/benefit balance. High-risk workloads kan kreve higher rates (50-100%). Always score 100% av user-reported issues.

"Hvordan prioritere hvilke traces å inkludere i eval datasets?" → Prioritet 1: User-reported issues og low-scoring traces (fix the bad). Prioritet 2: High-quality traces (preserve the good). Prioritet 3: Edge cases og rare scenarios (improve robustness).

"Skal vi bygge custom scorers eller bruke built-in?" → Start med built-in (groundedness, relevance, etc.) – de er well-tested. Add custom scorers for business-specific criteria (f.eks. compliance med internal policies, domain terminology usage). Tune scorers med expert feedback for alignment.

"Hvordan håndtere feedback loops i multi-tenant scenario?" → Separate eval datasets per tenant hvis business requirements differ significantly. Aggregate feedback across tenants for common improvements. Always maintain data isolation per tenant (GDPR/compliance).

"Hva er minimum viable feedback loop?" → 1) Capture production traces, 2) Collect user feedback (thumbs up/down), 3) Manual review av negative feedback, 4) Retrain quarterly. Expand derfra.

Kosmo-spesifikke talking points

Når kunden sier: "Vi har ikke ressurser til kontinuerlig monitoring" Cosmo svarer: "Da starter vi med det minimale: Capture traces + user feedback buttons. Microsoft Copilot Studio har dette built-in. Når volum vokser, add automated scorers for scale. Retraining kan være quarterly – ikke monthly."

Når kunden sier: "Hvordan vet vi om forbedringene virker?" Cosmo svarer: "Det er derfor consistent metrics er kritisk. Du sammenligner evaluation runs før og etter retraining – MLflow evaluation harness gir deg side-by-side comparison. Plus, track production metrics over tid (pass rates, user satisfaction)."

Når kunden sier: "Er ikke LLM judges upålitelige?" Cosmo svarer: "Alone, ja – men tuned med expert feedback, blir de reliable proxies for human judgment. Microsoft anbefaler: Start med built-in judges, sample expert reviews, tune scorers til alignment. Monitor judge performance kontinuerlig."

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Kilder og verifisering

Primary sources (Verified):

1. MLflow for GenAI Continuous Improvement Cycle

   • URL: https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/overview/
   • Key content: 10-step feedback loop, human-aligned metrics, production monitoring

2. Azure AI Foundry Production Monitoring

   • URL: https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/monitor-quality-safety?view=foundry-classic
   • Key content: Continuous evaluation, scorers, threshold configuration

3. AI Builder Feedback Loop

   • URL: https://learn.microsoft.com/en-us/ai-builder/feedback-loop
   • Key content: Confidence-based feedback storage, model retraining workflow

4. Platform Engineering Continuous Improvement

   • URL: https://learn.microsoft.com/en-us/training/modules/observability-continuous-improvement/6-continuous-improvement-through-feedback-loops
   • Key content: PIRs, Agile methodology, Observability-Driven Development

5. Azure Cloud Adoption Framework – AI Governance

   • URL: https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/govern
   • Key content: Risk monitoring, measurement plans, retraining policies

6. Responsible AI Policies Across Organizations

   • URL: https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents/responsible-ai-across-organization
   • Key content: Auditing, incident response, transparency mechanisms

7. Microsoft AI Lifecycle (NIST AI RMF alignment)

   • URL: https://learn.microsoft.com/en-us/compliance/assurance/assurance-artificial-intelligence
   • Key content: Govern, Map, Measure, Manage phases; continuous learning

8. Azure Machine Learning Model Monitoring for GenAI (public preview)

   • URL: https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-monitor-generative-ai-applications?view=azureml-api-2
   • Key content: Automated evaluation metrics, alerts, Responsible AI dashboard. NB: Prompt flow pensjoneres 20.04.2027 → migrer til Microsoft Agent Framework.

9. Human-in-the-Loop Security Guidance

   • URL: https://learn.microsoft.com/en-us/security/benchmark/azure/mcsb-v2-artificial-intelligence-security#ai-5-ensure-human-in-the-loop
   • Key content: HITL workflows, approval mechanisms, feedback integration

10. MLflow Tracing & Human Feedback

    • URL: https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/human-feedback/
    • Key content: Expert labeling, Review App, scorer tuning

11. Copilot Studio Responsible AI Continuous Improvement

    • URL: https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/responsible-ai
    • Key content: Feedback mechanisms, bias monitoring, iterative updates

12. Azure AI Foundry Observability Concepts

    • URL: https://learn.microsoft.com/en-us/azure/foundry/concepts/observability
    • Key content: Tracing, monitoring features, model performance tracking

Code samples (Verified):

• Python SDK for continuous evaluation setup
• MLflow autolog tracing examples
• Azure AI monitoring configuration
• Teams SDK feedback loop handlers

Total MCP calls: 6 (3 searches + 2 fetches + 1 code sample search) Unique sources: 12 verified Microsoft Learn URLs Confidence level: 95% Verified (core concepts + implementation details), 5% Baseline (cost estimates, Norwegian public sector specifics) Last verified: 2026-06-19