Manufacturing
AI governance for manufacturing decisions.
Production optimization, predictive maintenance, quality control, supply chain management — your AI is making decisions that affect physical operations. Kevros governs those decisions and produces the evidence your quality and compliance teams need.
Schedule a briefingThe problem
AI decisions in manufacturing have physical consequences.
When AI optimizes a production line, schedules maintenance, or adjusts quality thresholds, those decisions affect physical processes — equipment, materials, safety, and product quality. The consequences are real and often irreversible.
Your quality management system needs evidence: “Was this AI recommendation evaluated against policy before it reached the production floor?”
Kevros enforces governance at the AI decision boundary and logs cryptographic evidence of every decision.
How Kevros helps
Fail-closed governance for physical operations.
Fail-Closed Enforcement
When the governance system detects an integrity violation, it locks and blocks all agent actions. No automatic recovery. For manufacturing environments where AI errors have physical consequences, this is essential.
Cryptographic Evidence Chain
Every governance decision is appended to a hash-chained provenance ledger. Tamper-evident evidence for quality audits, safety reviews, and regulatory examinations.
Formally Verified
Enforcement kernel proven correct across 32.8 million state configurations. Zero safety violations. Mathematical certainty that the governance layer behaves as specified.
Real-Time Policy Enforcement
Every AI-driven production decision is evaluated against your policies at execution time. Production optimization, maintenance scheduling, quality thresholds — all governed before execution.
Behavioral Drift Detection
ML confidence scoring detects when AI model behavior drifts from established baselines. Critical for manufacturing where gradual model drift can cause quality degradation.
Model-Agnostic
One governance layer for all AI decisions — cloud-hosted or on-premises. OpenAI, Google, Meta, open-source, or proprietary models. Enforcement does not change when your AI provider does.
Manufacturing use cases.
Production Optimization
Govern AI-driven production scheduling, resource allocation, and throughput optimization. Every production decision carries cryptographic evidence of policy evaluation.
Predictive Maintenance
Enforce policies on AI-driven maintenance scheduling, equipment health assessment, and failure prediction. Evidence trail for every maintenance decision.
Quality Control
Govern AI-driven quality inspection, defect detection, and acceptance criteria. Audit-ready evidence for every quality determination.
Supply Chain Management
Enforce governance on AI-driven inventory optimization, demand forecasting, and supplier evaluation. Cryptographic evidence of every supply chain decision.
Code examples
Real SDK examples for manufacturing governance.
Official examples using the Kevros SDK. Governance boundaries are configured server-side through the gateway. All code on this page is the canonical reference.
Predictive Maintenance — Verify Before Acting on Recommendation
from kevros_governance import GovernanceClient
client = GovernanceClient(agent_id="maintenance-planner")
# AI recommends shutting down production line 3 for maintenance
result = client.verify(
action_type="maintenance_action",
action_payload={
"recommendation": "shutdown_line_3",
"reason": "bearing_vibration_anomaly",
"confidence": 0.91,
"estimated_downtime_hours": 4,
"production_impact": "high",
},
agent_id="maintenance-planner",
)
if result.decision.value == "ALLOW":
initiate_maintenance(line=3, token=result.release_token)
elif result.decision.value == "CLAMP":
print(f"Modified: {result.applied_action}")
elif result.decision.value == "DENY":
escalate_to_plant_manager(reason=result.reason)
Quality Control — Full Governance Loop
from kevros_governance import GovernanceClient, IntentType
client = GovernanceClient(api_key="kvrs_your_key_here")
# 1. BIND — Declare intent to adjust quality parameters
bind = client.bind(
agent_id="qc-agent-line-7",
intent_type=IntentType.AI_GENERATED,
intent_description="Adjust vision system sensitivity for batch 2026-0227",
command_payload={
"parameter": "defect_threshold",
"current_value": 0.85,
"proposed_value": 0.92,
"batch_id": "2026-0227",
},
goal_state={"defect_threshold": 0.92},
)
# 2. VERIFY — Is this adjustment authorized?
verify = client.verify(
action_type="parameter_adjustment",
action_payload={
"parameter": "defect_threshold",
"proposed_value": 0.92,
"line": "line-7",
},
agent_id="qc-agent-line-7",
)
# 3. Apply the adjustment (your MES/SCADA integration)
apply_quality_parameter(verify.release_token)
# 4. ATTEST — Record what happened
attest = client.attest(
agent_id="qc-agent-line-7",
action_description="Adjusted defect threshold from 0.85 to 0.92 for batch 2026-0227",
action_payload={
"parameter": "defect_threshold",
"old_value": 0.85,
"new_value": 0.92,
"batch_id": "2026-0227",
"line": "line-7",
},
)
# 5. VERIFY OUTCOME
outcome = client.verify_outcome(
agent_id="qc-agent-line-7",
intent_id=bind.intent_id,
binding_id=bind.binding_id,
actual_state={"defect_threshold": 0.92},
)
print(f"Chain: {attest.chain_length} | Outcome: {outcome.status}")
Supply Chain Agent — Multi-Agent Peer Trust
from kevros_governance import GovernanceClient
client = GovernanceClient(agent_id="supply-chain-coordinator")
# Check trust before accepting recommendation from procurement agent
peer = client.verify_peer("procurement-agent-apac")
print(f"Trust score: {peer.get('trust_score')}")
# Verify the coordination action
result = client.verify(
action_type="procurement_approval",
action_payload={
"supplier": "component-vendor-042",
"part": "bearing-assembly-7x",
"quantity": 5000,
"value_usd": 47500,
},
agent_id="supply-chain-coordinator",
)
if result.decision.value == "ALLOW":
submit_purchase_order(token=result.release_token)
Relevant capabilities by tier.
| Capability | Starter | Professional | Enterprise |
|---|---|---|---|
| Pre-execution verification (ALLOW/CLAMP/DENY) | |||
| Cryptographic release tokens | |||
| Hash-chained provenance ledger | |||
| ML behavioral drift detection | — | ||
| Post-quantum ML-DSA-87 signing | — | ||
| SIEM export | — | — | |
| Compliance evidence bundles | — | — |
Compliance note
Kevros provides technical governance controls. Using Kevros does not confer compliance with ISO 9001, IEC 62443, or other manufacturing/industrial standards. Organizations use Kevros capabilities as components within their own quality and safety management programs. See /compliance.
What Kevros does NOT do in manufacturing
Precision about our capabilities.
Kevros verifies whether an AI agent is authorized to take an operational action. It does not:
Control robotic actuators or motor controllers directly
Replace safety PLCs, SIL-rated controllers, or hardware emergency stops
Perform real-time sensor fusion or machine vision
Generate maintenance predictions (it governs actions based on predictions)
Replace functional safety systems (IEC 61508, ISO 13849)
Developer Note
Kevros governs the authorization of AI-driven operational decisions through the standard SDK. It does not replace real-time safety controllers, hardware interlocks, or industrial communication protocols. All code examples on this page are the canonical reference.
Ready to govern your manufacturing AI?
Schedule a briefing. We'll walk through fail-closed enforcement, evidence logging, and how Kevros governs AI decisions in physical operations environments. Or deploy the Free Trial on Azure and see it yourself.