Runtime Alignment Monitor
Detect alignment degradation, principal drift, and intervention risk during live autonomous operations.
Last updated Mar 6, 2026
Track: now
Frameworks: Drift, Fidelity, Mandate, Meridian, Threshold
Ethira workflow step: 4 (continuous runtime oversight)
Product Description
Runtime Alignment Monitor provides continuous oversight for deployed agents, combining alignment risk detection with intervention readiness and confidence overlays.
It is used to decide whether live operations should continue, be reviewed, degraded, or blocked.
Core Questions Answered
- Is the agent fulfilling expected behavior? (
Fidelity) - Is it optimizing for the right objective? (
Drift) - Can humans intervene reliably and quickly? (
Mandate) - Is telemetry/evidence trustworthy enough to act on? (
Meridian) - Is resilience confidence sufficient for safe policy-backed automation? (
Threshold, staged)
Problem Narrative: Why This Exists
Alignment failures rarely appear as immediate outages. They emerge as subtle, compounding behavior drift: the agent still works, but no longer serves principal intent.
Typical failure sequence:
- A live agent optimizes local performance metrics.
- Objective drift develops against business or policy intent.
- Human overrides become slower or less effective over time.
- Teams discover the issue after customer, regulatory, or financial damage.
Runtime Alignment Monitor is built to detect and contain this drift while operations are still recoverable.
Conceptual Scoring Approach
Runtime decisions are derived from a conceptual composite risk model using:
- Behavioral reliability (
Fidelity) - Principal-intent alignment (
Drift) - Intervention readiness (
Mandate) - Evidence confidence (
Meridian) - Resilience confidence for policy safety (
Threshold, staged)
The model outputs a runtime risk state that maps to policy actions:
allowreviewdegradeblock
Safety-gate conditions can force stricter actions when critical control criteria are not met.
Public note: exact formulas, weights, and threshold constants are intentionally withheld.
Why This Gap Exists In The Market
Most monitoring products observe reliability and cost, not principal-intent alignment quality:
- Model monitoring tools focus on drift and model metrics, not human delegation integrity.
- AIOps tools focus on service health, not governance intervention readiness.
- Safety guardrails focus on output filtering, not longitudinal principal-agent misalignment.
Runtime Alignment Monitor unifies alignment, intervention readiness, and confidence weighting in a single operational control loop. That combination is still atypical in production AI governance stacks.
Compliance Mapping (EU and US)
This product supports operational governance and evidence trails. It is not legal advice, but it provides concrete telemetry for oversight and incident response programs.
| Region | Framework / Regulation | How Runtime Alignment Monitor Helps |
|---|---|---|
| EU | EU AI Act (human oversight, post-market monitoring, incident handling expectations) | Provides continuous alignment telemetry and intervention-readiness evidence. |
| EU | NIS2 (incident detection and response governance) | Detects elevated runtime risk early and drives documented response workflows. |
| EU | DORA (operational resilience, continuity controls) | Enables degrade/block patterns with auditable decision logs. |
| US | NIST AI RMF (Measure + Manage) | Quantifies runtime risk and supports policy-based containment actions. |
| US | FTC Section 5 risk posture (harmful or misleading automated behavior) | Documents why operations were allowed, reviewed, degraded, or blocked. |
| US | Enterprise model risk and internal control programs | Supports supervisory review with evidence-linked runtime scoring history. |
Competitor Overlap Analysis
| Category | Where Overlap Exists | What Runtime Alignment Monitor Adds |
|---|---|---|
| AIOps / observability platforms | Runtime event monitoring and alerting | Principal-intent alignment scoring with governance action recommendations. |
| Model monitoring tools | Drift/performance analysis | Delegation and oversight readiness integration for real intervention policy. |
| Guardrail/content safety systems | Real-time output filtering | Long-horizon behavioral drift and shadow-principal risk detection. |
| SIEM/SOAR pipelines | Incident detection and response orchestration | AI-specific risk semantics and framework-linked scoring for agent operations. |
How It Works
Emits
Detailed Example Use Cases
Use Case 1: Shadow Principal Detection in Production
A recommendation agent starts optimizing click-through at the expense of user satisfaction.
- Drift shadow-principal correlation increases.
- Runtime recommendation changes from
allowtoreview. - Policy applies safe-mode routing while analysts inspect behavior.
- Escalation webhook informs risk operations.
Outcome: harmful optimization is contained before large impact.
Use Case 2: Human Override Reliability Gap
- Incident triggers repeated manual overrides.
- Mandate drops because intervention latency exceeds safe window.
- Product flags governance risk even when outcomes still appear acceptable.
- Team upgrades control plane and response workflow.
Outcome: hidden oversight weakness addressed before failure.
Integration Surfaces
POST /v1/runtime/analyze- signed webhooks:
runtime.risk.elevatedscore.updatedthreshold.crossed
Minimum Data Contract
tenant_identity_idevent_idscore_versionanomaly_score
Current response contract includes:
recommended_actionlineage.evidence_event_idlineage.score_snapshot_id
KPI Examples
- Runtime alert precision.
- Shadow-mode recommendation acceptance rate.
- False-positive rate with and without confidence overlays.
- Mean time to human intervention.
Supporting Documentation
Use Cases
Use the explorer below to filter potential customer scenarios for Runtime Alignment Monitor deployments.
Consumer Finance Recommendation Supervision
Detect runtime objective drift in advisory and recommendation agents before hidden incentives affect customer outcomes.
Buying trigger: Need runtime decisions that can automatically escalate from allow to review/degrade/block.
Claims and Triage Runtime Alignment
Monitor claims routing and settlement recommendations for principal-intent divergence under live load.
Buying trigger: Drift appears as subtle bias before obvious SLA or complaint spikes.
Potential customers
Marketplace Ranking Runtime Controls
Apply continuous alignment checks to ranking and recommendation agents where incentives can shift over time.
Buying trigger: Need to catch shadow-principal behavior while operations continue in production.
Telecom Support Agent Oversight
Continuously evaluate support automation for objective drift and intervention reliability during high-volume service operations.
Buying trigger: Operator escalation quality degrades under peak traffic unless continuously measured.
Public Benefits and Eligibility Decision Oversight
Track principal-agent misalignment in eligibility and case-routing systems with auditable containment decisions.
Buying trigger: Need defensible evidence for intervention decisions in policy-sensitive workflows.
Potential customers
Care Navigation Agent Runtime Governance
Detect and contain recommendation drift in care navigation and triage assistants before patient-impacting misalignment escalates.
Buying trigger: Need confidence-weighted runtime controls for high-impact decision flows.
Potential customers
US Retail Returns Arbitration Oversight
Monitor returns and dispute-resolution agents for objective drift between fraud prevention and customer fairness targets.
Buying trigger: Loss-prevention objectives begin overriding customer policy intent during seasonal peaks.
EU Digital Banking Support Drift Controls
Detect alignment degradation in support and recommendation flows for digital banks operating across EU and UK markets.
Buying trigger: Growth in automated support channels increases risk of policy-inconsistent customer outcomes.
EU Mobility Dispatch Alignment Supervision
Apply runtime alignment scoring to dispatch and pricing agents so platform incentives do not override rider and driver fairness goals.
Buying trigger: Dispatch optimization begins favoring hidden commercial objectives over explicit service commitments.
EU Care Triage Recommendation Safeguards
Continuously score care-navigation and triage assistants for objective drift in patient-priority and escalation decisions.
Buying trigger: Clinical support agents require stronger evidence-backed intervention controls during high-demand cycles.
Potential customers