Product Guide

Runtime Alignment Monitor

Detect alignment degradation, principal drift, and intervention risk during live autonomous operations.

Last updated Mar 4, 2026

Track: now
Frameworks: Drift, Fidelity, Mandate, Meridian
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)

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.

Mathematical Approach Applied

Runtime decisions are derived from a composite risk index:

F = fidelity_score / 100
D = drift_score / 100
M = mandate_score / 100
C = meridian_confidence   (0 to 1)

RuntimeRisk = 100 * (0.35*(1 - D) + 0.30*(1 - F) + 0.20*(1 - M) + 0.15*(1 - C))

Decision policy:

if RuntimeRisk < 30: recommendation = "allow"
if RuntimeRisk >= 30 and RuntimeRisk < 50: recommendation = "review"
if RuntimeRisk >= 50 and RuntimeRisk < 70: recommendation = "degrade"
if RuntimeRisk >= 70: recommendation = "block"

Hard safety gates are applied when critical control conditions fail:

if drift_score < drift_min or mandate_score < mandate_min:
  recommendation = "block"

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

1Runtime Telemetry

2Policy + Objective Context

3Runtime Analyze API

4Alignment Decision Engine

5Signed Risk Webhooks

Emits

Drift/Fidelity/Mandate/Meridian scoresrecommendation: allow / review / degrade / block

1Collect runtime events

2Evaluate alignment + confidence

3Emit recommendation

4Apply operator/policy action

5Log intervention for next evaluation

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 allow to review.
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.elevated
- score.updated
- threshold.crossed

Minimum Data Contract

agent_id
telemetry event references
policy and objective context
intervention/control-point metadata
evidence quality metrics for confidence overlays