Phase 1 - Immediate FP&A foundation

Variance Intelligence Runtime

Automate variance explanation by connecting actuals, forecasts, drivers, evidence, and recommended response.

Business problem: FP&A spends significant effort explaining variance; the target model is that variances explain themselves, but credibility requires root cause, attribution, evidence, confidence, and action. Open JSON contract Back to foundations
Runtime Hardening Proof

Execution evidence from Enterprise Decision Runtime service

This panel is generated from runtime service functions on each request: event correlation, policy simulation, learning metrics, agent guardrails, decision trace, and audit package export. It closes the view-vs-system gap by showing the platform executing, not only describing architecture.

7registered agents
5registered policies
1correlated signal(s) from 3 events
$450.0Kpolicy simulation amount
100.0%human acceptance over 2785610 decided executions (90d)
5825088decision memory records
BLOCKbad-data guardrail result
route_to_human_supervisoragent failure fallback
Live event correlation
SignalPriorityImpactEvent IDs
sig-2efe089ec8P1$1.4Mevt-001, evt-002, evt-003
Policy + guardrail hardening
ControlRuntime value
Policyhuman-approval-policy
Prior threshold$50.0K
Simulated threshold$100.0K
Outcomeescalate
Approval volume change-18%
Guardrail failuresmissing_evidence, low_source_confidence, writeback_requires_human_approval
Hallucination guardevidence_required_before_recommendation
Replay + audit export
ArtifactValue
Audit packageaudit-97ab17991395
Integrity hashc80fd1aea51cec3d76a8d2e6346938acd1938531f544f8f3f7fd3917f1d6c98c
Regulatory export readyTrue
Trace steps10
Runtime chainSignal -> Context -> Ontology -> Policy -> Evidence -> Decision -> Governance -> Action -> Replay -> Learning
Runtime APIs
Endpoint
/api/finance-foundations/runtime-proof
/api/finance-foundations/variance-intelligence-runtime/runtime-proof
/api/iaf/enterprise-decision-runtime/learning-metrics
/api/iaf/enterprise-decision-runtime/agent-guardrails
/api/iaf/enterprise-decision-runtime/policy-simulation
/api/iaf/enterprise-decision-runtime/event-correlation
Hardening stance: agents fail closed to human supervision on missing evidence, low source confidence, or write-back attempts. Learning is measured, replay-backed, and cannot change policy or autonomy without approval.
Open Enterprise Decision Runtime Open runtime proof JSON
North Star runtime outcome

Variances explain themselves: every material variance shows what changed, why, which driver caused it, what evidence proves it, and what action should follow.

Core components
ComponentCapability
Root Cause DiscoveryVolume, price, mix, FX, timing, cost, utilization, maintenance, and working-capital root causes.
Attribution AnalysisLinks each variance to operational driver, source record, entity, owner, and financial outcome.
Explainability EngineShows why changed, evidence, source confidence, policy relevance, and replay.
Recommended ResponseSpend control, hedging, supplier action, production action, reforecast, or escalation.
Variance Workbench UIVariance, root cause, evidence, recommended action, expected outcome, and narrative.
Self-Explaining Variance ContractImplements the target phrase: variances explain themselves, with driver attribution and evidence rather than generic commentary.
Success metrics
MetricTarget
Variance attribution90% target
Commentary evidence coverage100% target
Narrative generation<10s target
Action linkage95% target
FP&A deck alignment
Target operating-model theme
Variance Analysis
Commentary
Insights
Root cause
Explainable finance intelligence
Agent family
AgentPurpose
Variance Intelligence AgentExplains plan-vs-actual and forecast-vs-actual movement.
Root Cause AgentFinds causal drivers and owner.
Narrative AgentCreates governed commentary with sources and confidence.
Proof links
SurfaceRoute
FP&A Command CenterOpen
FP&A Decision OSOpen
CFO Command CenterOpen
Replay CenterOpen
API contract
Endpoint
/api/finance-foundations/variance-intelligence-runtime
/ui/fpa-command-center
/ui/fpa-decision-os
Validation questions
Question
Why did the variance happen?
Which driver caused it?
Which transactions or source records prove it?
What action should finance take?