Phase 1 - Immediate FP&A foundation

Forecast Intelligence Runtime

Move forecasting from a number to an explainable decision: what changed, why it changed, what happens next, and what action should be taken.

Business problem: Automated forecasting and commentary are insufficient unless the runtime connects drivers, variance, scenarios, cash impact, policy, evidence, and recommended response. 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 2785615 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/forecast-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

Forecasts become continuously refreshed, driver-explained, scenario-aware, policy-bound, and replayable.

Core components
ComponentCapability
Forecast AgentProduces revenue, margin, cash, EBITDA, FCF, ROACE, and confidence.
Variance AgentExplains plan vs actual and forecast vs actual using volume, price, mix, FX, timing, and driver movement.
Scenario AgentRuns best/base/worst cases and quantifies downstream cash, tax, EBITDA, and working-capital effects.
Narrative AgentGenerates board commentary, management commentary, and evidence-linked insight.
Forecast Studio UIShows forecast, confidence, risk, drivers, narrative, actions, evidence, and replay.
Success metrics
MetricTarget
Forecast accuracy95% target
Narrative evidence coverage100% target
Scenario latency<5s target
Driver attribution90% target
FP&A deck alignment
Target operating-model theme
Automated forecasting
Commentary
Insights generation
What changed
Why it changed
Agent family
AgentPurpose
Forecast Intelligence AgentExplains what changed, why, what happens next, and what action is recommended.
Scenario AgentEvaluates alternative futures and intervention options.
Narrative AgentProduces governed commentary with sources and confidence.
Proof links
SurfaceRoute
Continuous Forecast IntelligenceOpen
FP&A Decision OSOpen
CFO Command CenterOpen
Runtime Command CenterOpen
API contract
Endpoint
/api/finance-foundations/forecast-intelligence-runtime
/ui/fpa-decision-os
/ui/continuous-forecast-intelligence
Validation questions
Question
What changed in the forecast?
Why did it change?
What is the cash, EBITDA, tax, and risk impact?
What action should finance recommend?