Phase 2 - Future finance demonstration

Decision Simulation Engine

Allow finance leaders to test decisions before executing them.

Business problem: Finance often reports what happened; the North Star requires simulation of what could happen and what should be done. 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/decision-simulation-engine/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

CFO decision intelligence is grounded in scenario, financial impact, policy impact, recommendations, and alternatives.

Core components
ComponentCapability
Scenario BuilderOil price, payment terms, supplier failure, reserve movement, exposure, and working-capital target changes.
Financial Impact ModelRevenue, margin, cash, EBITDA, working capital, debt, and risk.
Policy Impact SimulatorPolicies affected, approvals required, risk introduced, and controls triggered.
Agent Recommendation SimulatorCompare option A, B, C with confidence and tradeoffs.
Executive WorkspaceCFO, Controller, Treasury, and FP&A simulations with board-ready explanation.
Success metrics
MetricTarget
Simulation latency<5s target
Forecast accuracy90% target
Explainability100% target
FP&A deck alignment
Target operating-model theme
Agent family
AgentPurpose
Scenario Modeling AgentBuilds and parameterizes scenarios.
Impact Analysis AgentQuantifies financial, risk, policy, and operational effects.
Recommendation AgentRanks options and explains tradeoffs.
Proof links
SurfaceRoute
FP&A Decision OSOpen
LNG Economics StudioOpen
CFO Command CenterOpen
Runtime Command CenterOpen
API contract
Endpoint
/api/finance-foundations/decision-simulation-engine
/ui/fpa-decision-os
/ui/lng-economics-decision-studio
Validation questions
Question
What happens if oil drops 10%?
Which policy approvals change?
Which option improves cash with lowest risk?
Can the scenario be explained to the board?