Phase 1 - Immediate foundation

Policy Intelligence Runtime

Create a policy execution platform where every material finance decision is governed by machine-readable policy.

Business problem: Policies live in PDFs, SharePoint, SOPs, spreadsheets, and human memory; agents need executable controls. 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/policy-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.
North Star runtime outcome

Every decision carries policy coverage, explainability, escalation, and auditability.

Core components
ComponentCapability
Policy RepositoryApproval policies, SOX controls, treasury limits, credit rules, journal rules, procurement policies.
Policy EngineExecutable if/then controls for exposure, materiality, authority, segregation, and write-back.
Policy SimulationTest changes such as approval threshold movement and estimate downstream impact.
Policy GraphPolicy -> Entity -> Process -> Decision relationships.
Policy ExplainabilityWhy triggered, evidence used, expected outcome, escalation path.
Success metrics
MetricTarget
Decision policy coverage100% target
Policy execution<500ms target
Explainability100% target
FP&A deck alignment
Target operating-model theme
Agent family
AgentPurpose
Policy Interpretation AgentTurns policy source text into candidate executable rules.
Policy Validation AgentTests rule behavior against historical decisions and exceptions.
Policy Conflict AgentDetects incompatible thresholds, authority gaps, and process conflicts.
Proof links
SurfaceRoute
Policy LibraryOpen
Access ControlsOpen
Runtime Command CenterOpen
Replay CenterOpen
API contract
Endpoint
/api/finance-foundations/policy-intelligence-runtime
/ui/policy-library
/system/governance/policies
Validation questions
Question
Which policy blocked this action?
Who owns the policy and what version ran?
What if the approval threshold changes?
Can a human override and is the override audited?