Phase 3 - Differentiator

Finance Memory and Learning Runtime

Create institutional memory for finance so the platform learns from every governed decision.

Business problem: Organizations repeatedly solve the same problems; knowledge disappears when people leave or when decisions are not replayable. 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

Control	Runtime value
Policy	human-approval-policy
Prior threshold	$50.0K
Simulated threshold	$100.0K
Outcome	escalate
Approval volume change	-18%
Guardrail failures	missing_evidence, low_source_confidence, writeback_requires_human_approval
Hallucination guard	evidence_required_before_recommendation

Replay + audit export

Artifact	Value
Audit package	audit-97ab17991395
Integrity hash	c80fd1aea51cec3d76a8d2e6346938acd1938531f544f8f3f7fd3917f1d6c98c
Regulatory export ready	True
Trace steps	10
Runtime chain	Signal -> Context -> Ontology -> Policy -> Evidence -> Decision -> Governance -> Action -> Replay -> Learning

Runtime APIs

Endpoint
/api/finance-foundations/runtime-proof
/api/finance-foundations/finance-memory-learning-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

Every signal, context, policy, recommendation, human decision, outcome, and override becomes governed memory.

Core components

Component	Capability
Decision Memory Store	Captures signal, context, policy, recommendation, human decision, outcome, evidence, and replay.
Outcome Tracking	Measures whether recommendation was correct, risk occurred, and value was realized.
Learning Engine	Identifies successful decisions, failed decisions, repeat patterns, and reusable practices.
Knowledge Retrieval	Answers have we seen this before and what worked last time.
Learning Dashboard	Improvement, accuracy, autonomy growth, value created, and promotion gates.

Success metrics

Metric	Target
Decision capture	100% target
Retrieval accuracy	95% target
Recommendation quality	Continuous improvement

FP&A deck alignment

Target operating-model theme

Agent family

Agent	Purpose
Memory Agent	Retrieves similar decisions and prior outcomes.
Outcome Agent	Tracks realized outcome and value.
Learning Agent	Creates candidate learning from replay-backed outcomes.
Recommendation Improvement Agent	Suggests changes to future recommendations under governance.

Feedback and recommendation learning governance

Every human feedback event, recommendation acceptance, override, edit, rejection, and outcome becomes a governed learning candidate. Learning improves future recommendations only after owner approval; it never mutates policy, memory, or model behavior silently.

Feedback capture

Approve, reject, edit, request evidence, override, and escalation reasons are stored with case and role.

Recommendation quality

Acceptance rate, false positives, false negatives, confidence drift, and override patterns are tracked.

Learning approval

Policy owner, process owner, control owner, or agent owner approves promotion to enterprise memory.

Value learning

Before/after impact measures exception volume, effort, cash, leakage, risk, and cycle time.

Proof links

Surface	Route
Decision Memory	Open
Replay Learning Evolution	Open
Replay Center	Open
Runtime Command Center	Open

API contract

Endpoint
/api/finance-foundations/finance-memory-learning-runtime
/ui/decision-memory
/ui/replay-learning-evolution

Validation questions

Question
Have we seen this before?
What worked last time?
What did humans override and why?
How do you prevent bad learning?