Executive Summary

We studied how to operationalize AI-assisted decisioning in environments where auditability and reversibility matter as much as accuracy. The key insight: model outputs should not be treated as “the decision,” but as one input into a decision process that is event-sourced, reproducible, and reviewable.

The result is an architecture where every decision can be reconstructed from an immutable stream of events, enabling reliable incident response, regulator-friendly explanations, and safe iteration.

Abstract

As organizations move from manual triage to partially-automated decisioning (routing, eligibility, prioritization, exception handling), they face recurring failure modes:

• Decisions are not reproducible because prompts/models changed.
• Manual overrides are not captured structurally.
• Downstream systems cannot determine “why” an outcome occurred.

We propose an event-sourced decisioning approach that separates:

• Evidence capture (inputs, retrieved context, model suggestions)
• Deterministic decision policy (rules + constraints)
• Human review actions (overrides, approvals)
• Side-effects (updates, notifications)

Problem Statement

Decisioning systems must satisfy:

• Traceability: What evidence and policy produced the outcome?
• Replayability: Can we reproduce the outcome under the same inputs?
• Safety controls: Can we enforce guardrails and conservative defaults?
• Iteration speed: Can we improve models without breaking governance?

Approach

1) Event Model

We use an append-only stream that includes:

• case.created
• evidence.added (raw documents, structured fields)
• model.suggested (structured proposal + metadata)
• policy.evaluated (rules, constraints, computed scores)
• human.approved / human.overridden
• effect.executed (DB update, email send, task creation)

2) Deterministic Policy Evaluation

Policies are versioned and executed deterministically. Models are used to propose structured candidates; policies decide whether those candidates are eligible for auto-action or require review.

3) Safe Side-Effects

Side-effects are emitted as idempotent commands only after a final approval step (automatic or human). This prevents partial actions from inconsistent state.

System Architecture

• Ingestion: inputs normalized into evidence events
• Retrieval: optional KB/context events with provenance
• Decisioning: policy engine computes outcome + rationale
• Review: operator UI can approve, adjust, or override
• Execution: side-effects are queued with idempotency keys

Evaluation Design

We evaluate along two axes:

1. Operational reliability: rate of inconsistent outcomes, rollback effort, time-to-debug
2. Governance readiness: completeness of audit log, replay success rate, policy/version trace coverage

Results

• Replay success: >99.9% (reconstructable decisions when using pinned policy + model metadata)
• Mean time-to-debug: hours → minutes when tracing outcomes by event stream
• Policy iteration safety: versioned rollout with clean diff between policy revisions

Governance and Controls

• Version and hash every policy revision.
• Record model identifiers and prompt hashes for any model suggestion.
• Require explicit finalization before side-effects (auto or human) to prevent partial automation.

Deliverables

• Event schema and typed SDK for writing and replaying decision streams
• Policy engine reference patterns (deterministic evaluation)
• Review UI primitives for approve/override flows
• Operational playbook for incident response and audits

Deployment Notes

• Pin model versions (or model IDs) for decisions that must remain reproducible.
• Store minimal but sufficient artifacts: prompt hash, model ID, retrieved doc IDs, structured output.
• Enforce “no side-effect without finalization” to prevent partial automation.

Commercial Application

This architecture supports:

• Customer support automation with defensible audit trails
• Finance operations where decision history must be explainable
• Regulated workflows where “why” matters more than raw accuracy

Licensable Outcomes

1. Decision event schema + SDK (TypeScript): typed events and replay helpers
2. Policy engine patterns: deterministic evaluation with versioned policies
3. Review UI primitives: approve/override flows with strong traceability
4. Operational playbook: incident response using event-stream reconstruction

Limitations and Next Work

• Some workflows require capturing external state; deterministic replay must include those snapshots.
• Future work: formal verification of policies and automated drift detection on policy outcomes.

Evaluation Date: December 2025
Status: Architecture validated in production-like workloads; ready for domain-specific adaptation