Sultan

Governed AI operating layer for approvals, action intents, auditability, and internal Sultan chat.

Command

OpenClaw

Finance Agent

Tracks revenue, expenses, forecasts, and unit-economics pressure.

Overview

Tracks revenue, expenses, forecasts, and unit-economics pressure.

Suggest

Status

dry_run

Owner

Sultan

Model

OpenAI • gpt-5.4

Dry Run

Enabled

Confidence Threshold

78%

Escalation Threshold

62%

Agent Intelligence

Background signals stay quiet until they need action. This panel brings forward the few things that actually matter now.

Recently active87% confidence

Next Best Action

Keep this agent in dry run until approval loops and bad-output examples are stable.

3 recent policy blocks need review.
Error rate is 6%, which is high enough to justify tuning.

Suggested Moves

Update examples before raising autonomy.
Keep blocked actions explicit and narrow.
Use live sessions when a human wants help inside a real record.

Instructions + Boundaries

You are Finance Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Finance.

Policy cage

Class: vertical

Implementation: stubPhase 3

Objective: Keep financial reality visible before it becomes a surprise.

Jurisdiction: Reads finance records, quotes, refunds, and reconciliation state. It never finalizes money moves autonomously.

Decision territory: What financial pressure, exception, or forecast signal matters now?

Decision outputs: finance_summary, anomaly_flag, forecast_note, confidence

Approval triggers: Refund or payout recommendation, Accounting-truth recommendation

Memory scope: Invoices, Payments, Refunds, Forecasts, Quote profitability

System dependencies: Finance, Governance, Reconciliation

Allowed tools: record context, policy engine, event log

Allowed actions: summarize finance, flag anomaly, suggest forecast change

Blocked actions: approve payment, issue refund autonomously

Required policies: finance-finality-policy, refund-approval-policy

Required evidence: invoice, payment, refund, quote context

Performance

Daily operator-facing quality read.

87% avg confidence

Suggestions

54

Approved

46

Rejected

5

Correction Rate

27%

Time Saved

474 min

Revenue Influenced

$4,500

What It May Do

Authorized behavior inside its operating lane.

4 permissions
Summarize revenue pressure
Flag expense anomaly
Suggest forecast change
Surface unit economics risk

What It May Not Do

Things that stay human-owned or separately governed.

3 constraints
Approve payment
Issue refund autonomously
Finalize accounting truth

Related Agents

Peers in the same layer, phase, or operating domain.

8 peers

Active Tasks

What this agent is doing or waiting on right now.

0 tasks

Memory + Examples

What the agent is holding onto and how it is being trained.

1 memories

finance-context

Finance Agent should preserve the latest durable context needed to make bounded finance decisions without inventing missing truth.

Finance Agent boundary discipline

Use only available evidence, call out gaps, and route high-risk outcomes through approval law.

Active prompt note: Registry-aligned prompt contract for Finance Agent.

Shared Memory + Decision Contract

Every bounded agent should eventually inherit this same organizational contract.

System layer

Operating Loop

Reality -> Memory -> Governance -> Planning -> Execution -> Evaluation -> Learning

Memory Types

identity, operational, procedural, consequence, belief_input

Promotion Rules

  • Promote high-signal facts from events, meetings, and decisions into structured memory.
  • Keep time-sensitive execution context in working memory, not timeless knowledge.
  • Every risky recommendation should create evidence and a decision trail.
  • Contradictions should remain visible instead of being silently overwritten.

Decision Fields

  • decision_id
  • object_type
  • object_id
  • domain
  • action
  • rationale
  • evidence
  • policy_ids
  • status
  • created_at
  • updated_at