Sultan

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

Command

OpenClaw

Reply Classification Agent

Classifies prospect replies and recommends the next governed action.

Overview

Classifies prospect replies and recommends the next governed action.

Suggest

Status

healthy

Owner

Sultan

Model

OpenAI • gpt-5.4

Dry Run

Disabled

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 active83% confidence

Next Best Action

Review its queue, examples, and policy blocks before changing autonomy.

Margin floor passed.

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 Reply Classification Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Sales.

Policy cage

Class: vertical

Implementation: partialPhase 1

Objective: Turn inbox noise into clear state changes and next moves.

Jurisdiction: Reads inbound replies, cadence history, and linked records. It classifies and suggests, but does not send or close records autonomously.

Decision territory: Is this a positive reply, objection, stop signal, nurture signal, or routing event?

Decision outputs: reply_classification, next_action, suppression_signal, confidence

Approval triggers: Suppression recommendation, Opportunity-stage change recommendation

Memory scope: Reply history, Cadence history, Lead state, Suppression state

System dependencies: CRM, Cadences, Governance

Allowed tools: record context, policy engine, event log

Allowed actions: classify reply, recommend next action, create follow-up task

Blocked actions: send response autonomously, advance stage autonomously

Required policies: lead-response-policy, marketing-suppression-policy

Required evidence: reply content, cadence context, lead state

Performance

Daily operator-facing quality read.

83% avg confidence

Suggestions

72

Approved

64

Rejected

7

Correction Rate

27%

Time Saved

636 min

Revenue Influenced

$4,500

What It May Do

Authorized behavior inside its operating lane.

4 permissions
Classify reply
Recommend next action
Flag unsubscribe or suppression
Create follow-up task

What It May Not Do

Things that stay human-owned or separately governed.

3 constraints
Send reply autonomously
Advance opportunity stage autonomously
Ignore compliance signals

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

sales-context

Reply Classification Agent should preserve the latest durable context needed to make bounded sales decisions without inventing missing truth.

Reply Classification 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 Reply Classification 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