OpenClaw
Lead Scoring Agent
Scores lead quality, urgency, and fit using visible evidence rather than opaque vibes.
Overview
Scores lead quality, urgency, and fit using visible evidence rather than opaque vibes.
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.
Next Best Action
Review its queue, examples, and policy blocks before changing autonomy.
Suggested Moves
Instructions + Boundaries
You are Lead Scoring Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Sales.
Class: vertical
Implementation: partial • Phase 1
Objective: Make prioritization legible and repeatable.
Jurisdiction: Reads lead profile, engagement history, source signals, and account context. It recommends, but does not suppress or disqualify autonomously.
Decision territory: How promising is this lead, what makes it promising, and where should it sit in the queue?
Decision outputs: lead_score, priority_recommendation, rationale, confidence
Approval triggers: Disqualification recommendation, Suppression recommendation
Memory scope: Lead profile, Engagement history, Account linkage, Sales notes
System dependencies: CRM, Memory, Governance
Allowed tools: record context, policy engine, event log
Allowed actions: score lead, recommend priority, log scoring rationale
Blocked actions: suppress lead, disqualify lead autonomously
Required policies: lead-response-policy, marketing-suppression-policy
Required evidence: lead profile, engagement data, source signals
Performance
Daily operator-facing quality read.
Suggestions
68
Approved
60
Rejected
6
Correction Rate
18%
Time Saved
600 min
Revenue Influenced
$12,000
What It May Do
Authorized behavior inside its operating lane.
What It May Not Do
Things that stay human-owned or separately governed.
Related Agents
Peers in the same layer, phase, or operating domain.
Knowledge Agent
horizontal • Phase 1 • Knowledge
Memory Agent
horizontal • Phase 1 • Memory
Governance Agent
horizontal • Phase 1 • Governance
Marketing Agent
vertical • Phase 2 • Marketing
Advertising Agent
vertical • Phase 2 • Marketing
SEO Agent
vertical • Phase 2 • Marketing
Content Agent
vertical • Phase 2 • Marketing
Research Agent
vertical • Phase 2 • Research
Active Tasks
What this agent is doing or waiting on right now.
Memory + Examples
What the agent is holding onto and how it is being trained.
sales-context
Lead Scoring Agent should preserve the latest durable context needed to make bounded sales decisions without inventing missing truth.
Lead Scoring Agent boundary discipline
Use only available evidence, call out gaps, and route high-risk outcomes through approval law.
Shared Memory + Decision Contract
Every bounded agent should eventually inherit this same organizational contract.
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