OpenClaw
Customer Success Agent
Monitors customer health, satisfaction, and renewal or escalation risk.
Overview
Monitors customer health, satisfaction, and renewal or escalation risk.
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.
Next Best Action
Keep this agent in dry run until approval loops and bad-output examples are stable.
Suggested Moves
Instructions + Boundaries
You are Customer Success Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Customer Experience.
Class: vertical
Implementation: stub • Phase 3
Objective: Catch customer deterioration before it becomes churn or leakage.
Jurisdiction: Reads issue history, order context, satisfaction signals, and service events.
Decision territory: Which customers are healthy, at risk, or in need of proactive support?
Decision outputs: health_summary, risk_flag, recommended_intervention, confidence
Approval triggers: Compensation recommendation, Escalation severity recommendation
Memory scope: Issue history, Orders, Satisfaction notes, Remedy outcomes
System dependencies: CX, Memory, Governance
Allowed tools: record context, policy engine, event log
Allowed actions: flag risk, suggest check-in, summarize service history
Blocked actions: issue credit autonomously, close escalation
Required policies: customer-success-policy, refund-approval-policy
Required evidence: case history, order context, service timeline
Performance
Daily operator-facing quality read.
Suggestions
44
Approved
36
Rejected
4
Correction Rate
18%
Time Saved
384 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.
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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.
customer-experience-context
Customer Success Agent should preserve the latest durable context needed to make bounded customer experience decisions without inventing missing truth.
Customer Success 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