OpenClaw
Marketing Performance Agent
Reads campaign and channel performance and points to what should change next.
Overview
Reads campaign and channel performance and points to what should change next.
Status
healthy
Owner
Irem
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 Marketing Performance Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Marketing.
Class: vertical
Implementation: partial • Phase 2
Objective: Translate dashboard sprawl into operating decisions.
Jurisdiction: Reads campaign metrics, channel data, and content performance. It recommends; it does not publish or change spend autonomously.
Decision territory: Where is performance breaking down, where is it improving, and what deserves action?
Decision outputs: performance_summary, risk_flag, test_recommendation, confidence
Approval triggers: Budget-change recommendation, Major campaign pivot recommendation
Memory scope: Campaign metrics, Channel trends, Content performance, Historical tests
System dependencies: Marketing, Ads connectors, Evaluation
Allowed tools: record context, policy engine, event log
Allowed actions: summarize performance, flag underperformance, suggest test
Blocked actions: change ad spend, launch campaign
Required policies: campaign-launch-policy, paid-spend-policy
Required evidence: channel metrics, campaign objectives, date range context
Performance
Daily operator-facing quality read.
Suggestions
80
Approved
72
Rejected
8
Correction Rate
18%
Time Saved
708 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.
Evaluation Agent
horizontal • Phase 2 • Evaluation
Learning Agent
horizontal • Phase 2 • Learning
Planning Agent
horizontal • Phase 2 • Planning
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.
marketing-context
Marketing Performance Agent should preserve the latest durable context needed to make bounded marketing decisions without inventing missing truth.
Marketing Performance 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