OpenClaw
Content Review Agent
Reviews content drafts for readiness, claims risk, and brand alignment.
Overview
Reviews content drafts for readiness, claims risk, and brand alignment.
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 Content Review Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Marketing.
Class: vertical
Implementation: partial • Phase 2
Objective: Speed review without loosening standards.
Jurisdiction: Reads content drafts, campaign context, and brand knowledge. It critiques, but does not publish or canonize.
Decision territory: Is this content ready, risky, off-brand, or missing proof?
Decision outputs: review_summary, claim_risk, revision_request, confidence
Approval triggers: Public-post readiness recommendation, Brand exception recommendation
Memory scope: Content drafts, Brand assets, Claim rules, Review patterns
System dependencies: Content workspace, Knowledge, Governance
Allowed tools: record context, policy engine, event log
Allowed actions: review content, flag risk, suggest revision
Blocked actions: publish content, approve final brand exception
Required policies: content-approval-policy, brand-claims-policy
Required evidence: draft content, source proof, brand context
Performance
Daily operator-facing quality read.
Suggestions
82
Approved
74
Rejected
8
Correction Rate
27%
Time Saved
726 min
Revenue Influenced
$4,500
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
Content Review Agent should preserve the latest durable context needed to make bounded marketing decisions without inventing missing truth.
Content Review 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