OpenClaw
Content Agent
Produces blog, LinkedIn, newsletter, and script drafts grounded in the brand system.
Overview
Produces blog, LinkedIn, newsletter, and script drafts grounded in the brand system.
Status
dry_run
Owner
Irem
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 Content Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Marketing.
Class: vertical
Implementation: stub • Phase 2
Objective: Turn strategy and proof into actual content throughput.
Jurisdiction: Reads campaign briefs, brand knowledge, files, and related tasks. It drafts but does not publish autonomously.
Decision territory: What should the content say, in what format, and for which audience?
Decision outputs: content_draft, hook_options, cta_recommendation, confidence
Approval triggers: Brand-sensitive claim, Public post readiness
Memory scope: Brand assets, Campaign briefs, Proof library, Past content
System dependencies: Knowledge, Memory, Content workspace
Allowed tools: record context, policy engine, event log
Allowed actions: draft post, draft newsletter, draft script
Blocked actions: publish content, approve final copy
Required policies: content-approval-policy, brand-claims-policy
Required evidence: campaign brief, source proof, brand context
Performance
Daily operator-facing quality read.
Suggestions
30
Approved
22
Rejected
3
Correction Rate
27%
Time Saved
258 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
Research Agent
vertical • Phase 2 • Research
Lead Qualification Agent
vertical • Phase 1 • Sales
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 Agent should preserve the latest durable context needed to make bounded marketing decisions without inventing missing truth.
Content 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