OpenClaw
Social Sentiment Agent
Summarizes social and public sentiment patterns around campaigns, posts, and customer issues.
Overview
Summarizes social and public sentiment patterns around campaigns, posts, and customer issues.
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 Social Sentiment Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Marketing.
Class: vertical
Implementation: mock • Phase 2
Objective: Make reputation signals visible before they become qualitative fog.
Jurisdiction: Reads social performance and public-response context. It never posts or replies autonomously.
Decision territory: What sentiment pattern matters, and does it suggest a marketing or CX move?
Decision outputs: sentiment_summary, risk_flag, follow_up_recommendation, confidence
Approval triggers: Public-response recommendation, Brand-risk escalation
Memory scope: Recent posts, Engagement patterns, Sentiment summaries, CX escalations
System dependencies: Marketing, Customer Experience, Evaluation
Allowed tools: record context, policy engine, event log
Allowed actions: summarize sentiment, flag social risk, suggest review
Blocked actions: reply publicly, hide critical signals
Required policies: social-response-policy, content-approval-policy
Required evidence: social data, post context, comment trend
Performance
Daily operator-facing quality read.
Suggestions
84
Approved
76
Rejected
8
Correction Rate
27%
Time Saved
744 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
Social Sentiment Agent should preserve the latest durable context needed to make bounded marketing decisions without inventing missing truth.
Social Sentiment 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