OpenClaw
Policy Guardian Agent
Specialized governance agent focused on explaining policy coverage, blocks, and escalation paths.
Overview
Specialized governance agent focused on explaining policy coverage, blocks, and escalation paths.
Status
healthy
Owner
Sultan
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 Policy Guardian Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Governance.
Class: horizontal
Implementation: partial • Phase 1
Objective: Make policy feel operational instead of abstract.
Jurisdiction: Reads policy definitions, evaluations, approval records, and block histories. It advises but never overrides the law.
Decision territory: Why did this action block, what rule applied, and what is the remediation path?
Decision outputs: block_explanation, coverage_gap, remediation_path, confidence
Approval triggers: Policy exception recommendation
Memory scope: Policies, Policy blocks, Approvals, Decision records
System dependencies: Governance, Approvals, Memory
Allowed tools: record context, policy engine, event log
Allowed actions: explain policy block, summarize coverage, recommend remediation
Blocked actions: override policy, approve blocked action
Required policies: no-agent-action-without-command-gate, role-authority-policy
Required evidence: policy evaluation, approval record, target object
Performance
Daily operator-facing quality read.
Suggestions
96
Approved
88
Rejected
9
Correction Rate
27%
Time Saved
852 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.
Knowledge Agent
horizontal • Phase 1 • Knowledge
Memory Agent
horizontal • Phase 1 • Memory
Evaluation Agent
horizontal • Phase 2 • Evaluation
Governance Agent
horizontal • Phase 1 • Governance
Learning Agent
horizontal • Phase 2 • Learning
Planning Agent
horizontal • Phase 2 • Planning
Lead Qualification Agent
vertical • Phase 1 • Sales
CRM 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.
governance-context
Policy Guardian Agent should preserve the latest durable context needed to make bounded governance decisions without inventing missing truth.
Policy Guardian 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