OpenClaw
Logistics Packet Agent
Validates logistics handoff packets before work moves into execution.
Overview
Validates logistics handoff packets before work moves into execution.
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 Logistics Packet Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Operations.
Class: vertical
Implementation: partial • Phase 2
Objective: Keep the physical world from getting punished by missing details.
Jurisdiction: Reads handoff packets, carrier requirements, approvals, and readiness evidence. It flags and routes; it does not book autonomously.
Decision territory: Is the packet complete, blocked, risky, or ready for approval?
Decision outputs: packet_status, missing_evidence, approval_route, confidence
Approval triggers: Booking recommendation, Packet exception recommendation
Memory scope: Handoff packets, Carrier requirements, Approval history, Failure patterns
System dependencies: Operations, Governance, Partners
Allowed tools: record context, policy engine, event log
Allowed actions: validate packet, flag missing evidence, route approval
Blocked actions: book shipment autonomously, override packet policy
Required policies: freight-packet-completeness-policy, logistics-human-approval-policy
Required evidence: packet data, dimensions, service level, address verification
Performance
Daily operator-facing quality read.
Suggestions
88
Approved
80
Rejected
8
Correction Rate
27%
Time Saved
780 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.
Prepare RXO packet
Complete missing service level and dimensions before RXO delivery packet can move.
Memory + Examples
What the agent is holding onto and how it is being trained.
operations-context
Logistics Packet Agent should preserve the latest durable context needed to make bounded operations decisions without inventing missing truth.
Logistics Packet 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