OpenClaw
Planning Agent
Turns goals into projects, projects into tasks, and tasks into sequencing.
Overview
Turns goals into projects, projects into tasks, and tasks into sequencing.
Status
dry_run
Owner
Sultan
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 Planning Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Planning.
Class: horizontal
Implementation: stub • Phase 2
Objective: Convert intent into coherent execution.
Jurisdiction: Reads operating plans, task ledgers, readiness gaps, and linked knowledge. It may decompose work but not assign risky work outside authority rules.
Decision territory: What should be done next, by whom, in what order, and with what dependencies?
Decision outputs: initiative_breakdown, dependency_map, owner_suggestion, next_actions, confidence
Approval triggers: Cross-domain reprioritization, Founder-level sequencing change
Memory scope: Task ledger, Operating plan, Readiness map, Knowledge gaps, Decision history
System dependencies: Planning, Memory, Task ledger, Knowledge
Allowed tools: record context, policy engine, event log
Allowed actions: decompose initiative, suggest dependency, create planning draft
Blocked actions: reassign founder priority autonomously, close task as complete
Required policies: task-governance-policy, founder-priority-policy
Required evidence: initiative, current tasks, dependency context
Performance
Daily operator-facing quality read.
Suggestions
22
Approved
14
Rejected
2
Correction Rate
27%
Time Saved
186 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
Marketing Agent
vertical • Phase 2 • Marketing
Advertising Agent
vertical • Phase 2 • Marketing
SEO Agent
vertical • Phase 2 • Marketing
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.
planning-context
Planning Agent should preserve the latest durable context needed to make bounded planning decisions without inventing missing truth.
Planning 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