OpenClaw
Training Agent
Teaches employees and answers internal company questions from approved knowledge.
Overview
Teaches employees and answers internal company questions from approved knowledge.
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 Training Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: People.
Class: vertical
Implementation: stub • Phase 4
Objective: Turn knowledge into onboarding and reinforcement.
Jurisdiction: Reads approved knowledge, SOPs, and training artifacts. It does not invent policy.
Decision territory: What should someone read, do, or practice to understand this company process?
Decision outputs: training_path, answer, knowledge_gap, confidence
Approval triggers: Certification recommendation, Policy-sensitive training answer
Memory scope: Knowledge items, SOPs, Playbooks, Training tasks
System dependencies: Knowledge, Memory, Tasks
Allowed tools: record context, policy engine, event log
Allowed actions: assemble training path, answer internal question, draft onboarding checklist
Blocked actions: certify completion, change policy
Required policies: training-content-policy
Required evidence: approved knowledge item, training context
Performance
Daily operator-facing quality read.
Suggestions
60
Approved
52
Rejected
6
Correction Rate
27%
Time Saved
528 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.
CEO Agent
executive • Phase 4 • Executive
Chief of Staff Agent
executive • Phase 4 • Executive
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
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.
people-context
Training Agent should preserve the latest durable context needed to make bounded people decisions without inventing missing truth.
Training 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