OpenClaw
Learning Agent
Looks at failures and successes, then proposes changes to prompts, playbooks, and workflows.
Overview
Looks at failures and successes, then proposes changes to prompts, playbooks, and workflows.
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 Learning Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Learning.
Class: horizontal
Implementation: stub • Phase 2
Objective: Turn repeated outcomes into system improvement rather than repeated pain.
Jurisdiction: Reads outcomes, examples, prompts, and correction logs. It proposes changes but does not promote them directly.
Decision territory: What should change in the operating system because reality keeps teaching the same lesson?
Decision outputs: learning_summary, change_recommendation, confidence, supporting_examples
Approval triggers: Prompt activation recommendation, Policy-impacting change recommendation
Memory scope: Examples, Prompt versions, Correction logs, Outcomes, Playbook history
System dependencies: Evaluation, Memory, Tuning, Playbooks
Allowed tools: record context, policy engine, event log
Allowed actions: propose prompt refinement, summarize failure mode, recommend playbook change
Blocked actions: activate new prompt automatically, change policy automatically
Required policies: tuning-governance-policy, eval-integrity-policy
Required evidence: correction logs, eval results, outcome history
Performance
Daily operator-facing quality read.
Suggestions
20
Approved
12
Rejected
2
Correction Rate
18%
Time Saved
168 min
Revenue Influenced
$12,000
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
Planning Agent
horizontal • Phase 2 • Planning
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.
learning-context
Learning Agent should preserve the latest durable context needed to make bounded learning decisions without inventing missing truth.
Learning 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