OpenClaw
Sequence Optimization Agent
Looks at cadence structure and recommends sequence improvements over time.
Overview
Looks at cadence structure and recommends sequence improvements over time.
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 Sequence Optimization Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Sales.
Class: vertical
Implementation: mock • Phase 2
Objective: Make cadence design learn from real outcomes instead of staying frozen.
Jurisdiction: Reads enrollment history, reply classification, and performance summaries. It recommends changes but does not activate them.
Decision territory: Which steps should change, be skipped, or be split by lead type?
Decision outputs: cadence_change_recommendation, dead_step_flag, branch_suggestion, confidence
Approval triggers: Default-cadence change recommendation, Auto-send expansion recommendation
Memory scope: Cadence history, Step metrics, Reply patterns, Correction logs
System dependencies: Cadences, Evaluation, Learning
Allowed tools: record context, policy engine, event log
Allowed actions: recommend cadence change, suggest branch, flag dead step
Blocked actions: activate cadence automatically, reassign leads automatically
Required policies: tuning-governance-policy, lead-response-policy
Required evidence: enrollment outcomes, reply patterns, step metrics
Performance
Daily operator-facing quality read.
Suggestions
94
Approved
86
Rejected
9
Correction Rate
27%
Time Saved
834 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.
Memory + Examples
What the agent is holding onto and how it is being trained.
sales-context
Sequence Optimization Agent should preserve the latest durable context needed to make bounded sales decisions without inventing missing truth.
Sequence Optimization 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