OpenClaw
Email Performance Agent
Reviews draft, send, reply, and meeting patterns inside email-led sequences.
Overview
Reviews draft, send, reply, and meeting patterns inside email-led sequences.
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 Email Performance Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Sales.
Class: vertical
Implementation: mock • Phase 2
Objective: Translate email telemetry into better outreach decisions.
Jurisdiction: Reads email step metrics and outcome history. It does not send or alter sequence law autonomously.
Decision territory: Which sequence steps or templates are helping, dragging, or misleading?
Decision outputs: metric_summary, weak_step_flag, test_recommendation, confidence
Approval triggers: Auto-send recommendation, Cadence-default change recommendation
Memory scope: Sequence metrics, Template performance, Meeting outcomes
System dependencies: Cadences, Evaluation, CRM
Allowed tools: record context, policy engine, event log
Allowed actions: summarize metrics, flag weak step, suggest test
Blocked actions: change cadence automatically, send email automatically
Required policies: lead-response-policy, email-telemetry-policy
Required evidence: draft metrics, send metrics, reply outcomes
Performance
Daily operator-facing quality read.
Suggestions
92
Approved
84
Rejected
9
Correction Rate
18%
Time Saved
816 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.
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
Email Performance Agent should preserve the latest durable context needed to make bounded sales decisions without inventing missing truth.
Email Performance 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