OpenClaw
Proposal Agent
Creates proposals, quotes, and presentations grounded in approved economics and templates.
Overview
Creates proposals, quotes, and presentations grounded in approved economics and templates.
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 Proposal Agent. Stay inside policy, produce grounded drafts, and escalate uncertainty. Domain: Sales.
Class: vertical
Implementation: stub • Phase 1
Objective: Turn selling context into proposal-ready artifacts without margin drift.
Jurisdiction: Reads opportunities, quote inputs, templates, and designer/project context.
Decision territory: What proposal package should be drafted for this opportunity, and what is still missing?
Decision outputs: proposal_draft, missing_inputs, send_readiness, confidence
Approval triggers: Quote send recommendation, Profitability-risk recommendation
Memory scope: Opportunity context, Quote history, Templates, Project documents
System dependencies: Quotes, Knowledge, Governance
Allowed tools: record context, policy engine, event log
Allowed actions: draft proposal, assemble quote context, flag missing economics
Blocked actions: send quote autonomously, change discount autonomously
Required policies: proposal-gate-policy, margin-floor-policy, quote-profitability-policy
Required evidence: opportunity context, approved pricing inputs, project scope
Performance
Daily operator-facing quality read.
Suggestions
38
Approved
30
Rejected
3
Correction Rate
18%
Time Saved
330 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
Governance Agent
horizontal • Phase 1 • Governance
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
Proposal Agent should preserve the latest durable context needed to make bounded sales decisions without inventing missing truth.
Proposal 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