mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-18 07:19:35 +00:00
124 lines
4.3 KiB
Python
124 lines
4.3 KiB
Python
"""Safe AI Agent Runtime for Dealix v3.
|
|
|
|
This is intentionally deterministic and policy-first. It can later be backed by
|
|
LangGraph, OpenAI Agents SDK, CrewAI, or Google ADK without changing the public
|
|
contract.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass, field
|
|
from enum import StrEnum
|
|
from typing import Any
|
|
from uuid import uuid4
|
|
|
|
|
|
class AgentName(StrEnum):
|
|
PROSPECTING = "prospecting"
|
|
SIGNAL = "signal"
|
|
ENRICHMENT = "enrichment"
|
|
PERSONALIZATION = "personalization"
|
|
COMPLIANCE = "compliance"
|
|
OUTREACH = "outreach"
|
|
REPLY = "reply"
|
|
MEETING = "meeting"
|
|
DEAL_COACH = "deal_coach"
|
|
CUSTOMER_SUCCESS = "customer_success"
|
|
EXECUTIVE_ANALYST = "executive_analyst"
|
|
|
|
|
|
class TaskStatus(StrEnum):
|
|
CREATED = "created"
|
|
NEEDS_APPROVAL = "needs_approval"
|
|
APPROVED = "approved"
|
|
EXECUTED = "executed"
|
|
REJECTED = "rejected"
|
|
BLOCKED = "blocked"
|
|
|
|
|
|
@dataclass
|
|
class AgentTask:
|
|
agent: AgentName
|
|
objective: str
|
|
customer_id: str
|
|
context: dict[str, Any] = field(default_factory=dict)
|
|
requires_approval: bool = True
|
|
risk_level: str = "medium"
|
|
task_id: str = field(default_factory=lambda: f"task_{uuid4().hex[:12]}")
|
|
status: TaskStatus = TaskStatus.CREATED
|
|
|
|
def to_dict(self) -> dict[str, Any]:
|
|
return {
|
|
"task_id": self.task_id,
|
|
"agent": self.agent.value,
|
|
"objective": self.objective,
|
|
"customer_id": self.customer_id,
|
|
"context": self.context,
|
|
"requires_approval": self.requires_approval,
|
|
"risk_level": self.risk_level,
|
|
"status": self.status.value,
|
|
}
|
|
|
|
|
|
class SafeAgentRuntime:
|
|
"""Small policy-first runtime for agent tasks."""
|
|
|
|
restricted_actions = {"send_cold_whatsapp", "auto_linkedin_dm", "delete_data", "export_pii"}
|
|
|
|
def __init__(self) -> None:
|
|
self.tasks: dict[str, AgentTask] = {}
|
|
|
|
def create_task(self, task: AgentTask) -> AgentTask:
|
|
action = str(task.context.get("action", ""))
|
|
if action in self.restricted_actions:
|
|
task.status = TaskStatus.BLOCKED
|
|
task.risk_level = "blocked"
|
|
elif task.requires_approval:
|
|
task.status = TaskStatus.NEEDS_APPROVAL
|
|
else:
|
|
task.status = TaskStatus.APPROVED
|
|
self.tasks[task.task_id] = task
|
|
return task
|
|
|
|
def approve(self, task_id: str) -> AgentTask:
|
|
task = self.tasks[task_id]
|
|
if task.status == TaskStatus.BLOCKED:
|
|
return task
|
|
task.status = TaskStatus.APPROVED
|
|
return task
|
|
|
|
def reject(self, task_id: str) -> AgentTask:
|
|
task = self.tasks[task_id]
|
|
task.status = TaskStatus.REJECTED
|
|
return task
|
|
|
|
def execute(self, task_id: str) -> dict[str, Any]:
|
|
task = self.tasks[task_id]
|
|
if task.status not in {TaskStatus.APPROVED, TaskStatus.EXECUTED}:
|
|
return {"ok": False, "task": task.to_dict(), "reason": "approval_required_or_blocked"}
|
|
task.status = TaskStatus.EXECUTED
|
|
return {
|
|
"ok": True,
|
|
"task": task.to_dict(),
|
|
"result": {
|
|
"summary": f"{task.agent.value} completed objective: {task.objective}",
|
|
"next_step": "record_outcome_in_revenue_memory",
|
|
},
|
|
}
|
|
|
|
|
|
def agent_catalog() -> list[dict[str, str]]:
|
|
return [
|
|
{"agent": AgentName.PROSPECTING.value, "job": "Find high-fit Saudi B2B accounts."},
|
|
{"agent": AgentName.SIGNAL.value, "job": "Detect why-now buying triggers."},
|
|
{"agent": AgentName.ENRICHMENT.value, "job": "Complete company/contact context."},
|
|
{"agent": AgentName.PERSONALIZATION.value, "job": "Draft Arabic/English outreach."},
|
|
{"agent": AgentName.COMPLIANCE.value, "job": "Block unsafe PDPL/contactability actions."},
|
|
{"agent": AgentName.OUTREACH.value, "job": "Queue or send approved messages."},
|
|
{"agent": AgentName.REPLY.value, "job": "Classify replies and intent."},
|
|
{"agent": AgentName.MEETING.value, "job": "Convert positive replies into meetings."},
|
|
{"agent": AgentName.DEAL_COACH.value, "job": "Recommend next best deal action."},
|
|
{"agent": AgentName.CUSTOMER_SUCCESS.value, "job": "Prevent churn and surface expansion."},
|
|
{"agent": AgentName.EXECUTIVE_ANALYST.value, "job": "Write founder daily brief."},
|
|
]
|