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