"""Weekly external research loop for Architect Governance Agent.

The agent plans and normalizes weekly research tasks. Actual internet access is
provided by the runtime/search worker; this module defines the audited workflow,
findings, impact analysis, and V/R/C recommendations.
"""

from __future__ import annotations

from dataclasses import asdict, dataclass, field
from datetime import datetime, timezone
from typing import Any
from uuid import uuid4


def utc_now() -> str:
    return datetime.now(timezone.utc).isoformat()


@dataclass(frozen=True)
class ResearchTarget:
    id: str
    topic: str
    source_type: str
    query: str
    cadence: str = "weekly"

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass(frozen=True)
class ResearchFinding:
    id: str
    target_id: str
    title: str
    summary: str
    source: str
    relevance: str
    impact: str
    recommendation: str
    created_at: str = field(default_factory=utc_now)

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass(frozen=True)
class ResearchDecision:
    status: str
    version_bump: str
    reason: str
    findings: list[ResearchFinding]
    created_at: str = field(default_factory=utc_now)

    def to_dict(self) -> dict[str, Any]:
        return {
            "status": self.status,
            "version_bump": self.version_bump,
            "reason": self.reason,
            "findings": [f.to_dict() for f in self.findings],
            "created_at": self.created_at,
        }


class ArchitectWeeklyResearchAgent:
    DEFAULT_TARGETS = (
        ResearchTarget("agent-frameworks", "Agent framework versions", "web", "latest multi-agent orchestration framework releases"),
        ResearchTarget("model-ecosystem", "Model ecosystem changes", "web", "latest LLM model releases tool calling agent features"),
        ResearchTarget("github-top-agentops", "GitHub top AgentOps implementations", "github", "top repositories agent observability multi agent orchestration"),
        ResearchTarget("security-agent-risks", "Agent security changes", "web", "latest AI agent security sandbox prompt injection tool use risks"),
    )

    def plan(self) -> dict[str, Any]:
        return {
            "status": "planned",
            "cadence": "weekly",
            "targets": [target.to_dict() for target in self.DEFAULT_TARGETS],
            "created_at": utc_now(),
        }

    def normalize_finding(
        self,
        target_id: str,
        title: str,
        summary: str,
        source: str,
        relevance: str = "medium",
        impact: str = "none",
        recommendation: str = "monitor",
    ) -> ResearchFinding:
        return ResearchFinding(
            id=f"finding_{uuid4().hex[:12]}",
            target_id=target_id,
            title=title,
            summary=summary,
            source=source,
            relevance=relevance,
            impact=impact,
            recommendation=recommendation,
        )

    def decide(self, findings: list[ResearchFinding]) -> ResearchDecision:
        if any(f.impact == "breaking" for f in findings):
            return ResearchDecision("action_required", "V", "breaking external ecosystem change", findings)
        if any(f.impact in {"architecture", "new_capability"} for f in findings):
            return ResearchDecision("optimization_recommended", "R", "external capability may improve architecture", findings)
        if any(f.impact in {"bugfix", "minor"} for f in findings):
            return ResearchDecision("minor_change_recommended", "C", "minor compatible improvement", findings)
        return ResearchDecision("no_action", "none", "no relevant external change", findings)


__all__ = ["ResearchTarget", "ResearchFinding", "ResearchDecision", "ArchitectWeeklyResearchAgent"]