Kairo-Jinkendo/backend/steering/strategies/next_action/execution_ready.py
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AP2.0d: Next-Action-Strategien nutzen Execution-Graph ready_actions.
Archetyp-Strategien priorisieren ausführungsbereite APs mit Begründung execution_ready/critical_path; continuous_product und program_delivery integriert.

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-12 12:04:40 +02:00

196 lines
6.3 KiB
Python

"""Execution-ready ranking for NextAction strategies — AP2.0d."""
from __future__ import annotations
from typing import Any, Iterable
from steering.graph.execution_engine import load_initiative_execution_graph_state
from tenant_context import TenantContext
_OPEN_ACTION = frozenset({"open", "ready", "in_progress", "blocked", "review_required"})
def rank_ready_action_ids(
graph_state: dict[str, Any],
*,
prefer_critical_path: bool = True,
) -> list[str]:
"""Order ready_actions: critical path first, then sort_order."""
ready = [str(action_id) for action_id in graph_state.get("ready_actions", [])]
if not ready:
return []
items = graph_state.get("items") or {}
ready_set = set(ready)
ordered: list[str] = []
if prefer_critical_path:
for action_id in graph_state.get("critical_path") or []:
aid = str(action_id)
if aid in ready_set and aid not in ordered:
ordered.append(aid)
remaining = [aid for aid in ready if aid not in ordered]
remaining.sort(
key=lambda action_id: (
items.get(action_id, {}).get("sort_order", 0),
action_id,
)
)
ordered.extend(remaining)
return ordered
def action_to_next_candidate(
action: dict[str, Any],
*,
reason_code: str = "execution_ready",
summary: str = "Ausführungsbereit — keine blockierenden Vorgänger",
recommended_action: str = "Als Nächstes ausführen",
on_critical_path: bool = False,
) -> dict[str, Any]:
"""Convert action row to NextAction candidate."""
if on_critical_path and reason_code == "execution_ready":
reason_code = "execution_critical_path"
summary = "Kritischer Pfad — als Nächstes ausführen"
return {
"kind": "action",
"title": action.get("title") or "Arbeitspaket",
"summary": summary,
"initiative_id": str(action["initiative_id"]),
"action_id": str(action["id"]),
"backlog_item_id": None,
"reason_code": reason_code,
"recommended_action": recommended_action,
}
def build_execution_ready_candidates(
*,
actions: list[dict[str, Any]],
graph_state: dict[str, Any],
limit: int,
prefer_critical_path: bool = True,
reason_code: str = "execution_ready",
) -> list[dict[str, Any]]:
"""Build ranked NextAction candidates from a preloaded graph state."""
if limit < 1:
return []
action_by_id = {str(action["id"]): action for action in actions}
critical_path = {str(action_id) for action_id in graph_state.get("critical_path") or []}
blocked_ids = {str(action_id) for action_id in graph_state.get("blocked_actions") or []}
candidates: list[dict[str, Any]] = []
for action_id in rank_ready_action_ids(
graph_state, prefer_critical_path=prefer_critical_path
):
action = action_by_id.get(action_id)
if not action:
continue
if action.get("status") not in _OPEN_ACTION:
continue
if action_id in blocked_ids:
continue
candidates.append(
action_to_next_candidate(
action,
reason_code=reason_code,
on_critical_path=action_id in critical_path,
)
)
if len(candidates) >= limit:
break
return candidates
def list_execution_ready_candidates(
ctx: TenantContext,
initiative_id: str,
*,
limit: int,
scope_roadmap_item_id: str | None = None,
prefer_critical_path: bool = True,
reason_code: str = "execution_ready",
) -> list[dict[str, Any]]:
"""Load execution graph and return ranked ready-action candidates."""
from services import actions as action_service
actions = action_service.list_actions_for_initiative(
tenant_id=ctx.tenant_id, initiative_id=initiative_id
)
graph_state = load_initiative_execution_graph_state(
tenant_id=ctx.tenant_id,
initiative_id=initiative_id,
scope_roadmap_item_id=scope_roadmap_item_id,
)
return build_execution_ready_candidates(
actions=actions,
graph_state=graph_state,
limit=limit,
prefer_critical_path=prefer_critical_path,
reason_code=reason_code,
)
def merge_candidates(
*sources: Iterable[dict[str, Any]],
limit: int,
exclude_action_ids: set[str] | None = None,
) -> list[dict[str, Any]]:
"""Merge candidate lists without duplicate action_ids."""
excluded = exclude_action_ids or set()
merged: list[dict[str, Any]] = []
seen_actions: set[str] = set()
for source in sources:
for item in source:
action_id = item.get("action_id")
if action_id:
if action_id in excluded or action_id in seen_actions:
continue
seen_actions.add(action_id)
merged.append(item)
if len(merged) >= limit:
return merged
return merged
def blocked_execution_action_ids(
ctx: TenantContext, initiative_id: str, *, scope_roadmap_item_id: str | None = None
) -> set[str]:
"""Action IDs blocked by execution graph (not necessarily status=blocked)."""
graph_state = load_initiative_execution_graph_state(
tenant_id=ctx.tenant_id,
initiative_id=initiative_id,
scope_roadmap_item_id=scope_roadmap_item_id,
)
return {str(action_id) for action_id in graph_state.get("blocked_actions") or []}
def first_active_gate_id(ctx: TenantContext, initiative_id: str) -> str | None:
"""First active roadmap item for initiative scope (Gate-Horizont)."""
from db import get_connection
from psycopg2.extras import RealDictCursor
conn = get_connection()
try:
with conn.cursor(cursor_factory=RealDictCursor) as cur:
cur.execute(
"""
SELECT ri.id
FROM roadmap_items ri
JOIN roadmaps r ON r.id = ri.roadmap_id AND r.tenant_id = ri.tenant_id
WHERE ri.tenant_id = %s AND r.initiative_id = %s
AND ri.status = 'active'
ORDER BY ri.target_date ASC NULLS LAST, ri.updated_at DESC
LIMIT 1
""",
(ctx.tenant_id, initiative_id),
)
row = cur.fetchone()
return str(row["id"]) if row else None
finally:
conn.close()