Put work items in the state store, and bind the log to them (M1.1)

Development Plan M1.1, as discussed.

- Schema v4: a work_items table (id, scope, title, status, outcome,
  position, data) and an agent row whose active_item points at one
  (NULL: idle). Canonical events and intents carry their work item, and
  events their actor. Older stores are refused with a clear message:
  there was no production data, so no migration (the mechanism stays).
- The log is reached through a Transcript bound to one work item and
  one actor: store.transcript(item, actor) reads only that item and
  writes with both. The planner, executor, delegation and expert desk
  get one per call from the orchestrator instead of holding the store,
  so a component can neither read another task's events (the planner
  read every earlier task's, #78) nor write without attribution.
  store.all_events() is kept for readers that span items (evaluation
  capture, dumps). Intents are begun through the transcript or with
  work_item=, so each belongs to its item.
- The orchestrator opens a work item on 'use', closes it with its
  outcome (completed or abandoned), and carries what the operator said
  while idle into the new item as copies with their origin, so a task
  sent before 'use' is still planned from (D1). control.py keeps its
  state machine through get_state()/transition(), now over the active
  work item, until M2.6 retires it.
- nisse_dev.dump shows work items and each event's item and actor.

Tests: test_work_items.py (scoping, attribution, intent ownership,
restart, outcomes, carried messages, a second task never seeing the
first); the suites updated for the per-call transcript.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PPQ14L3LikyefCMFBqEbKE

nisse-dev/tests/test_context.py

@@ -248,7 +248,7 @@ def test_the_executor_sees_only_its_milestone(tmp_path: Path) -> None:
         GateOpened(gate="milestone-1", reason="plan submitted"),
         UserMessage(text="start milestone 1", source="t"),
     ):
-        store.append_event(event)
+        store.transcript("t", "executor").append(event)
 
     class Workspace:
         def describe(self) -> str:
@@ -263,7 +263,7 @@ def test_the_executor_sees_only_its_milestone(tmp_path: Path) -> None:
             return [AssistantText(text="done", model=model)]
 
     roles = RoleMap(executor="m", planner="m", helper="m", expert="m")
-    LLMExecutor(Client(), store, roles, Workspace()).run(1)
+    LLMExecutor(Client(), roles, Workspace()).run(store.transcript("t", "executor"), 1)
 
     texts = [getattr(e, "text", None) for e in seen["events"]]
     assert "start milestone 1" in texts
@@ -278,7 +278,9 @@ def test_a_request_that_cannot_fit_stops_the_milestone(tmp_path: Path) -> None:
     # Never sent: the executor stops and asks the operator, like any failed
     # call, rather than handing a server a prompt it would silently cut.
     store = StateStore(tmp_path / "state.db", oplog=OpLog("t", io.StringIO()))
-    store.append_event(UserMessage(text="start milestone 1", source="t"))
+    store.transcript("t", "operator").append(
+        UserMessage(text="start milestone 1", source="t")
+    )
 
     class Workspace:
         def describe(self) -> str:
@@ -287,7 +289,9 @@ def test_a_request_that_cannot_fit_stops_the_milestone(tmp_path: Path) -> None:
     inner = RecordingClient()
     client = BudgetedClient(inner, lambda m: (4096, 1024))
     roles = RoleMap(executor="m", planner="m", helper="m", expert="m")
-    outcome = LLMExecutor(client, store, roles, Workspace()).run(1)
+    outcome = LLMExecutor(client, roles, Workspace()).run(
+        store.transcript("t", "executor"), 1
+    )
 
     assert outcome.status == "escalate"
     assert "fit" in outcome.note