feat(agents): per-agent inference — momentum, overdue-task, recent-patterns, focus-area (ADR-0014 step 7)

All four agents bumped to v1.1.0.

momentum (#114): infers engagement_trend ('up'|'stable'|'down') by comparing
done-rate in the last 7 days vs the prior 7 days. Agent surfaces the trend
in its snippet ("trending up — build on the momentum").

overdue-task (#115): infers lateness_tolerance_days (0/1/2) from snooze rate.
Agent now filters tasks against the tolerance so low-urgency users aren't
nagged about tasks that are only hours overdue.

recent-patterns (#116): infers window_days (7/14/30) from feedback event
density — sparse users get a wider window so the snippet isn't always empty.

focus-area (#113): no inferred params (project-level feedback linkage needed,
tracked under #78). preferred_areas pref was declared but ignored; agent now
honours it as a tiebreaker and mentions it in the snippet.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

ml/agents/focus_area.py

@@ -1,13 +1,15 @@
 from __future__ import annotations
+
 from collections import defaultdict
 from typing import ClassVar
+
 from .base import BaseAgent, AgentInput, AgentOutput
 from .manifest import AgentManifest
 
 
 MANIFEST = AgentManifest(
     id="focus-area",
-    version="1.0.0",
+    version="1.1.0",  # bumped: preferred_areas pref is now honoured in compute (#113)
     description="Identifies the most congested project/area in the user's task list.",
     pref_schema={
         "type": "object",
@@ -25,6 +27,9 @@ MANIFEST = AgentManifest(
     required_consents=["data:core", "data:todoist", "agent:focus-area"],
     output_contract={"type": "snippet", "format": "free_text"},
     ttl_sec=43_200,
+    # No inferred_params: preferred_areas requires project-level feedback linkage
+    # that isn't available in feedback_history alone. Revisit with #78 (signal
+    # abstraction) once per-task reactions can be traced back to a project.
 )
 
 
@@ -35,6 +40,7 @@ class FocusAreaAgent(BaseAgent):
     version: ClassVar[str] = MANIFEST.version
 
     def compute(self, inp: AgentInput) -> AgentOutput:
+        preferred: list[str] = inp.agent_prefs.get("preferred_areas", [])
         by_project: dict[str, list[dict]] = defaultdict(list)
         for task in inp.tasks:
             project = task.get("project_id") or task.get("project") or "default"
@@ -44,19 +50,27 @@ class FocusAreaAgent(BaseAgent):
             prompt = "No tasks available to identify a focus area."
             return self._make_output(inp, prompt, {"project_count": 0})
 
-        # Score each project: overdue tasks count double
-        def score(tasks: list[dict]) -> float:
-            return sum(2.0 if t.get("is_overdue") else 1.0 for t in tasks)
+        def score(project: str, tasks: list[dict]) -> tuple[float, bool]:
+            base = sum(2.0 if t.get("is_overdue") else 1.0 for t in tasks)
+            # Boost preferred areas to break ties in their favour
+            boosted = project in preferred or any(p in project for p in preferred)
+            return (base + (0.5 if boosted else 0.0), boosted)
 
-        top_project, top_tasks = max(by_project.items(), key=lambda kv: score(kv[1]))
+        top_project, top_tasks = max(
+            by_project.items(),
+            key=lambda kv: score(kv[0], kv[1]),
+        )
         overdue_in_top = sum(1 for t in top_tasks if t.get("is_overdue"))
         label = "the default project" if top_project == "default" else f'"{top_project}"'
         n = len(top_tasks)
+        boosted = top_project in preferred or any(p in top_project for p in preferred)
 
         parts = [
             f"The user's most congested area is {label} "
             f"({n} task{'s' if n != 1 else ''}, {overdue_in_top} overdue)."
         ]
+        if boosted:
+            parts.append("This area matches the user's stated focus preferences.")
         if overdue_in_top >= 3:
             parts.append("Consider surfacing an action from this area.")
 
@@ -66,5 +80,6 @@ class FocusAreaAgent(BaseAgent):
             "top_task_count": n,
             "top_overdue_count": overdue_in_top,
             "project_count": len(by_project),
+            "preferred_areas": preferred,
         }
         return self._make_output(inp, prompt, snapshot)