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/momentum.py

@@ -1,12 +1,57 @@
 from __future__ import annotations
+
+from datetime import datetime, timedelta, timezone
 from typing import ClassVar
+
 from .base import BaseAgent, AgentInput, AgentOutput
-from .manifest import AgentManifest
+from .inference.history import UserHistory
+from .manifest import AgentManifest, InferredParam
+
+
+def _infer_engagement_trend(history: UserHistory) -> str:
+    """Compare done-rate in the most recent 7 days vs the 7 days before that."""
+    events = sorted(history.events, key=lambda e: e.created_at)
+    if not events:
+        return "stable"
+
+    try:
+        latest = datetime.fromisoformat(events[-1].created_at.replace("Z", "+00:00"))
+    except ValueError:
+        return "stable"
+
+    cutoff_recent = latest - timedelta(days=7)
+    cutoff_older = latest - timedelta(days=14)
+
+    recent = [e for e in events if _parse_dt(e.created_at) >= cutoff_recent]
+    older = [e for e in events if cutoff_older <= _parse_dt(e.created_at) < cutoff_recent]
+
+    if len(older) < 3:
+        return "stable"  # not enough baseline to compare
+
+    recent_rate = sum(1 for e in recent if e.action == "done") / max(len(recent), 1)
+    older_rate = sum(1 for e in older if e.action == "done") / max(len(older), 1)
+
+    delta = recent_rate - older_rate
+    if delta > 0.10:
+        return "up"
+    if delta < -0.10:
+        return "down"
+    return "stable"
+
+
+def _parse_dt(iso: str) -> datetime:
+    try:
+        dt = datetime.fromisoformat(iso.replace("Z", "+00:00"))
+        if dt.tzinfo is None:
+            dt = dt.replace(tzinfo=timezone.utc)
+        return dt
+    except ValueError:
+        return datetime.min.replace(tzinfo=timezone.utc)
 
 
 MANIFEST = AgentManifest(
     id="momentum",
-    version="1.0.0",
+    version="1.1.0",  # bumped: engagement_trend InferredParam added (#114)
     description="Characterises the user's recent engagement trend from profile features.",
     pref_schema={
         "type": "object",
@@ -25,6 +70,15 @@ MANIFEST = AgentManifest(
     required_consents=["data:core", "agent:momentum"],
     output_contract={"type": "snippet", "format": "free_text"},
     ttl_sec=21_600,
+    inferred_params=[
+        InferredParam(
+            key="engagement_trend",
+            ttl_sec=21_600,          # recompute every 6 hours alongside snippet
+            cold_start_default="stable",
+            min_history=10,
+            infer=_infer_engagement_trend,
+        ),
+    ],
 )
 
 
@@ -38,6 +92,7 @@ class MomentumAgent(BaseAgent):
         completion = inp.profile.get("completion_rate_30d")
         dismiss = inp.profile.get("dismiss_rate_30d")
         volume = inp.profile.get("tip_volume_30d")
+        trend: str = inp.agent_prefs.get("engagement_trend", "stable")
 
         parts: list[str] = []
 
@@ -65,10 +120,16 @@ class MomentumAgent(BaseAgent):
         if volume is not None and int(volume) < 5:
             parts.append("Very few tips served so far — this is an early-stage user.")
 
+        if trend == "up":
+            parts.append("Engagement is trending up compared to last week — build on the momentum.")
+        elif trend == "down":
+            parts.append("Engagement is trending down — a motivational or easy-win tip may help.")
+
         prompt = " ".join(parts) if parts else "No engagement data available yet."
         snapshot = {
             "completion_rate_30d": completion,
             "dismiss_rate_30d": dismiss,
             "tip_volume_30d": volume,
+            "engagement_trend": trend,
         }
         return self._make_output(inp, prompt, snapshot)