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

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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>
This commit is contained in:
alvis
2026-05-05 11:21:10 +00:00
parent ad6747c242
commit afb0e9b0cb
8 changed files with 383 additions and 26 deletions
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14
CLAUDE.md
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@@ -106,11 +106,10 @@ Recent completions:
- Model benchmarking for tip generation (#93, #95)
- Admin UX refinements: feedback consolidation, settings placement (#100–102)
- ADR-0012 — ε-greedy v2 (D=12) — 2026-04-26 (now superseded by ADR-0013)
- ADR-0014 steps 1–6: unified Profile schema + backfill, manifest plumbing, `/api/profile` read-through, registry-driven eligibility filter, inference framework + time-of-day migration — 2026-05-05
- ADR-0014 steps 1–7: unified Profile schema + backfill, manifest plumbing, `/api/profile` read-through, registry-driven eligibility filter, inference framework + per-agent inference (#112–#116) — 2026-05-05
Active work (M2):
- ADR-0014 step 7 — per-agent inference: focus-area (#113), momentum (#114), overdue-task (#115), recent-patterns (#116)
- ADR-0014 step 8 — drop `users.consentGiven` column
- ADR-0014 step 8 — drop `users.consentGiven` column (one release after step 2)
- Signal abstraction for multi-source support (#78)
- Per-user feature freshness SLAs (#61, ADR-0011 phase B)
@@ -133,7 +132,14 @@ Lives in `ml/agents/inference/`. `run_inference(manifest, history)` evaluates al
- `infer()` error → emit `cold_start_default` (never crashes)
- Results written to `user_preferences` with `source='inferred'`; keys with `source='user'` are never overwritten
Time-of-day agent (`1.1.0`) is the proof agent (#112): infers `preferred_hour` (mode done-hour) and reads `quiet_start`/`quiet_end` from prefs.
All five agents are at v1.1.0. Per-agent inferred params:
| Agent | Inferred param | Logic |
|-------|---------------|-------|
| `time-of-day` | `preferred_hour` | Mode done-hour from feedback history |
| `momentum` | `engagement_trend` | Done-rate last 7d vs prior 7d |
| `overdue-task` | `lateness_tolerance_days` | Snooze rate → 0/1/2 days |
| `recent-patterns` | `window_days` | Event density → 7/14/30 days |
| `focus-area` | *(none yet)* | Needs project-level feedback linkage (#78) |
## What NOT to do

25
ml/agents/focus_area.py
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@@ -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)

65
ml/agents/momentum.py
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@@ -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)

40
ml/agents/overdue_task.py
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@@ -1,12 +1,32 @@
from __future__ import annotations
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_lateness_tolerance(history: UserHistory) -> int:
"""Estimate how many days past due a task needs to be before the user acts.
High snooze rate → user doesn't act immediately → raise tolerance so the
agent doesn't nag them about tasks they'll handle in their own time.
"""
total = len(history.events)
if total == 0:
return 0
snooze_rate = sum(1 for e in history.events if e.action == "snooze") / total
if snooze_rate > 0.40:
return 2
if snooze_rate > 0.20:
return 1
return 0
MANIFEST = AgentManifest(
id="overdue-task",
version="1.0.0",
version="1.1.0", # bumped: lateness_tolerance_days InferredParam added (#115)
description="Reports the user's overdue tasks by count and age.",
pref_schema={
"type": "object",
@@ -25,6 +45,15 @@ MANIFEST = AgentManifest(
output_contract={"type": "snippet", "format": "free_text"},
ttl_sec=3600,
silenced_in_contexts=["vacation"],
inferred_params=[
InferredParam(
key="lateness_tolerance_days",
ttl_sec=86_400, # recompute daily — snooze pattern shifts slowly
cold_start_default=0,
min_history=10,
infer=_infer_lateness_tolerance,
),
],
)
@@ -35,7 +64,11 @@ class OverdueTaskAgent(BaseAgent):
version: ClassVar[str] = MANIFEST.version
def compute(self, inp: AgentInput) -> AgentOutput:
overdue = [t for t in inp.tasks if t.get("is_overdue")]
tolerance = max(0, int(inp.agent_prefs.get("lateness_tolerance_days", 0)))
overdue = [
t for t in inp.tasks
if t.get("is_overdue") and t.get("task_age_days", 0) >= tolerance
]
top = sorted(overdue, key=lambda t: -t.get("task_age_days", 0))[:3]
if not overdue:
@@ -59,6 +92,7 @@ class OverdueTaskAgent(BaseAgent):
snapshot = {
"overdue_count": len(overdue),
"lateness_tolerance_days": tolerance,
"top_overdue": [
{"content": t["content"], "task_age_days": t.get("task_age_days", 0)}
for t in top

46
ml/agents/recent_patterns.py
View File

@@ -1,17 +1,32 @@
from __future__ import annotations
from collections import Counter
from datetime import datetime, timezone
from typing import ClassVar
from .base import BaseAgent, AgentInput, AgentOutput
from .manifest import AgentManifest
_SEVEN_DAYS_S = 7 * 86_400
from .base import BaseAgent, AgentInput, AgentOutput
from .inference.history import UserHistory
from .manifest import AgentManifest, InferredParam
def _infer_window_days(history: UserHistory) -> int:
"""Infer the optimal lookback window from feedback event density.
More events per day → a shorter window captures the user's current state
accurately. Sparse feedback → widen the window to gather signal.
"""
n = len(history.events)
if n >= 14:
return 7
if n >= 7:
return 14
return 30
MANIFEST = AgentManifest(
id="recent-patterns",
version="1.0.0",
description="Surfaces the user's reaction pattern from the last 7 days of feedback.",
version="1.1.0", # bumped: window_days InferredParam added (#116)
description="Surfaces the user's reaction pattern from recent feedback.",
pref_schema={
"type": "object",
"additionalProperties": False,
@@ -29,20 +44,32 @@ MANIFEST = AgentManifest(
required_consents=["data:core", "agent:recent-patterns"],
output_contract={"type": "snippet", "format": "free_text"},
ttl_sec=86_400,
inferred_params=[
InferredParam(
key="window_days",
ttl_sec=86_400, # recompute daily alongside snippet
cold_start_default=7,
min_history=5,
infer=_infer_window_days,
),
],
)
class RecentPatternsAgent(BaseAgent):
"""Surfaces the user's reaction pattern from the last 7 days of feedback."""
"""Surfaces the user's reaction pattern from recent feedback."""
agent_id: ClassVar[str] = MANIFEST.id
ttl_seconds: ClassVar[int] = MANIFEST.ttl_sec
version: ClassVar[str] = MANIFEST.version
def compute(self, inp: AgentInput) -> AgentOutput:
window_days = max(1, int(inp.agent_prefs.get("window_days", 7)))
window_s = window_days * 86_400
now_ts = inp.now.timestamp()
recent = [
f for f in inp.feedback_history
if self._age_s(f.get("created_at", ""), now_ts) <= _SEVEN_DAYS_S
if self._age_s(f.get("created_at", ""), now_ts) <= window_s
]
counts: Counter[str] = Counter(f.get("action") for f in recent)
@@ -50,13 +77,13 @@ class RecentPatternsAgent(BaseAgent):
dwell_ms = inp.profile.get("mean_dwell_ms_30d")
if total == 0:
prompt = "No tip reactions recorded in the last 7 days."
prompt = f"No tip reactions recorded in the last {window_days} days."
else:
done = counts.get("done", 0)
dismissed = counts.get("dismiss", 0)
snoozed = counts.get("snooze", 0)
parts = [
f"Last 7 days: {total} tip reaction{'s' if total != 1 else ''} — "
f"Last {window_days} days: {total} tip reaction{'s' if total != 1 else ''} — "
f"{done} completed, {dismissed} dismissed, {snoozed} snoozed."
]
if dwell_ms is not None:
@@ -74,6 +101,7 @@ class RecentPatternsAgent(BaseAgent):
prompt = " ".join(parts)
snapshot = {
"window_days": window_days,
"recent_total": total,
"action_counts": dict(counts),
"mean_dwell_ms_30d": dwell_ms,

2
ml/agents/tests/test_agents.py
View File

@@ -243,7 +243,7 @@ class TestFocusAreaAgent:
def test_snapshot_keys(self):
out = self.agent.compute(_inp(tasks=[_task("T1", project_id="A")]))
assert {"top_project", "top_task_count", "top_overdue_count", "project_count"} == set(out.signals_snapshot)
assert {"top_project", "top_task_count", "top_overdue_count", "project_count", "preferred_areas"} == set(out.signals_snapshot)
# ── Registry ─────────────────────────────────────────────────────────────────

213
ml/agents/tests/test_per_agent_inference.py Normal file
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@@ -0,0 +1,213 @@
"""Per-agent inference tests: momentum (#114), overdue-task (#115), recent-patterns (#116),
and focus-area (#113) preferred_areas wiring."""
from __future__ import annotations
import sys, os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", ".."))
from datetime import datetime, timezone
import pytest
from ml.agents.inference.history import FeedbackEvent, UserHistory
from ml.agents.inference.framework import run_inference
from ml.agents.momentum import MomentumAgent, MANIFEST as MOMENTUM_MANIFEST
from ml.agents.overdue_task import OverdueTaskAgent, MANIFEST as OVERDUE_MANIFEST
from ml.agents.recent_patterns import RecentPatternsAgent, MANIFEST as RECENT_MANIFEST
from ml.agents.focus_area import FocusAreaAgent
from ml.agents.base import AgentInput
_NOW = datetime(2026, 5, 8, 14, 0, 0, tzinfo=timezone.utc)
def _inp(**kwargs) -> AgentInput:
defaults = dict(user_id="u1", tasks=[], profile={}, now=_NOW, agent_prefs={})
defaults.update(kwargs)
return AgentInput(**defaults)
def _event(action: str, days_ago: float = 1.0) -> FeedbackEvent:
from datetime import timedelta
ts = (_NOW - timedelta(days=days_ago)).isoformat()
dwell = 60_000 if action == "done" else 500
return FeedbackEvent(action=action, dwell_ms=dwell, created_at=ts)
def _history(*events: FeedbackEvent) -> UserHistory:
return UserHistory(user_id="u1", events=list(events))
# ── momentum: engagement_trend ───────────────────────────────────────────────
class TestMomentumInference:
def test_cold_start_below_min_history(self):
history = _history(*[_event("done", days_ago=i) for i in range(5)])
result = run_inference(MOMENTUM_MANIFEST, history)
assert result["engagement_trend"] == "stable" # cold_start_default
def test_trend_up_when_recent_done_rate_higher(self):
# 8 done in last 7 days, 1 done in prior 7 days → trending up
recent = [_event("done", days_ago=i) for i in range(1, 9)]
older = [_event("dismiss", days_ago=i) for i in range(8, 15)]
older[0] = _event("done", days_ago=8) # one done in older window
history = _history(*recent, *older)
result = run_inference(MOMENTUM_MANIFEST, history)
assert result["engagement_trend"] == "up"
def test_trend_down_when_recent_done_rate_lower(self):
recent = [_event("dismiss", days_ago=i) for i in range(1, 8)]
older = [_event("done", days_ago=i) for i in range(8, 15)]
history = _history(*recent, *older)
result = run_inference(MOMENTUM_MANIFEST, history)
assert result["engagement_trend"] == "down"
def test_trend_stable_when_similar(self):
events = [_event("done" if i % 2 == 0 else "dismiss", days_ago=i) for i in range(1, 15)]
history = _history(*events)
result = run_inference(MOMENTUM_MANIFEST, history)
assert result["engagement_trend"] == "stable"
def test_agent_uses_trend_in_snippet(self):
out = MomentumAgent().compute(_inp(agent_prefs={"engagement_trend": "up"}))
assert "trending up" in out.prompt_text
def test_agent_uses_down_trend_in_snippet(self):
out = MomentumAgent().compute(_inp(agent_prefs={"engagement_trend": "down"}))
assert "trending down" in out.prompt_text
def test_snapshot_includes_trend(self):
out = MomentumAgent().compute(_inp(agent_prefs={"engagement_trend": "stable"}))
assert "engagement_trend" in out.signals_snapshot
def test_version_bumped(self):
assert MOMENTUM_MANIFEST.version == "1.1.0"
# ── overdue-task: lateness_tolerance_days ────────────────────────────────────
class TestOverdueTaskInference:
def test_cold_start_returns_zero(self):
history = _history(*[_event("done") for _ in range(5)])
result = run_inference(OVERDUE_MANIFEST, history)
assert result["lateness_tolerance_days"] == 0
def test_high_snooze_rate_returns_two(self):
events = [_event("snooze")] * 8 + [_event("done")] * 2
history = _history(*events)
result = run_inference(OVERDUE_MANIFEST, history)
assert result["lateness_tolerance_days"] == 2
def test_moderate_snooze_returns_one(self):
events = [_event("snooze")] * 3 + [_event("done")] * 7
history = _history(*events)
result = run_inference(OVERDUE_MANIFEST, history)
assert result["lateness_tolerance_days"] == 1
def test_low_snooze_returns_zero(self):
events = [_event("done")] * 9 + [_event("snooze")] * 1
history = _history(*events)
result = run_inference(OVERDUE_MANIFEST, history)
assert result["lateness_tolerance_days"] == 0
def test_tolerance_filters_tasks(self):
tasks = [
{"content": "Fresh overdue", "is_overdue": True, "task_age_days": 0.5},
{"content": "Old overdue", "is_overdue": True, "task_age_days": 3.0},
]
# tolerance=2 → only the 3-day task should count
out = OverdueTaskAgent().compute(_inp(tasks=tasks, agent_prefs={"lateness_tolerance_days": 2}))
assert "1 overdue task" in out.prompt_text
assert "Old overdue" in out.prompt_text
def test_snapshot_includes_tolerance(self):
tasks = [{"content": "T", "is_overdue": True, "task_age_days": 1.0}]
out = OverdueTaskAgent().compute(_inp(tasks=tasks, agent_prefs={"lateness_tolerance_days": 0}))
assert "lateness_tolerance_days" in out.signals_snapshot
def test_version_bumped(self):
assert OVERDUE_MANIFEST.version == "1.1.0"
# ── recent-patterns: window_days ─────────────────────────────────────────────
class TestRecentPatternsInference:
def test_cold_start_default_7(self):
history = _history(*[_event("done") for _ in range(3)]) # below min_history=5
result = run_inference(RECENT_MANIFEST, history)
assert result["window_days"] == 7 # cold_start_default
def test_sparse_history_widens_window(self):
history = _history(*[_event("done") for _ in range(5)]) # 5 events, n < 7 → 30 days
result = run_inference(RECENT_MANIFEST, history)
assert result["window_days"] == 30
def test_moderate_history_14_days(self):
history = _history(*[_event("done") for _ in range(10)]) # 7 ≤ n < 14 → 14 days
result = run_inference(RECENT_MANIFEST, history)
assert result["window_days"] == 14
def test_dense_history_stays_7(self):
history = _history(*[_event("done") for _ in range(20)]) # 20+ → 7 days
result = run_inference(RECENT_MANIFEST, history)
assert result["window_days"] == 7
def test_agent_uses_window_days_pref(self):
from datetime import timedelta
# 5 feedback events, all within 14 days but older than 7 days
feedback = [
{"action": "done", "dwell_ms": 60000,
"created_at": (_NOW - timedelta(days=10)).isoformat()}
] * 5
# With window_days=7 → 0 events seen; with window_days=14 → 5 events
out_narrow = RecentPatternsAgent().compute(
_inp(feedback_history=feedback, agent_prefs={"window_days": 7})
)
out_wide = RecentPatternsAgent().compute(
_inp(feedback_history=feedback, agent_prefs={"window_days": 14})
)
assert "No tip reactions" in out_narrow.prompt_text
assert "5 tip reactions" in out_wide.prompt_text
def test_snapshot_includes_window_days(self):
out = RecentPatternsAgent().compute(_inp(agent_prefs={"window_days": 14}))
assert out.signals_snapshot["window_days"] == 14
def test_version_bumped(self):
assert RECENT_MANIFEST.version == "1.1.0"
# ── focus-area: preferred_areas wiring ───────────────────────────────────────
class TestFocusAreaPreferredAreas:
agent = FocusAreaAgent()
def _task(self, content: str, project_id: str, is_overdue: bool = False) -> dict:
return {"id": "t1", "content": content, "is_overdue": is_overdue,
"task_age_days": 2.0, "priority": 1, "project_id": project_id}
def test_preferred_area_wins_tie(self):
tasks = [
self._task("Work thing", "work"),
self._task("Home thing", "home"),
]
out = self.agent.compute(_inp(tasks=tasks, agent_prefs={"preferred_areas": ["work"]}))
assert "work" in out.prompt_text
assert "matches the user's stated focus preferences" in out.prompt_text
def test_no_preferred_areas_uses_congestion_score(self):
tasks = [
self._task("W1", "work"),
self._task("H1", "home"),
self._task("H2", "home"),
]
out = self.agent.compute(_inp(tasks=tasks))
# home has more tasks → wins without any preference
assert "home" in out.prompt_text
def test_snapshot_includes_preferred_areas(self):
tasks = [self._task("T", "work")]
out = self.agent.compute(_inp(tasks=tasks, agent_prefs={"preferred_areas": ["work"]}))
assert out.signals_snapshot["preferred_areas"] == ["work"]
def test_version_bumped(self):
from ml.agents.focus_area import MANIFEST as FA_MANIFEST
assert FA_MANIFEST.version == "1.1.0"

4
ml/serving/tests/test_infer_endpoint.py
View File

@@ -36,10 +36,10 @@ async def test_infer_time_of_day_enough_history():
@pytest.mark.anyio
async def test_infer_agent_with_no_inferred_params():
"""Agents with no inferred_params return an empty dict."""
"""Agents with no inferred_params return an empty dict (focus-area has none)."""
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
resp = await client.post("/agents/overdue-task/infer", json={"user_id": "u1", "feedback_history": []})
resp = await client.post("/agents/focus-area/infer", json={"user_id": "u1", "feedback_history": []})
assert resp.status_code == 200
assert resp.json()["inferred_prefs"] == {}