feat(ml): multi-agent context framework + v4 orchestrator prompt

Adds ml/agents/ — five specialised sub-agents (overdue_task, momentum,
time_of_day, recent_patterns, focus_area) each producing a prompt snippet
from user signals. A registry wires them up; the orchestrator prompt in
ml/serving/prompts.py synthesises their outputs into one tip via LiteLLM.

Also wires /api/agents route in the API and updates the Dockerfile to copy
the full ml/ tree with PYTHONPATH=/app so agent imports resolve correctly.

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

infra/docker/Dockerfile.ml

@@ -1,6 +1,8 @@
 FROM python:3.12-slim
-WORKDIR /app
+WORKDIR /app/ml/serving
 COPY ml/serving/requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
-COPY ml/serving/*.py .
+COPY ml/ /app/ml/
+# PYTHONPATH=/app lets 'import ml.agents.*' resolve from /app/ml/agents/
+ENV PYTHONPATH=/app
 CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

ml/agents/__init__.py

@@ -0,0 +1,4 @@
+from .base import BaseAgent, AgentInput, AgentOutput
+from .registry import get_agent, all_agents
+
+__all__ = ["BaseAgent", "AgentInput", "AgentOutput", "get_agent", "all_agents"]

ml/agents/base.py

@@ -0,0 +1,53 @@
+"""Base class and shared data structures for all recommendation sub-agents."""
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta, timezone
+from typing import ClassVar
+
+
+@dataclass
+class AgentInput:
+    """Everything an agent may need to produce its prompt snippet."""
+    user_id: str
+    tasks: list[dict]                          # task signal dicts (content, priority, is_overdue, …)
+    profile: dict[str, float | None]           # profile feature values keyed by feature name
+    feedback_history: list[dict] = field(default_factory=list)  # [{action, dwell_ms, created_at}, …]
+    now: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+@dataclass
+class AgentOutput:
+    """Result produced by an agent; persisted to agent_outputs table."""
+    user_id: str
+    agent_id: str
+    prompt_text: str                           # snippet passed to the orchestrator
+    signals_snapshot: dict                     # inputs consumed (for explainability / debugging)
+    computed_at: str                           # ISO 8601
+    expires_at: str                            # ISO 8601
+    agent_version: str
+
+
+class BaseAgent(ABC):
+    agent_id: ClassVar[str]
+    ttl_seconds: ClassVar[int]
+    version: ClassVar[str]
+
+    @abstractmethod
+    def compute(self, inp: AgentInput) -> AgentOutput:
+        """Analyse inp and return a prompt snippet describing what was found."""
+        ...
+
+    def _make_output(self, inp: AgentInput, prompt_text: str, snapshot: dict) -> AgentOutput:
+        computed_at = inp.now.astimezone(timezone.utc).isoformat()
+        expires_at = (inp.now.astimezone(timezone.utc) + timedelta(seconds=self.ttl_seconds)).isoformat()
+        return AgentOutput(
+            user_id=inp.user_id,
+            agent_id=self.agent_id,
+            prompt_text=prompt_text,
+            signals_snapshot=snapshot,
+            computed_at=computed_at,
+            expires_at=expires_at,
+            agent_version=self.version,
+        )

ml/agents/focus_area.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+from collections import defaultdict
+from typing import ClassVar
+from .base import BaseAgent, AgentInput, AgentOutput
+
+
+class FocusAreaAgent(BaseAgent):
+    """Identifies the most congested project/area in the user's task list."""
+    agent_id: ClassVar[str] = "focus-area"
+    ttl_seconds: ClassVar[int] = 43_200  # 12h
+    version: ClassVar[str] = "1.0.0"
+
+    def compute(self, inp: AgentInput) -> AgentOutput:
+        by_project: dict[str, list[dict]] = defaultdict(list)
+        for task in inp.tasks:
+            project = task.get("project_id") or task.get("project") or "default"
+            by_project[project].append(task)
+
+        if not by_project:
+            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)
+
+        top_project, top_tasks = max(by_project.items(), key=lambda kv: score(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)
+
+        parts = [
+            f"The user's most congested area is {label} "
+            f"({n} task{'s' if n != 1 else ''}, {overdue_in_top} overdue)."
+        ]
+        if overdue_in_top >= 3:
+            parts.append("Consider surfacing an action from this area.")
+
+        prompt = " ".join(parts)
+        snapshot = {
+            "top_project": top_project,
+            "top_task_count": n,
+            "top_overdue_count": overdue_in_top,
+            "project_count": len(by_project),
+        }
+        return self._make_output(inp, prompt, snapshot)

ml/agents/momentum.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+from typing import ClassVar
+from .base import BaseAgent, AgentInput, AgentOutput
+
+
+class MomentumAgent(BaseAgent):
+    """Characterises the user's recent engagement trend from profile features."""
+    agent_id: ClassVar[str] = "momentum"
+    ttl_seconds: ClassVar[int] = 21600  # 6h
+    version: ClassVar[str] = "1.0.0"
+
+    def compute(self, inp: AgentInput) -> AgentOutput:
+        completion = inp.profile.get("completion_rate_30d")
+        dismiss = inp.profile.get("dismiss_rate_30d")
+        volume = inp.profile.get("tip_volume_30d")
+
+        parts: list[str] = []
+
+        if completion is not None:
+            pct = round(completion * 100)
+            if pct >= 50:
+                parts.append(f"The user completes {pct}% of tips (strong engagement).")
+            elif pct >= 25:
+                parts.append(f"The user completes {pct}% of tips (moderate engagement).")
+            else:
+                parts.append(
+                    f"The user completes {pct}% of tips "
+                    f"(low engagement — prefer simple, immediately actionable tips)."
+                )
+        else:
+            parts.append("No completion-rate data yet (new user).")
+
+        if dismiss is not None:
+            dpct = round(dismiss * 100)
+            if dpct >= 40:
+                parts.append(f"Dismiss rate is high ({dpct}%) — avoid repetitive or irrelevant tips.")
+            elif dpct <= 10:
+                parts.append(f"Dismiss rate is low ({dpct}%).")
+
+        if volume is not None and int(volume) < 5:
+            parts.append("Very few tips served so far — this is an early-stage user.")
+
+        prompt = " ".join(parts) if parts else "No engagement data available yet."
+        snapshot = {
+            "completion_rate_30d": completion,
+            "dismiss_rate_30d": dismiss,
+            "tip_volume_30d": volume,
+        }
+        return self._make_output(inp, prompt, snapshot)

ml/agents/overdue_task.py

@@ -0,0 +1,42 @@
+from __future__ import annotations
+from typing import ClassVar
+from .base import BaseAgent, AgentInput, AgentOutput
+
+
+class OverdueTaskAgent(BaseAgent):
+    """Reports the user's overdue tasks by count and age."""
+    agent_id: ClassVar[str] = "overdue-task"
+    ttl_seconds: ClassVar[int] = 3600  # 1h — overdue status changes infrequently
+    version: ClassVar[str] = "1.0.0"
+
+    def compute(self, inp: AgentInput) -> AgentOutput:
+        overdue = [t for t in inp.tasks if t.get("is_overdue")]
+        top = sorted(overdue, key=lambda t: -t.get("task_age_days", 0))[:3]
+
+        if not overdue:
+            prompt = "The user has no overdue tasks at this time."
+        elif len(overdue) == 1:
+            t = top[0]
+            age = round(t.get("task_age_days", 0))
+            prompt = (
+                f'The user has 1 overdue task: "{t["content"]}" '
+                f"({age} day{'s' if age != 1 else ''} overdue)."
+            )
+        else:
+            items = ", ".join(
+                f'"{t["content"]}" ({round(t.get("task_age_days", 0))}d)'
+                for t in top
+            )
+            prompt = (
+                f"The user has {len(overdue)} overdue tasks. "
+                f"Top {len(top)}: {items}."
+            )
+
+        snapshot = {
+            "overdue_count": len(overdue),
+            "top_overdue": [
+                {"content": t["content"], "task_age_days": t.get("task_age_days", 0)}
+                for t in top
+            ],
+        }
+        return self._make_output(inp, prompt, snapshot)

ml/agents/recent_patterns.py

@@ -0,0 +1,68 @@
+from __future__ import annotations
+from collections import Counter
+from datetime import datetime, timezone
+from typing import ClassVar
+from .base import BaseAgent, AgentInput, AgentOutput
+
+_SEVEN_DAYS_S = 7 * 86_400
+
+
+class RecentPatternsAgent(BaseAgent):
+    """Surfaces the user's reaction pattern from the last 7 days of feedback."""
+    agent_id: ClassVar[str] = "recent-patterns"
+    ttl_seconds: ClassVar[int] = 86_400  # 24h
+    version: ClassVar[str] = "1.0.0"
+
+    def compute(self, inp: AgentInput) -> AgentOutput:
+        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
+        ]
+
+        counts: Counter[str] = Counter(f.get("action") for f in recent)
+        total = len(recent)
+        dwell_ms = inp.profile.get("mean_dwell_ms_30d")
+
+        if total == 0:
+            prompt = "No tip reactions recorded in the last 7 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"{done} completed, {dismissed} dismissed, {snoozed} snoozed."
+            ]
+            if dwell_ms is not None:
+                dwell_s = round(dwell_ms / 1000)
+                if dwell_s < 15:
+                    parts.append(
+                        "Average dwell is very short — user may be acting on auto-pilot; vary tip content."
+                    )
+                elif dwell_s < 60:
+                    parts.append(f"Average dwell {dwell_s}s — tips are being read.")
+                else:
+                    parts.append(
+                        f"Average dwell {dwell_s}s — user deliberates; prefer tips that reward reflection."
+                    )
+            prompt = " ".join(parts)
+
+        snapshot = {
+            "recent_total": total,
+            "action_counts": dict(counts),
+            "mean_dwell_ms_30d": dwell_ms,
+        }
+        return self._make_output(inp, prompt, snapshot)
+
+    @staticmethod
+    def _age_s(iso: str, now_ts: float) -> float:
+        if not iso:
+            return float("inf")
+        try:
+            dt = datetime.fromisoformat(iso.replace("Z", "+00:00"))
+            if dt.tzinfo is None:
+                dt = dt.replace(tzinfo=timezone.utc)
+            return now_ts - dt.timestamp()
+        except Exception:
+            return float("inf")

ml/agents/registry.py

@@ -0,0 +1,28 @@
+from __future__ import annotations
+from .base import BaseAgent
+from .overdue_task import OverdueTaskAgent
+from .momentum import MomentumAgent
+from .time_of_day import TimeOfDayAgent
+from .recent_patterns import RecentPatternsAgent
+from .focus_area import FocusAreaAgent
+
+_AGENTS: dict[str, BaseAgent] = {
+    a.agent_id: a
+    for a in [
+        OverdueTaskAgent(),
+        MomentumAgent(),
+        TimeOfDayAgent(),
+        RecentPatternsAgent(),
+        FocusAreaAgent(),
+    ]
+}
+
+
+def get_agent(agent_id: str) -> BaseAgent:
+    if agent_id not in _AGENTS:
+        raise KeyError(f"Unknown agent: {agent_id!r}. Known: {sorted(_AGENTS)}")
+    return _AGENTS[agent_id]
+
+
+def all_agents() -> list[BaseAgent]:
+    return list(_AGENTS.values())

ml/agents/tests/test_agents.py

@@ -0,0 +1,275 @@
+"""Unit tests for all sub-agents and the registry."""
+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.base import AgentInput, AgentOutput
+from ml.agents.overdue_task import OverdueTaskAgent
+from ml.agents.momentum import MomentumAgent
+from ml.agents.time_of_day import TimeOfDayAgent
+from ml.agents.recent_patterns import RecentPatternsAgent
+from ml.agents.focus_area import FocusAreaAgent
+from ml.agents.registry import get_agent, all_agents
+
+_NOW = datetime(2026, 5, 1, 9, 0, 0, tzinfo=timezone.utc)  # Thursday 09:00 UTC
+
+
+def _inp(**kwargs) -> AgentInput:
+    defaults = dict(
+        user_id="u1",
+        tasks=[],
+        profile={},
+        feedback_history=[],
+        now=_NOW,
+    )
+    defaults.update(kwargs)
+    return AgentInput(**defaults)
+
+
+def _task(content="Do thing", is_overdue=False, task_age_days=0.0, priority=1, project_id=None):
+    t = {"id": "t1", "content": content, "is_overdue": is_overdue,
+         "task_age_days": task_age_days, "priority": priority}
+    if project_id:
+        t["project_id"] = project_id
+    return t
+
+
+# ── helpers ──────────────────────────────────────────────────────────────────
+
+def _check_output(out: AgentOutput, agent) -> None:
+    assert isinstance(out, AgentOutput)
+    assert out.user_id == "u1"
+    assert out.agent_id == agent.agent_id
+    assert out.prompt_text
+    assert out.computed_at
+    assert out.expires_at > out.computed_at
+    assert out.agent_version == agent.version
+
+
+# ── OverdueTaskAgent ──────────────────────────────────────────────────────────
+
+class TestOverdueTaskAgent:
+    agent = OverdueTaskAgent()
+
+    def test_no_overdue(self):
+        out = self.agent.compute(_inp(tasks=[_task("Read book")]))
+        _check_output(out, self.agent)
+        assert "no overdue" in out.prompt_text.lower()
+        assert out.signals_snapshot["overdue_count"] == 0
+
+    def test_single_overdue(self):
+        out = self.agent.compute(_inp(tasks=[_task("Call dentist", is_overdue=True, task_age_days=3)]))
+        _check_output(out, self.agent)
+        assert "1 overdue" in out.prompt_text
+        assert "Call dentist" in out.prompt_text
+        assert "3 day" in out.prompt_text
+
+    def test_multiple_overdue_top3(self):
+        tasks = [
+            _task(f"Task {i}", is_overdue=True, task_age_days=float(i))
+            for i in range(1, 6)
+        ]
+        out = self.agent.compute(_inp(tasks=tasks))
+        _check_output(out, self.agent)
+        assert "5 overdue" in out.prompt_text
+        assert out.signals_snapshot["overdue_count"] == 5
+        assert len(out.signals_snapshot["top_overdue"]) == 3
+        # Top 3 should be highest age: 5, 4, 3
+        ages = [t["task_age_days"] for t in out.signals_snapshot["top_overdue"]]
+        assert ages == sorted(ages, reverse=True)
+
+    def test_ttl_respected(self):
+        out = self.agent.compute(_inp())
+        assert out.expires_at > out.computed_at
+
+
+# ── MomentumAgent ─────────────────────────────────────────────────────────────
+
+class TestMomentumAgent:
+    agent = MomentumAgent()
+
+    def test_no_profile(self):
+        out = self.agent.compute(_inp(profile={}))
+        _check_output(out, self.agent)
+        assert "new user" in out.prompt_text.lower() or "no " in out.prompt_text.lower()
+
+    def test_strong_engagement(self):
+        out = self.agent.compute(_inp(profile={"completion_rate_30d": 0.65, "dismiss_rate_30d": 0.05}))
+        assert "strong engagement" in out.prompt_text
+
+    def test_low_completion_warns(self):
+        out = self.agent.compute(_inp(profile={"completion_rate_30d": 0.1}))
+        assert "low engagement" in out.prompt_text
+        assert "actionable" in out.prompt_text
+
+    def test_high_dismiss_warns(self):
+        out = self.agent.compute(_inp(profile={"completion_rate_30d": 0.3, "dismiss_rate_30d": 0.5}))
+        assert "dismiss rate is high" in out.prompt_text.lower()
+
+    def test_early_stage_user(self):
+        out = self.agent.compute(_inp(profile={"tip_volume_30d": 2.0}))
+        assert "early-stage" in out.prompt_text
+
+
+# ── TimeOfDayAgent ────────────────────────────────────────────────────────────
+
+class TestTimeOfDayAgent:
+    agent = TimeOfDayAgent()
+
+    def test_morning_label(self):
+        inp = _inp(now=datetime(2026, 5, 1, 8, 0, tzinfo=timezone.utc))  # Friday
+        out = self.agent.compute(inp)
+        assert "morning" in out.prompt_text
+        assert "08:00" in out.prompt_text
+
+    def test_weekend_note(self):
+        inp = _inp(now=datetime(2026, 5, 2, 10, 0, tzinfo=timezone.utc))  # Saturday
+        out = self.agent.compute(inp)
+        assert "weekend" in out.prompt_text.lower()
+
+    def test_peak_hour_exact(self):
+        inp = _inp(
+            now=datetime(2026, 5, 1, 10, 0, tzinfo=timezone.utc),
+            profile={"preferred_hour": 10.0},
+        )
+        out = self.agent.compute(inp)
+        assert "peak productivity hour" in out.prompt_text
+
+    def test_approaching_peak(self):
+        inp = _inp(
+            now=datetime(2026, 5, 1, 9, 0, tzinfo=timezone.utc),
+            profile={"preferred_hour": 10.0},
+        )
+        out = self.agent.compute(inp)
+        assert "approaching" in out.prompt_text.lower()
+
+    def test_no_preferred_hour(self):
+        out = self.agent.compute(_inp())
+        assert "no preferred-hour" in out.prompt_text.lower()
+
+    def test_snapshot_keys(self):
+        out = self.agent.compute(_inp())
+        assert {"hour", "day_of_week", "preferred_hour"} == set(out.signals_snapshot)
+
+
+# ── RecentPatternsAgent ───────────────────────────────────────────────────────
+
+class TestRecentPatternsAgent:
+    agent = RecentPatternsAgent()
+
+    def test_no_feedback(self):
+        out = self.agent.compute(_inp())
+        assert "no tip reactions" in out.prompt_text.lower()
+
+    def test_recent_feedback_summary(self):
+        now_iso = _NOW.isoformat()
+        feedback = [
+            {"action": "done", "dwell_ms": 30000, "created_at": now_iso},
+            {"action": "done", "dwell_ms": 45000, "created_at": now_iso},
+            {"action": "dismiss", "dwell_ms": 2000, "created_at": now_iso},
+        ]
+        out = self.agent.compute(_inp(feedback_history=feedback))
+        assert "3 tip reactions" in out.prompt_text
+        assert "2 completed" in out.prompt_text
+        assert "1 dismissed" in out.prompt_text
+
+    def test_old_feedback_excluded(self):
+        # 10 days ago — should be excluded from 7-day window
+        old_iso = "2026-04-21T09:00:00+00:00"
+        feedback = [{"action": "done", "dwell_ms": 5000, "created_at": old_iso}]
+        out = self.agent.compute(_inp(feedback_history=feedback))
+        assert "no tip reactions" in out.prompt_text.lower()
+
+    def test_short_dwell_note(self):
+        now_iso = _NOW.isoformat()
+        feedback = [{"action": "done", "dwell_ms": 5000, "created_at": now_iso}]
+        out = self.agent.compute(_inp(
+            feedback_history=feedback,
+            profile={"mean_dwell_ms_30d": 5000.0},
+        ))
+        assert "auto-pilot" in out.prompt_text.lower() or "short" in out.prompt_text.lower()
+
+    def test_long_dwell_note(self):
+        now_iso = _NOW.isoformat()
+        feedback = [{"action": "done", "dwell_ms": 90000, "created_at": now_iso}]
+        out = self.agent.compute(_inp(
+            feedback_history=feedback,
+            profile={"mean_dwell_ms_30d": 90000.0},
+        ))
+        assert "deliberate" in out.prompt_text.lower() or "reflection" in out.prompt_text.lower()
+
+
+# ── FocusAreaAgent ────────────────────────────────────────────────────────────
+
+class TestFocusAreaAgent:
+    agent = FocusAreaAgent()
+
+    def test_no_tasks(self):
+        out = self.agent.compute(_inp())
+        assert "no tasks" in out.prompt_text.lower()
+
+    def test_single_project(self):
+        tasks = [_task(f"T{i}", project_id="Work") for i in range(3)]
+        out = self.agent.compute(_inp(tasks=tasks))
+        assert '"Work"' in out.prompt_text
+        assert "3 tasks" in out.prompt_text
+
+    def test_most_congested_wins(self):
+        tasks = (
+            [_task(f"W{i}", project_id="Work") for i in range(5)]
+            + [_task(f"H{i}", project_id="Home") for i in range(2)]
+        )
+        out = self.agent.compute(_inp(tasks=tasks))
+        assert '"Work"' in out.prompt_text
+
+    def test_overdue_weighting(self):
+        # Home has 2 tasks (1 overdue), Work has 3 non-overdue tasks
+        # Home score = 2+1 = 3; Work score = 3 — Home should win due to overdue weight
+        tasks = (
+            [_task("Home1", project_id="Home", is_overdue=True),
+             _task("Home2", project_id="Home")]
+            + [_task(f"W{i}", project_id="Work") for i in range(3)]
+        )
+        out = self.agent.compute(_inp(tasks=tasks))
+        assert '"Work"' not in out.prompt_text or '"Home"' in out.prompt_text
+
+    def test_default_project_fallback(self):
+        out = self.agent.compute(_inp(tasks=[_task("No project task")]))
+        assert "default project" in out.prompt_text
+
+    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)
+
+
+# ── Registry ─────────────────────────────────────────────────────────────────
+
+class TestRegistry:
+    def test_all_agents_present(self):
+        agents = all_agents()
+        ids = {a.agent_id for a in agents}
+        assert ids == {"overdue-task", "momentum", "time-of-day", "recent-patterns", "focus-area"}
+
+    def test_get_agent(self):
+        a = get_agent("momentum")
+        assert a.agent_id == "momentum"
+
+    def test_get_unknown_raises(self):
+        with pytest.raises(KeyError, match="Unknown agent"):
+            get_agent("nonexistent")
+
+    def test_all_agents_compute(self):
+        inp = _inp(
+            tasks=[_task("Buy milk", is_overdue=True, task_age_days=2, project_id="Personal")],
+            profile={"completion_rate_30d": 0.4, "tip_volume_30d": 10.0, "preferred_hour": 9.0},
+            feedback_history=[
+                {"action": "done", "dwell_ms": 25000, "created_at": _NOW.isoformat()}
+            ],
+        )
+        for agent in all_agents():
+            out = agent.compute(inp)
+            _check_output(out, agent)

ml/agents/time_of_day.py

@@ -0,0 +1,50 @@
+from __future__ import annotations
+from typing import ClassVar
+from .base import BaseAgent, AgentInput, AgentOutput
+
+_DOW_NAMES = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
+
+
+class TimeOfDayAgent(BaseAgent):
+    """Frames the current moment relative to the user's productive peak."""
+    agent_id: ClassVar[str] = "time-of-day"
+    ttl_seconds: ClassVar[int] = 900  # 15m — must stay current-hour accurate
+    version: ClassVar[str] = "1.0.0"
+
+    def compute(self, inp: AgentInput) -> AgentOutput:
+        hour = inp.now.hour
+        dow = inp.now.weekday()  # 0=Monday … 6=Sunday
+        preferred = inp.profile.get("preferred_hour")
+        is_weekend = dow >= 5
+
+        parts = [f"It is {hour:02d}:00 on {_DOW_NAMES[dow]} ({self._label(hour)})."]
+
+        if is_weekend:
+            parts.append("Weekend context — prefer personal or reflective tips over work tasks.")
+
+        if preferred is not None:
+            ph = int(preferred)
+            delta = min(abs(hour - ph), 24 - abs(hour - ph))  # circular distance
+            if delta == 0:
+                parts.append(
+                    f"This is the user's peak productivity hour ({ph:02d}:00) — "
+                    f"a high-impact tip is appropriate."
+                )
+            elif delta <= 2:
+                parts.append(f"Approaching the user's peak productivity window ({ph:02d}:00).")
+        else:
+            parts.append("No preferred-hour data yet.")
+
+        prompt = " ".join(parts)
+        snapshot = {"hour": hour, "day_of_week": dow, "preferred_hour": preferred}
+        return self._make_output(inp, prompt, snapshot)
+
+    @staticmethod
+    def _label(hour: int) -> str:
+        if 5 <= hour < 12:
+            return "morning"
+        if 12 <= hour < 17:
+            return "afternoon"
+        if 17 <= hour < 21:
+            return "evening"
+        return "night"

ml/serving/prompts.py

@@ -108,6 +108,52 @@ PROMPTS: dict[str, Prompt] = {
 }
 
 
+# ── v4-orchestrator ────────────────────────────────────────────────────────
+# Not a Prompt entry — takes pre-computed agent snippets, not a _Ctx.
+
+_SYS_V4_ORCHESTRATOR = (
+    "You are a personal advisor generating a single, perfectly-timed tip. "
+    "Multiple specialized agents have analyzed the user's current context and provided "
+    "their insights below. Synthesize their combined perspective to generate exactly ONE "
+    "tip that is specific, actionable, and relevant right now. "
+    "Respond ONLY with a JSON object with keys: "
+    '"id" (short slug), "content" (the tip, ≤2 sentences), '
+    '"rationale" (why now, ≤1 sentence). '
+    "No markdown, no prose outside the JSON object."
+)
+
+
+def build_orchestrator_messages(
+    agent_outputs: list[dict],
+    tasks: list[dict],
+    hour_of_day: int,
+    day_of_week: int,
+) -> list[dict]:
+    """Build the [system, user] message list for the orchestrator LLM call.
+
+    agent_outputs: list of {agent_id, prompt_text} dicts.
+    Falls back to raw task summary when agent_outputs is empty.
+    """
+    lines = [f"Current time: {hour_of_day:02d}:00, day_of_week={day_of_week}", ""]
+    if agent_outputs:
+        lines.append("Context from analysis agents:")
+        for s in agent_outputs:
+            lines.append(f"[{s['agent_id']}] {s['prompt_text']}")
+    else:
+        overdue = [t for t in tasks if t.get("is_overdue")]
+        lines.append(
+            f"No pre-computed agent context available. "
+            f"Tasks: {len(tasks)} total, {len(overdue)} overdue."
+        )
+        for t in tasks[:3]:
+            lines.append(f"  - {t.get('content', '?')}")
+    lines.append("\nGenerate one tip as a JSON object.")
+    return [
+        {"role": "system", "content": _SYS_V4_ORCHESTRATOR},
+        {"role": "user", "content": "\n".join(lines)},
+    ]
+
+
 def default_version() -> str:
     return os.getenv("DEFAULT_PROMPT_VERSION", "v1")
 

services/api/src/index.ts

@@ -17,6 +17,7 @@ import { userRouter } from './routes/user.js';
 import { pushRouter } from './routes/push.js';
 import { adminRouter, adminInternalRouter } from './routes/admin.js';
 import benchRouter from './routes/bench.js';
+import agentOutputsRouter from './routes/agent-outputs.js';
 import { mkdir } from 'fs/promises';
 import { dirname } from 'path';
 import { requireAuth } from './middleware/session.js';
@@ -68,6 +69,7 @@ app.use('/api/push', pushRouter);
 app.use('/api/admin', adminRouter);
 app.use('/api/admin', adminInternalRouter);
 app.use('/api/bench', requireAuth as any, requireAdmin as any, benchRouter);
+app.use('/api/agents', agentOutputsRouter);
 
 app.use('/api/ml', requireAuth as any, requireAdmin as any, async (req: Request, res: Response) => {
   const mlUrl = config.ML_SERVING_URL;