Replaces snooze-rate heuristic with p50 of actual task lateness (completedAt − dueAt).
Adds project_realness inference: projects with chronic lateness get realness < 1 and
the agent softens its snippet language from "overdue" to "past target date".
- TaskCompletion added to UserHistory with lateness_days computed property
- _infer_lateness_tolerance: p50 of task_completions, clipped at 0, float
- _infer_project_realness: per-project median lateness normalised by global median
- Both InferredParams use 7d TTL; cold_start = 0.0 / {}
- AgentInferRequest accepts task_completions; endpoint wires them through
- 12 new tests covering punctual/chronic/mixed users and language softening
- Agent bumped to v1.2.0
Closes#115
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
Each agent now exports a module-level MANIFEST declaring id, version,
pref_schema, required_consents, ttl_sec, and silenced_in_contexts. The
registry surfaces both the agent and its manifest, and rejects on
mismatch so the two cannot drift.
ml/serving exposes GET /agents/registry; services/api proxies it as
GET /api/agents/registry with a 60s in-process cache so admin pageviews
don't hammer upstream. Failures aren't cached.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
