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Author	SHA1	Message	Date
alvis	afb0e9b0cb	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>	2026-05-05 11:21:10 +00:00
alvis	ad6747c242	feat(profile): /api/profile + eligibility filter + inference framework (ADR-0014 steps 4-6) Step 4 — /api/profile read-through API: GET /api/profile → { user, prefs, consents, contexts } PATCH /api/profile/prefs/:scope upsert user_preferences (source='user') PATCH /api/profile/consents grant / revoke consent keys PATCH /api/profile/contexts create / activate / deactivate contexts Legacy consentGiven bit folded in as data:core fallback. Step 5 — registry-driven eligibility filter: fetchRegistry() exported from agent-registry.ts. profile/eligibility.ts: getEligibleAgentIds(userId) — filters by required consents, silenced_in_contexts, and user_preferences[enabled=false]. fetchOrchestratorTip filters agent_outputs to eligible set before calling ml/serving /recommend. Fail-closed: registry unavailable → empty set. Step 6 — shared context-inference framework (#111) + time-of-day proof (#112): ml/agents/inference/: UserHistory, FeedbackEvent, run_inference(). Framework: cold-start, min_history gating, error fallback, structured logs. TimeOfDayAgent v1.1.0: inferred_params=[preferred_hour]; also reads quiet_start/quiet_end from agent_prefs. agent_prefs injected by TS caller. AgentInput gains agent_prefs field. ml/serving: POST /agents/{agent_id}/infer endpoint. agent-outputs.ts computeAndStore: loads prefs before compute, calls /infer after, persists results (source='inferred'); user overrides never touched. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-05 11:14:25 +00:00
alvis	b3cf588f2f	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>	2026-05-04 10:20:05 +00:00

Author

SHA1

Message

Date

alvis

afb0e9b0cb

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>

2026-05-05 11:21:10 +00:00

alvis

ad6747c242

feat(profile): /api/profile + eligibility filter + inference framework (ADR-0014 steps 4-6)

Step 4 — /api/profile read-through API:
  GET  /api/profile          → { user, prefs, consents, contexts }
  PATCH /api/profile/prefs/:scope  upsert user_preferences (source='user')
  PATCH /api/profile/consents      grant / revoke consent keys
  PATCH /api/profile/contexts      create / activate / deactivate contexts
  Legacy consentGiven bit folded in as data:core fallback.

Step 5 — registry-driven eligibility filter:
  fetchRegistry() exported from agent-registry.ts.
  profile/eligibility.ts: getEligibleAgentIds(userId) — filters by required
  consents, silenced_in_contexts, and user_preferences[enabled=false].
  fetchOrchestratorTip filters agent_outputs to eligible set before calling
  ml/serving /recommend. Fail-closed: registry unavailable → empty set.

Step 6 — shared context-inference framework (#111) + time-of-day proof (#112):
  ml/agents/inference/: UserHistory, FeedbackEvent, run_inference().
  Framework: cold-start, min_history gating, error fallback, structured logs.
  TimeOfDayAgent v1.1.0: inferred_params=[preferred_hour]; also reads
  quiet_start/quiet_end from agent_prefs. agent_prefs injected by TS caller.
  AgentInput gains agent_prefs field.
  ml/serving: POST /agents/{agent_id}/infer endpoint.
  agent-outputs.ts computeAndStore: loads prefs before compute, calls /infer
  after, persists results (source='inferred'); user overrides never touched.

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

2026-05-05 11:14:25 +00:00

alvis

b3cf588f2f

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>

2026-05-04 10:20:05 +00:00

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