docs: ADR-0014 — unified Profile model + agent registry

Propose a shared substrate for per-user prefs, contexts, per-key
consents, and per-agent state so adding an agent stays a manifest
change. Updates CLAUDE.md, README, and architecture docs to reflect
the multi-agent pipeline (ADR-0013) and the registry direction.

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

docs/architecture/overview.md

@@ -48,6 +48,8 @@ User reactions (done / snooze / dismiss) are events too. They close the loop as
 - **Feast** for feature store when we get there; homegrown adapter until then (Phase 1 seam).
 - **MLflow** for model registry and experiment tracking; deployed at `o.alogins.net/mlflow`.
 - **Auth.js** embedded behind an OIDC-shaped boundary (ADR-0004). Swap to a standalone OIDC provider when mobile ships.
+- **Multi-agent recommendation** (ADR-0013) — pre-compute agents emit prompt snippets, an orchestrator LLM produces the tip. Replaced the ε-greedy bandit (ADR-0007/0012) for explainability, cold-start, and decoupling generation from selection.
+- **Registry-driven agents + unified Profile** (ADR-0014) — agents are plugins with declared manifests; per-user prefs, contexts, and per-key consents live in shared tables; auto-inferred parameters share a common framework. Adding an agent is a manifest change.
 - **k3s** as the first step beyond docker-compose — no "compose → full k8s" cliff.
 
 ## AI stack
@@ -59,30 +61,43 @@ All LLM inference routes through **LiteLLM** (`llm.alogins.net`) backed by **Oll
 
 **OpenWebUI** (`ai.alogins.net`) is the human-facing interface for prompt iteration and model testing during development.
 
-## Decision flow for a new tip (Phase 2 target)
+## Decision flow for a new tip (M2, ADR-0013 + ADR-0014)
 
 ```
+                  ┌────────────────────────────────────────────────┐
+                  │ Pre-compute (every 15 min, per registered agent) │
+                  │  ml/agents/<id> → prompt snippet → agent_outputs │
+                  │  TTL per manifest; agent_version invalidates     │
+                  └────────────────────────────────────────────────┘
+
 client ─► gateway ─► recommender (TS)
+                          │
+                          ├─► profile:    GET /api/profile
+                          │               (user, prefs, active context, consents)
+                          │
+                          ├─► registry:   GET /api/agents/registry
+                          │               (manifests; eligibility filter inputs)
+                          │
+                          ├─► outputs:    pull freshest non-expired agent_outputs
+                          │               for eligible agents (consents granted,
+                          │               not silenced by active context, enabled)
                           │
                           ▼
                      ml/serving (Python)
                           │
-                          ├─► context:    ml/features/context.py
-                          │               (tasks + reactions + time patterns → prompt)
+                          ├─► assemble:   v4-orchestrator prompt
+                          │               = global prefs + active context + snippets
                           │
-                          ├─► generate:   LiteLLM → Ollama
-                          │               → N TipCandidates {content, kind, model, prompt_version}
+                          ├─► generate:   LiteLLM → Ollama → one tip
                           │
-                          ├─► score:      bandit policy scores each candidate
-                          │
-                          ├─► shadows:    shadow policies log picks without serving
-                          │
-                          └─► persist:    tip_scores {candidate, policy, features, latency}
-                          ◄─  best TipCandidate
+                          └─► persist:    tip_scores {tip, contributing agents,
+                                          prompt_version, llm_model, latency}
+                          ◄─  tip
 ```
 
-**Phase 1 (shipped M1):** candidates come from Todoist task list, no LLM. The bandit scores tasks directly.
+**Evolution:**
+- **Phase 1 (M1):** candidates from Todoist; ε-greedy bandit scored tasks directly (ADR-0007, ADR-0012). Superseded.
+- **Phase 2 early (M2):** LLM-generated candidates ranked by bandit. Superseded mid-milestone.
+- **Phase 2 current (M2):** multi-agent pipeline (ADR-0013), registry-driven and registry-extensible (ADR-0014). No bandit; the orchestrator LLM reasons over named agent snippets.
 
-**Phase 2 (shipped M2):** LLM candidates are generated in parallel with Todoist fetch. Both pools are merged, scored by the bandit, and the winner served. `tip_scores` tracks `prompt_version`, `llm_model`, and `tip_kind` for every row.
-
-Feedback: `POST /feedback → events.emit(reaction)` → online bandit update + `prompt_version` tracked for A/B analysis.
+Feedback: `POST /feedback → events.emit(reaction)`. No online ML reward loop (ADR-0013 §Consequences); reactions are logged in `tip_feedback` for observability and potential future supervised learning.