docs: A2A-3 — task schema, field-by-field KB-literal mapping

Adds §6a documenting how each Task field (id, intent, required tier,
target queue, priority, status, context refs, result ref, submitter,
deadline) is actually represented in Kanboard today (columns, tags,
score, priority, comments, native fields), per the already-agreed
KB-literal decision (§10.2). Ground truth taken from kb-claim and
kb_worker.py, not written in the abstract.

Kanboard #135

DESIGN-a2a-agents.md

@@ -1,170 +1,483 @@
-# DESIGN — Agap Agent Platform (A2A, model queues, agents)
+# DESIGN — Agap Agent Platform v2.1: the agent algebra
 
-Status: **draft for review** · Owner: alvis · Drafted 2026-07-21
+Status: **v2.1, agreed with alvis 2026-07-21** (v2 `7be30c71` + hardening review)
+Owner: alvis · Written with Claude
 
-This is the overall design for turning the Agap homelab from "one Adolf carrying every
+One design, two axioms, one verb. Everything alvis asked for — per-model queues,
-tool + a few background LLM calls" into a **multi-agent platform**: agents as
+quota parking, Hindsight reflect as an async task, the Claude Code loop as a task
-personas, models as queued compute, and A2A as the way work moves between them.
+puller, semantic/tier/direct routing — falls out as a special case rather than a
+rule. This document is the reference; the Kanboard A2A tasks implement it.
 
 ---
 
+## 0. Glossary
+
+- **Task plane / "the fabric"** — the task-passing substrate connecting all
+  agents: **Kanboard** (the durable task store and queue — for humans *and*
+  agents) + **A2A protocol semantics** (submit/status/result, Agent Cards,
+  context-by-reference) + the **conventions** on top (claim/lease, priorities,
+  parking, trust-class routing). Not a deployable component; the collective name,
+  the way "the network" names cables + IP + routing.
+- **Card** — an agent's self-description: capabilities, tier, cost class, trust
+  class, availability. Maps 1:1 to an A2A Agent Card.
+- **Backbone** — the concrete LLM an agent currently uses for reasoning.
+- **Context ref** — a pointer (Hindsight bank id, git ref, KB task id, file
+  path) passed *instead of* pasted content.
+- **fabric-keeper** — the janitor daemon owning time semantics (§6b): lease
+  sweeps, deadlines, dead-letter, inbox digests. It never assigns work and
+  never triggers work.
+
 ## 1. Why
 
-Current pain, all observed on the live stack:
+Observed on the live stack:
 
-- **Duplicated LLM spend.** Every Adolf turn costs two Kimi calls: the reply
+- **Duplicated LLM spend** — an Adolf turn costs a ~32.8K-token reply call plus a
-  (~32.8K tokens in) and a *separate* background Hindsight retain/extraction
+  ~22.8K-token background Hindsight extraction; ~425 tokens are the conversation.
-  (~22.8K). Only ~425 tokens of that is the actual conversation.
+- **Tool bloat** — one Adolf carries ~84 MCP tool schemas + ~26 built-in tools
-- **Tool bloat.** One Adolf carries ~84 MCP tool schemas (~12K tokens) + ~26
+  every turn (~22K tokens), relevant or not.
-  built-in Kimi tools (~10K) on **every** turn, whether relevant or not.
+- **Quota cliffs** — Kimi's flat window (~60 msgs/5h, ~300/wk measured) makes
-- **Quota cliffs.** Kimi is a flat, window-limited subscription (~60 messages per
+  Adolf go dark with no degradation path and no way to park work.
-  5h, ~300/week measured). When the window is spent, Adolf goes dark. There is no
+- **Hardcoded background cognition** — Hindsight reflect/consolidation call a
-  graceful degradation and no way to park work until the window resets.
+  fixed model directly: no scheduling, no priority, no quota awareness.
-- **Background work is hardcoded to a model.** Hindsight's reflect/consolidate
+- **No growth path** — the ambition is autonomous research agents, remote llama
-  call a fixed LLM directly. There is no scheduling, no priority, no quota
+  nodes, more GPUs, more agents.
-  awareness, no way to say "do this on the big model when it's free".
-- **No room to grow.** The ambition is autonomous research agents, remote llama
-  nodes, more GPUs, more agents. None of that fits a single hardcoded assistant.
 
-## 2. Core concepts (and the distinctions that matter)
+## 2. The algebra
 
-The central insight: **an agent is not a queue, and a model is not an agent.**
+### Axiom 1 — everything that can receive work is an Agent
 
-### Task
+An agent is `(identity, Card, Policy, State)`. The Card advertises capabilities,
-The unit of work. Durable, addressable, and **context-by-reference**: a task
+**tier** (model strength it offers or needs), **cost class**, **trust class**
-carries *pointers* (memory bank id, git ref, board task id, file path), never
+(§5), and an **availability function a(t)**. Special cases:
-pasted context. Fields: id, intent, required capability/tier, target model queue,
-priority, status, context refs, result ref, submitter, deadline.
 
-### Model (backbone) — the scarce resource
+| Agent | Persona | Memory | Card highlights |
-A concrete LLM endpoint reached through the LiteLLM gateway. Examples today:
+|---|---|---|---|
-`kimi` (flat quota), `claude-haiku` (paid, already wired), local ollama
+| LLM endpoint (`kimi`, `gemma3:4b`, …) | trivial (identity) | none | tier, cost, quota-shaped a(t) |
-(`qwen3.5:4b`, `qwen3:8b`, `gemma3:4b`), later a remote llama box or a second GPU.
+| **Adolf** | proactive auditor (SOUL.md) | Hindsight bank `adolf` | trusted; scoped core tools |
+| **claude-coder** (Claude Code loop) | implementer | session + repo | trusted; pulls complex coding tasks |
+| **Torgash** | marketplace analyst | own bank | sandboxed; marketplace tools only |
+| **researcher** | autonomous researcher | own bank | sandboxed/untrusted inputs; own KB project |
+| router | delegator | none | resolves constraints → agents |
+| **alvis (the human)** | — | — | trust=human; a(t)=waking hours; **inbox = KB "waiting-on-me"** |
 
-**Each model has its own queue and its own worker**, because the model is what is
+The human being an agent is not a metaphor: approval gates, escalations and
-actually scarce (quota, VRAM, cost, rate limit).
+decisions are ordinary tasks submitted to his inbox. The KB column he already
+processes *is* that inbox.
 
-### Agent — the persona
+### Axiom 2 — one verb
-An agent is a **combination of personality + system prompt + memory + tool scope**
-(e.g. Adolf the proactive auditor; Torgash the marketplace analyst; a research
-agent; the Claude coding loop). An agent is a *configuration*, not a runtime
-resource. Critically:
 
-> **An agent may change its backbone LLM.** Adolf on Kimi today, on a local model
+```
-> tomorrow, on Claude for a hard task. Therefore **queues are keyed by model, not
+submit(task, target) -> taskRef        # await(taskRef) optional => sync
-> by agent.** An agent *submits into* and *consumes from* model queues.
+task = (intent, context-refs, constraints, priority, deadline, provenance)
+target ∈ { agent-id            # direct: “this backbone / this specialist”
+         | constraint-set      # tier/capability: “any large model with tools”
+         | auto }              # router decides by availability/quota/complexity
+```
 
-### Queue — per model, async, with a lifecycle
+Context travels **by reference, never by value** — the single most important
-Queues are asynchronous by design and differ in how they drain:
+efficiency rule for inter-agent communication (A2A context-passing practice).
+`sync` vs `async` is not a second mechanism: sync = submit + await.
 
-| Lifecycle | Behaviour | Example |
+**Transport rule.** Sync and async share the algebra but not the transport:
+**sync goes direct** — an A2A `message/send` RPC straight to the target agent's
+endpoint, journaled to KB afterwards; **async/durable goes through KB** and is
+drained by polling workers. KB polling must never sit on a sync path — a sync
+call may not inherit poll-interval latency.
+
+### Completion vs verification
+
+KB convention (native semantics, no new machinery): the **Done column =
+unverified completion** — the worker/agent finished and self-reported. **Closing
+the task = verified completion.** The producer never closes its own task; the
+submitter, a human, or (later) a reviewer-agent closes after checking the
+task's acceptance criteria. Lifecycle: … → done (unverified) → closed
+(verified). For code, the PR review is the verification; closing follows merge.
+
+### Theorems — the old rules become consequences
+
+1. **"Queues are per model, not per agent."** Every agent has an inbox, but
+   queues *accumulate* only where a(t) or throughput binds — at scarce agents:
+   model-agents and the human. Persona agents transform-and-delegate, so their
+   inboxes stay near-empty. The v1 rule is the scarcity special case.
+2. **Quota lifecycles are shapes of a(t).** always-on: a(t)=1. quota-gated
+   (Kimi): a(t)=0 when the window is spent — the queue **parks**, nothing fails,
+   drains on reset. cost-gated: a(t)=0 past budget. on-demand (remote llama):
+   a(t)=0 until woken. Four lifecycles, one function.
+
+   **Note on "cost":** in this lab the binding constraint is **quota and VRAM,
+   not money** — see §3a. The cost-gated shape exists for the optional paid
+   fallback only; it is not the normal case.
+3. **Hindsight reflect is just a submit** — `{intent: reflect, refs: bank+query,
+   constraints: tier≥large}`, async. Same for consolidation (low priority).
+4. **Backbone swap is a constraint edit.** Persona agents name constraints, not
+   endpoints; the backbone resolves per-submit. Adolf-on-Kimi today,
+   Adolf-on-local tomorrow — no code change.
+5. **The Claude Code loop is an ordinary consumer** — an agent whose policy is
+   "pull complex coding tasks from the fabric". It was never special.
+6. **For free:** escalation = re-submit with wider constraints (gated by policy,
+   §5); approval = submit(…, target=alvis); proactivity/cron = delayed
+   self-submission; the researcher = a low-priority self-submitting loop.
+
+### Granularity rule
+
+A **Task** is a durable work item with a lifecycle worth auditing. A single LLM
+completion inside an agent's turn is **not** a Task — it is an implementation
+detail, observable in Langfuse, invisible to Kanboard. This keeps the KB-literal
+fabric free of micro-churn by construction.
+
+## 3. Planes
+
+```
+┌─ Task plane (“the fabric”) ─────────────────────────────────────────┐
+│ Kanboard = the queue + audit + human inboxes (KB-LITERAL: no        │
+│ separate store). A2A semantics; claim/lease; priorities; parking.   │
+└─────────────────────────────────────────────────────────────────────┘
+┌─ Agent plane ───────────────────────────────────────────────────────┐
+│ Registry of Cards (persona, memory bank, tool scope, trust class,   │
+│ preferred tier, current backbone). Runtimes: OpenClaw (Adolf +      │
+│ specialists), Claude Code CLI, thin workers.                        │
+└─────────────────────────────────────────────────────────────────────┘
+┌─ Model plane ───────────────────────────────────────────────────────┐
+│ LiteLLM gateway (:4000). Auto Router v2 (2026-07-14) does the SYNC  │
+│ routing natively: pinned model | tier pools | complexity/semantic   │
+│ auto-routing (SIMPLE<MEDIUM<COMPLEX<REASONING), plus virtual keys,  │
+│ budgets, 429-fallback. alvis's three routing modes map 1:1:         │
+│   specific backbone → pinned model_name                             │
+│   “tier” routing    → tier pool                                     │
+│   automatic router  → auto_router/complexity_router                 │
+│ Classification is EMBEDDING-based on the local bge-m3 (semantic-    │
+│ router): no classifier LLM, no API spend (§3a).                     │
+│ Deployments: kimi (wrapper) | local small model | local embedder;   │
+│ metered/paid = opt-in fallback only, never implicit.                │
+│ The fabric owns everything LiteLLM cannot: ASYNC queueing, parking  │
+│ across quota windows, leases, cross-agent quota/GPU arbitration.    │
+└─────────────────────────────────────────────────────────────────────┘
+┌─ Context stores ────────────────────────────────────────────────────┐
+│ Hindsight banks (per-agent memory) · gitea (code, docs, this file)  │
+│ · KB task bodies · files. Tasks point here; payloads never inline.  │
+└─────────────────────────────────────────────────────────────────────┘
+```
+
+### 3a. Cost model — no metered API by default
+
+**Hard scope constraint (alvis):** the workflow is **Claude Code + the Kimi
+wrapper + a local GPU embedder + a small weak local model**. We do **not** pay
+per-token for API usage. Consequences that shape the design:
+
+| Resource | Nature | Constraint |
 |---|---|---|
-| **always-on** | worker drains continuously in the background | local ollama models |
+| **Claude Code** | flat subscription; a *runtime*, not a metered API | the `claude-coder` agent's capacity |
-| **quota-gated** | drains until the window is exhausted, then parks and resumes on reset | Kimi |
+| **Kimi** (via adolf-llm / hindsight-llm wrappers) | flat subscription, windowed | ~60 msgs/5h, ~300/wk — quota, not money |
-| **cost-gated** | drains under a budget ceiling; stops/falls back when spent | paid Haiku/Flash |
+| **local small model** (ollama) | free | GPU/VRAM contention (§3b) |
-| **on-demand** | node is woken/attached when work exists | future remote llama / extra GPU |
+| **local embedder** (bge-m3) | free, already resident | never-evict |
+| paid API (Haiku/Flash/…) | metered | **optional fallback only — disabled by default, explicitly opt-in** |
 
-A task parked on a quota-gated queue is not lost — it waits for the window, or is
+So the "cheap tier" is the **local small model**, not a cheap paid model. The
-re-routed if it is urgent and another queue can satisfy the required capability.
+budget governor (§5) therefore arbitrates **quota and GPU**, not spend. Any
+paid deployment in the LiteLLM config must be explicitly enabled per agent via
+its virtual key; nothing routes to a metered model implicitly.
 
-## 3. Architecture
+**Routing classification is embedding-based, not model-based.** LiteLLM's Auto
+Router (built on `semantic-router`) takes a configurable `embedding_model` +
+`match_threshold`, so complexity/semantic classification runs on the **local
+bge-m3** we already keep resident — no classifier LLM call, no API spend, ~zero
+marginal cost. (Auto Router v2 is new as of 2026-07-14 and has an open
+embedding-related bug report: verify hands-on and keep a heuristic
+keyword/length fallback for the classifier.)
 
-Four planes. Keeping them separate is the whole point.
+### 3b. GPU residency — a local model's a(t) is not 1
+
+Local "free" models contend for VRAM with interactive components (measured on
+the 8 GB GTX 1070: bge-m3 + gemma3:4b + tei-reranker ≈ 6.2 GB; loading anything
+bigger evicts the reranker and silently regresses recall latency). So a local
+model's availability is **a(t) = f(VRAM headroom)**, and the model registry
+carries a **residency policy**: a never-evict set (embedder, reranker —
+interactive-critical), allowed co-residency groups, and a pre-load check every
+worker must pass before pulling a model onto a GPU. With more GPUs this becomes
+a placement problem — same policy, more slots.
+
+### Personas and Cards are code
+
+SOUL.md files and agent Cards live **in git** and are deployed to runtimes —
+never edited live in volumes. "Who changed Adolf's soul" must be a `git log`
+answer. (The current SOUL.md in the adolf-state volume is migration debt.)
+
+### Eval gate on backbone/routing changes
+
+Backbone swap being "one constraint edit" is quality-blind. Each agent keeps a
+**golden set** (10–20 canonical exchanges); any backbone or routing change is
+shadow-replayed against it and compared (Langfuse datasets/evals) before taking
+effect. The algebra's flexibility must not become a silent-degradation machine.
+
+## 4. A2A: the protocol, adopted now
+
+The algebra maps 1:1 onto A2A v1.0 (Jan 2026), which is why we implement the
+real protocol immediately rather than "patterns first":
+
+| Algebra | A2A |
+|---|---|
+| Card | Agent Card (`/.well-known/agent.json`) |
+| submit / await | `message/send` (sync-ish) / `tasks/get` (async) |
+| task lifecycle | submitted → working → input-required → completed/failed/canceled |
+| notify | push notifications |
+
+Implementation: JSON-RPC 2.0 over HTTP on the LAN; each runtime (OpenClaw,
+Claude loop, workers) exposes/consumes A2A; Kanboard remains the durable state
+behind the endpoints. Scalability/extensibility later (remote nodes, third-party
+agents) then needs zero redesign.
+
+**Auth is mandatory on every A2A surface.** The LAN is **not trusted** — the
+xray/3x-ui VPN terminates other people's peers on it. No unauthenticated
+JSON-RPC listener, ever: shared tokens minimum, mTLS preferred.
+
+## 5. Trust & sandboxing
+
+**Trust classes** (on every Card):
 
 ```
-┌─ Coordination plane ──────────────────────────────────────────┐
+human > trusted > sandboxed > untrusted
-│  Task registry + lifecycle (Kanboard as blackboard today)      │
-│  context-by-reference; claim/status; audit trail               │
-└───────────────────────────────────────────────────────────────┘
-┌─ Agent plane ─────────────────────────────────────────────────┐
-│  Agent registry: persona + system prompt + memory bank +       │
-│  tool scope + preferred capability tier                        │
-│  (Adolf, Torgash, research-agent, claude-coder, …)             │
-└───────────────────────────────────────────────────────────────┘
-┌─ Scheduling plane ────────────────────────────────────────────┐
-│  Per-MODEL queues + workers; lifecycle policy (always-on /     │
-│  quota-gated / cost-gated / on-demand); priority; claiming     │
-└───────────────────────────────────────────────────────────────┘
-┌─ Model plane ─────────────────────────────────────────────────┐
-│  LiteLLM gateway: kimi | claude-haiku | local ollama | remote  │
-│  routing, fallback on 429/quota, per-agent virtual keys+budget │
-└───────────────────────────────────────────────────────────────┘
 ```
 
-**Shared context stores** (what task references point at): Hindsight (memory
+- **trusted** (Adolf, claude-coder): vault access **yes**; outward actions per
-banks), git/gitea (code + docs), Kanboard (task context), files.
+  existing ask-first rules.
+- **sandboxed** (Torgash, researcher): **no vault**, no outward sends; scoped
+  MCP allowlist; KB access **project-scoped** (researcher gets its own KB
+  project(s)).
+- **untrusted** = anything ingesting the open web: its *outputs* are tainted.
 
-### A2A on top
+**Taint / prompt-injection boundary:** tainted output may be written only to the
-A2A gives the vocabulary we otherwise have to invent: **agent cards**
+agent's own bank/notes/project. Promotion into a trusted agent's memory or into
-(capability advertisement), **task lifecycle states**, structured task
+any action requires a gate (initially: a task to alvis's inbox; later possibly a
-submission/tracking, and — most importantly — the **context-by-reference**
+reviewer-agent).
-pattern (send a `contextId`, let the worker read the shared store). We adopt the
-*patterns* first; the wire protocol can follow once more than one runtime needs
-to interoperate.
 
-## 4. Worked examples (the required minimal set)
+**Escalation policy (initial): always-ask.** A task that fails on its tier is
+not silently retried on a bigger model; it becomes a decision task in alvis's
+inbox. Revisit once behavior is observed (debugging phase by design).
 
-**(1) Hindsight `reflect` becomes an A2A task.** Reflect is async by design.
+**Sandboxed coding — workspace lease:** per **task**, not per agent:
-Instead of Hindsight calling a fixed LLM inline, it **submits a task** — intent
+`workspaces/<agent>/<task-id>/` = ephemeral gitea clone + branch; execution
-`reflect`, context ref = bank + query, required tier = *large* — onto the
+inside a container (no vault creds by default, network allowlist, resource
-large-model queue. A worker runs it when that model has capacity/quota; the
+caps); merge **only via PR** to gitea; autonomous agents never push to main.
-result is written back to the bank. Same for consolidation. This removes the
+Reviewer = human, or later a reviewer-agent (just another persona).
-hardcoded background call and makes memory work schedulable, priced, and
-quota-aware. (See also the "in-loop extraction" option, which is the cheaper
-counterpart for the *retain* path.)
 
-**(2) The Claude Code CLI loop is just an agent.** `claude-coder` = an agent
+**Global budget governor:** near the end of a quota window, interactive agents
-whose persona is "implementer", whose backbone is a Claude model, and whose
+(Adolf) outrank background ones (researcher, consolidation) — arbitration lives
-consumption rule is *pull complex/coding tasks*. It is a **special case of a
+in the fabric (priorities + a small governor rule), not in LiteLLM.
-queue consumer**, not a privileged component. This is why it already works:
-Adolf files tasks, the Claude loop pulls them. We are formalising what exists.
 
-**(3) Model queues ≠ agent queues.** Adolf may run on Kimi now and something else
+**Fabric hygiene (runaway protection):** agents submit tasks that cause agents
-later; Torgash may be cheap-tier normally and escalate to a large model for a
+to submit tasks — idempotency keys stop duplicates, not generative loops. So:
-tricky comparison. So a task is queued against **the capability/model it needs**,
+per-agent **task-creation quotas**; an **ancestry depth cap** on provenance
-and the agent identity travels *with the task* (persona + memory refs), not with
+chains; cycle detection at submit; and a **dead-letter** state for poison tasks
-the queue.
+after max-retries — never an infinite retry loop through paid quota.
 
-**(4) Queues drain differently.** The local queue works all night; the Kimi queue
+## 5b. Humans (plural) and memory partitioning
-stops at 100% of the 5h window and resumes after reset; a paid queue stops at its
-budget. Submitters therefore must state urgency, and the router must be able to
-re-route or park.
 
-## 5. Growing the lab
+There is more than one human already (alvis and elizaveta are both on Adolf's
+Matrix allowlist) and there will be more. Every human is an agent with
+trust=human, their own inbox, and — critically — **their own privacy domain**.
 
-- **More GPUs / remote llama** → new model entries + their own queues and
+**Memory partitioning (hard rules):**
-  workers; `on-demand` lifecycle for nodes that are not always up. Nothing else
-  changes.
-- **More agents** (research, finance, home) → new agent registry entries with
-  scoped tools + their own memory banks. They inherit queues and A2A for free.
-- **Autonomous research agents** → long-running, low-priority tasks on always-on
-  local queues, escalating to the large model only for synthesis. This is exactly
-  what per-model queues + priorities make affordable.
 
-## 6. Migration (phased, smallest useful step first)
+- **Per-human private banks**: `adolf-alvis`, `adolf-elizaveta`, … Everything
+  learned in conversation with human H goes to H's private bank by default.
+  **Content from one human's conversations must never surface to another
+  human.** This is a correctness property, not a preference.
+- **One shared household bank** for facts that are explicitly household-wide
+  (addresses, devices, routines, shared plans). Trusted agents may write;
+  **promotion from a private bank happens only by that human's explicit action
+  or approval task** — never automatically.
+- **Recall is interlocutor-scoped**: when Adolf talks to H it recalls from H's
+  private bank + the shared bank, nothing else. The recall/retain hooks select
+  the bank by interlocutor identity.
+- Sandboxed agents (Torgash, researcher) read at most the shared bank; never
+  any private bank. This is the cross-human face of the memory matrix.
+- The current single `adolf` bank is migration debt: split into
+  `adolf-alvis` + shared.
 
-1. **Registries + schemas** — model registry (endpoint, capability, lifecycle,
+**Human inbox design:** notifications are priority-routed — gate/urgent tasks
-   quota), agent registry (persona/prompt/memory/tools), task schema.
+ping the human via Matrix (Adolf initiates them; cf. proactive-messaging work),
-2. **One queue + one worker** — always-on local model, end-to-end.
+everything else lands in a daily digest from the fabric-keeper. Ignored gate
-3. **Quota-aware worker** — Kimi: park on exhaustion, resume on reset.
+tasks park and re-remind; **they never default-approve**. Vacation mode: a
-4. **A2A submission/tracking** with context-by-reference.
+human's a(t)=0 parks their inbox like any other scarce queue — gated flows
-5. **Cut over the examples** — Hindsight reflect → queue; Claude loop → declared
+wait; predefined degraded defaults apply where explicitly configured.
-   agent/consumer; Adolf → declared agent with scoped tools.
-6. **Scale** — remote/extra models, more agents.
 
-## 7. Open questions
+## 6. Executor — thin KB-polling workers
 
-- Is Kanboard the queue itself, or does it stay the *human-facing* board while
+No Temporal/Hatchet: at homelab scale (dozens of tasks/day) a durable-execution
-  workers use a dedicated queue store (and the two are synced)?
+platform would duplicate Kanboard as a second source of truth. Instead, one
-- Where does the routing decision live — submitter picks the tier, or a central
+small worker daemon per model-queue (compose services, ~200 lines, shared lib):
-  policy re-routes based on live quota/budget?
-- How much A2A do we actually implement (patterns only vs the real protocol)?
-- Claim/lease semantics: what happens to a task whose worker dies mid-run?
-- Does an agent's memory bank follow it across backbones (yes, by design) — and
-  what does that mean for extraction quality when the backbone is weak?
 
-## 8. Related
+```
+loop:
+  a(t) check        # quota/budget/health probe; if 0 → park (sleep, re-probe)
+  poll KB view      # filtered: my queue, status=queued, by priority
+  claim             # atomic: assign-to-self + column move + lease timestamp
+  resolve refs      # fetch context by reference
+  execute           # via LiteLLM (model-agents) / agent runtime (persona)
+  write result ref  # to the shared store; never inline
+  update status     # done | failed(retry policy) | input-required(→ inbox)
+```
 
-- Kanboard epic: architecture + LiteLLM gateway + multi-agent framework.
+Leases + heartbeats make dead workers safe: an expired lease returns the task to
-- Hindsight in-loop extraction (the cheap counterpart to queued reflect).
+queued. Two workers on one queue never double-run a task (claim is atomic).
-- Per-agent tool scoping (why Adolf stops carrying every tool).
+Idempotency keys on submission prevent duplicate proactive tasks. OpenClaw cron
+is the proactive *submitter* (Adolf's schedule); workers are the *drainers*.
+
+### 6a. Task schema — the KB-literal mapping
+
+Per the **KB-literal decision** (§10.2: *"Kanboard is the queue, humans
+included; no separate store"*): there is no separate task schema or task
+table anywhere — a Task (§2, Axiom 2: `(intent, context-refs, constraints,
+priority, deadline, provenance)`) is **entirely represented by native
+Kanboard task fields plus a small set of conventions layered on top**
+(columns, tags, comments). The table below is that mapping, field by field.
+It is not aspirational: every row is what `kb-claim` and `kb_worker.py`
+(`/home/alvis/kanboard/bin/`) already read or write today — those two files
+are the ground truth this table documents, not a separate spec to keep in
+sync by hand.
+
+| Task field | Represented as | Notes |
+|---|---|---|
+| **id** | native Kanboard task `id` | Globally unique per Kanboard instance. A `taskRef` in the algebra (§2) is `(project_id, task_id)` — project scopes columns/tags, so the pair (not the bare id) is what a worker needs to act on a task. |
+| **intent** | `title` (short) + `description` (full spec, Markdown) | `description` is the literal task body — what to do, acceptance criteria, links. `kb-claim next`/`take` print it verbatim under `--- spec ---`; nothing paraphrases it. |
+| **required tier** | *derived*, not stored — from native `score` | `score` is Kanboard's complexity field (Fibonacci: 1,2,3,5,8,13,21), normally set by the human, settable via `create_task`/`update_task`. `kb-claim`'s `tier_for(score)` maps it: `0` (unrated) → `sonnet` (safe default, task should be flagged for rating, not silently run cheap) · `≤2` → `haiku` · `≤5` → `sonnet` · `>5` → `opus`. The **complexity gate** (kb/CLAUDE.md): an unrated task is tagged `blocked` with a comment asking for a score, instead of being dispatched on the default tier. |
+| **target queue** | Kanboard **project** | Each queue is a Kanboard project — e.g. the `Adolf` project is what the always-on-local and Kimi-quota worker configs point `"project"` at (`workers/*.example.json`). Trust-scoped agents get their own project (§5: researcher gets its own KB project). `swimlane_id` is read and carried through every claim/park/done move but is not yet used to subdivide a queue further — headroom for later without a new field. |
+| **priority** | native `priority` field, `0`–`3` | Take-order (kb/CLAUDE.md): `p1 > p2 > p0 > p3` (note `p0` sits between `p2` and `p3`, not below `p3`). `kb-claim`'s `eligible()` sorts candidates by `(-priority, board position)`; the interactive `/kb` loop layers a WIP-resume dimension on top (resuming WIP outranks fresh `p2`) — that extra ordering lives in the orchestrator convention, not in `kb-claim` itself. |
+| **status** | *derived* from column + tags + open/closed — no single status field | **Column** (`Backlog`→`Ready`→`Work in progress`→`Done`, looked up by title via `columns()`) is the coarse lifecycle stage. **Open vs closed** (`status_id`/`closeTask`) is unverified vs verified completion (§2 "Completion vs verification", decision log #15): Done+open = unverified (`kb-claim done`, never closes its own task); Done+closed = verified (`kb-claim close`, by someone other than the producer). **Tags** carry the remaining states: `blocked` = input-required / parked for a human (escalation, missing score, or a fabric-keeper deadline escalation — same tag, one park primitive); `dead-letter` = poison, retries exhausted (`kb-claim deadletter`); `needs-human-verify` = machine-verified but sensitive, left open for a human to close (`kb-claim escalate`). Maps onto the A2A lifecycle (§4): submitted≈Backlog/Ready, working≈WIP, input-required≈WIP+`blocked`, completed≈Done+closed, failed≈`dead-letter`. (`canceled` has no Kanboard convention yet — not in scope here.) Claim freshness (the lease) is a **comment**, not a field: `🔒 claimed by \`agent\` at <ts>` / `lease renewed by …`, parsed back out by `lease-status` via regex — this is what lets an expired lease be swept back to `Ready` without a dedicated lease column. |
+| **context refs** | plain text *inside* `description` (and follow-up `comment`s) — convention, not a typed field | Per Axiom 2, "context travels by reference, never by value": a bank id, git ref/SHA, another KB task id (`#N`, which Kanboard auto-links), or a filesystem path, written as text. Enforced by review/convention, consistent with KB-literal — there is no schema-validation layer sitting in front of Kanboard to enforce it mechanically. |
+| **result ref** | the `note` on the `done`/`park`/`deadletter` comment | `kb_worker.py`'s `Outcome.note` (in-process) is flushed by `report_outcome()` to a Kanboard comment (`✅ {note}` for done) via `kb-claim done --note …`. No separate result store: the comment thread *is* the audit trail (kb#159's "closing is verification" model reads it). |
+| **submitter** | native Kanboard `creator_id` | Set automatically by `createTask`; exists on every task already. Distinct from `owner_id`, which is the *current claimant* and is what `claim`/`park`/`done` mutate. Not yet read by `kb-claim`/`kb_worker.py`/`fabric-keeper.py` — available, unused, out of scope for this task. |
+| **deadline** | native Kanboard `date_due` field | Enforced by `fabric-keeper.py`'s `enforce_deadlines()` (§6b "time semantics"): a fabric-owned, open, unparked task with `date_due` in the past gets `kb-claim park`ed (tagged `blocked`, comment naming the responsible inbox = current owner, or alvis if unassigned). Idempotent — already-`blocked`/`dead-letter` tasks are skipped so re-sweeps don't spam. |
+
+### 6b. Who is "the scheduler"? — decomposed, plus one janitor
+
+There is deliberately **no central dispatcher**. Scheduling decomposes into
+four concerns, each with its own owner:
+
+| Concern | Question | Owner |
+|---|---|---|
+| Triggering | when do tasks appear? | OpenClaw cron, agents' delayed self-submissions, humans |
+| Dispatch | which task runs next? | each queue's worker (claim by priority under its a(t)) |
+| Admission | may it run now? | LiteLLM budgets/rate + the budget governor |
+| **Time semantics** | expired leases, deadlines, stuck tasks? | **the fabric-keeper** |
+| Failure/anomaly response | something went wrong — now what? | Zabbix → the ops-agent (§6d) |
+
+The **fabric-keeper** is one tiny always-on daemon that neither assigns nor
+triggers work. It sweeps expired leases back to queued, enforces task deadlines
+(escalating to the responsible inbox), moves poison tasks to dead-letter, emits
+the human daily digest, and exports queue depths/ages to Zabbix.
+
+Its defining property is being **off the critical path**: kill it and work still
+flows (workers keep pulling and running) — only hygiene degrades. That is what
+separates a janitor from a scheduler, which in a push system would stop
+everything.
+
+**Deliberately NOT a keeper duty: missed crons.** A cron window missed because
+the host was down is not a task-lifecycle event — it's an *operational fault*,
+and re-firing it would make the keeper a trigger. Instead: schedules that must
+not be missed are **monitored in Zabbix** (a missed run raises a warning like
+any other infra fault), and the response is handled per-incident, not by a
+blanket catch-up policy (§6d).
+
+### 6d. Faults are incidents, not keeper chores
+
+Anything that "went wrong" — a missed critical cron, a service down, a queue
+backing up, a stale backup — surfaces as a **Zabbix problem**. Zabbix is the
+single place operational faults are detected.
+
+Response is **per-incident, not global**: each Zabbix problem class gets its own
+mitigation path, expressed as a task. Later this is automated by a dedicated
+**ops-agent** — a worker that watches Zabbix problems and, per problem class,
+either applies a known mitigation or files a task (to the right agent, or to a
+human inbox) with the incident as context. One blanket "catch-up policy" would
+be exactly the wrong abstraction: a missed briefing, a missed backup and a
+missed consolidation want completely different responses.
+
+### 6c. Kanboard is tier-0 now
+
+Promoting KB to the fabric's backbone promotes its ops class: **backups on par
+with the vault**, Zabbix monitoring of the service and API, and a defined
+**degraded mode** — if KB is down, Adolf still answers Matrix chat (no fabric
+operations, no task memory), workers park, nothing crashes or data-loses.
+
+## 7. Observability — Langfuse (kept), wired for real
+
+Decision: keep **Langfuse** (already deployed; best-in-class self-hosted:
+traces + per-token cost + prompt management + evals, MIT). Grafana rejected for
+this role — generic metrics with no LLM semantics (the source of past
+dissatisfaction); Zabbix keeps infra monitoring. To do (it currently receives
+nothing): LiteLLM success/failure callbacks → Langfuse; tag every trace with
+`agent`, `task-id`, `queue`; per-agent cost dashboards; upgrade v2→v3. Every
+completion is traced here — this is where sub-Task granularity lives.
+
+## 8. Growing the lab
+
+- **More GPUs / remote llama** → new model-agent Cards with `on-demand` a(t)
+  (health probe, wake hook, graceful absence). Routing skips absent nodes.
+- **More specialists** → new Cards + scoped tools + own banks. The fabric and
+  A2A don't change.
+- **Autonomous research agents** → low-priority loops on always-on local queues,
+  escalating (via always-ask, initially) for large-model synthesis; own KB
+  project; tainted outputs until promoted.
+
+## 9. Migration order
+
+1. Registries: model Cards + agent Cards (schema + populate).
+2. First thin worker end-to-end on an always-on local queue.
+3. Quota-gated worker (Kimi park/resume). Claim/lease semantics.
+4. A2A protocol surface (JSON-RPC + Agent Cards) over the fabric.
+5. Cutovers: Hindsight reflect → fabric; consolidation → fabric (low prio);
+   claude-coder + Adolf declared as registry agents (Adolf's tools shrink to
+   scoped core).
+6. Trust enforcement: capability grants (virtual keys + MCP allowlists), taint
+   gate, budget governor, langfuse wiring.
+7. Scale: Torgash, researcher (own KB project), on-demand nodes.
+
+## 10. Decision log (2026-07-21, alvis)
+
+1. Single completions are not Tasks (langfuse-only) → no micro-churn.
+2. **KB-literal**: Kanboard is the queue, humans included; no separate store.
+3. Trust classes as §5; vault = trusted only.
+4. Escalation = always-ask initially.
+5. Researcher: KB access allowed, own project(s), scope-limited.
+6. Real A2A protocol now (JSON-RPC + Agent Cards).
+7. Proactive schedules: OpenClaw cron → fabric.
+8. Langfuse kept as the observability layer; Grafana rejected; Zabbix = infra.
+9. Executor = thin KB-polling workers; no Hatchet/Temporal at this scale.
+10. Sync routing = LiteLLM Auto Router v2; fabric owns async/parking.
+11. Sandbox = per-task workspace lease + container + PR-only merges.
+
+Added in v2.1 (hardening review, same day):
+
+12. **Multi-human**: per-human private banks + shared household bank;
+    interlocutor-scoped recall; cross-human leakage forbidden (hard rule);
+    promotion to shared only by the owning human's action/approval.
+13. A2A auth mandatory everywhere — the LAN is untrusted (VPN peers).
+14. KB = tier-0 infrastructure (backup, monitoring, degraded mode).
+15. Done column = unverified completion; closed task = verified; the producer
+    never closes its own task.
+16. Scheduler = decomposed (cron/self-submission triggers; workers dispatch;
+    LiteLLM+governor admit); the **fabric-keeper** janitor owns time semantics
+    (leases, deadlines, dead-letter, digests) and neither assigns nor triggers.
+17. Personas/Cards live in git, deployed — never edited live.
+18. Backbone/routing changes gated by golden-set shadow eval (Langfuse).
+19. Fabric hygiene: creation quotas, ancestry depth cap, cycle detection,
+    dead-letter for poison tasks.
+20. GPU residency policy: local a(t)=f(VRAM); never-evict set (embedder,
+    reranker); pre-load checks.
+21. Transport: sync = direct A2A RPC (journaled); async = KB polling; polling
+    never on a sync path.
+22. **Missed crons are not a keeper duty**: schedules that must not be missed
+    are monitored in **Zabbix**; faults are incidents handled **per problem
+    class** by a future **ops-agent** (§6d), never by a blanket catch-up policy.
+23. **No metered API by default** (§3a): scope = Claude Code + Kimi wrapper +
+    local GPU embedder + small local model. The cheap tier is the *local*
+    model, not a paid one; paid deployments are opt-in fallback per virtual
+    key. The governor arbitrates **quota and GPU, not money**.
+24. **Routing classification is embedding-based** on the local bge-m3 via
+    LiteLLM Auto Router (`semantic-router`): no classifier LLM call, no API
+    spend. Keep a heuristic fallback — Auto Router v2 is new (2026-07-14) with
+    an open embedding bug report.
+25. Hindsight gains an **optional caller-supplied cognition mode** (#131) so it
+    can run inside a Claude Code session with zero extra API calls; the
+    API/queue path stays the default. Same verb, different `target`.