adolf/DESIGN-a2a-agents.md

# DESIGN — Agap Agent Platform v2.1: the agent algebra

Status: **v2.1, agreed with alvis 2026-07-21** (v2 `7be30c71` + hardening review)
Owner: alvis · Written with Claude

One design, two axioms, one verb. Everything alvis asked for — per-model queues,
quota parking, Hindsight reflect as an async task, the Claude Code loop as a task
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

Observed on the live stack:

- **Duplicated LLM spend** — an Adolf turn costs a ~32.8K-token reply call plus a
  ~22.8K-token background Hindsight extraction; ~425 tokens are the conversation.
- **Tool bloat** — one Adolf carries ~84 MCP tool schemas + ~26 built-in tools
  every turn (~22K tokens), relevant or not.
- **Quota cliffs** — Kimi's flat window (~60 msgs/5h, ~300/wk measured) makes
  Adolf go dark with no degradation path and no way to park work.
- **Hardcoded background cognition** — Hindsight reflect/consolidation call a
  fixed model directly: no scheduling, no priority, no quota awareness.
- **No growth path** — the ambition is autonomous research agents, remote llama
  nodes, more GPUs, more agents.

## 2. The algebra

### Axiom 1 — everything that can receive work is an Agent

An agent is `(identity, Card, Policy, State)`. The Card advertises capabilities,
**tier** (model strength it offers or needs), **cost class**, **trust class**
(§5), and an **availability function a(t)**. Special cases:

| Agent | Persona | Memory | Card highlights |
|---|---|---|---|
| LLM endpoint (`kimi`, `gemma3:4b`, …) | trivial (identity) | none | tier, cost, quota-shaped a(t) |
| **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"** |

The human being an agent is not a metaphor: approval gates, escalations and
decisions are ordinary tasks submitted to his inbox. The KB column he already
processes *is* that inbox.

### Axiom 2 — one verb

```
submit(task, target) -> taskRef        # await(taskRef) optional => sync
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
```

Context travels **by reference, never by value** — the single most important
efficiency rule for inter-agent communication (A2A context-passing practice).
`sync` vs `async` is not a second mechanism: sync = submit + await.

**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 |
|---|---|---|
| **Claude Code** | flat subscription; a *runtime*, not a metered API | the `claude-coder` agent's capacity |
| **Kimi** (via adolf-llm / hindsight-llm wrappers) | flat subscription, windowed | ~60 msgs/5h, ~300/wk — quota, not money |
| **local small model** (ollama) | free | GPU/VRAM contention (§3b) |
| **local embedder** (bge-m3) | free, already resident | never-evict |
| paid API (Haiku/Flash/…) | metered | **optional fallback only — disabled by default, explicitly opt-in** |

So the "cheap tier" is the **local small model**, not a cheap paid model. The
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.

**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.)

### 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):

```
human > trusted > sandboxed > untrusted
```

- **trusted** (Adolf, claude-coder): vault access **yes**; outward actions per
  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.

**Taint / prompt-injection boundary:** tainted output may be written only to the
agent's own bank/notes/project. Promotion into a trusted agent's memory or into
any action requires a gate (initially: a task to alvis's inbox; later possibly a
reviewer-agent).

**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).

**Sandboxed coding — workspace lease:** per **task**, not per agent:
`workspaces/<agent>/<task-id>/` = ephemeral gitea clone + branch; execution
inside a container (no vault creds by default, network allowlist, resource
caps); merge **only via PR** to gitea; autonomous agents never push to main.
Reviewer = human, or later a reviewer-agent (just another persona).

**Global budget governor:** near the end of a quota window, interactive agents
(Adolf) outrank background ones (researcher, consolidation) — arbitration lives
in the fabric (priorities + a small governor rule), not in LiteLLM.

**Fabric hygiene (runaway protection):** agents submit tasks that cause agents
to submit tasks — idempotency keys stop duplicates, not generative loops. So:
per-agent **task-creation quotas**; an **ancestry depth cap** on provenance
chains; cycle detection at submit; and a **dead-letter** state for poison tasks
after max-retries — never an infinite retry loop through paid quota.

## 5b. Humans (plural) and memory partitioning

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**.

**Memory partitioning (hard rules):**

- **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.

**Human inbox design:** notifications are priority-routed — gate/urgent tasks
ping the human via Matrix (Adolf initiates them; cf. proactive-messaging work),
everything else lands in a daily digest from the fabric-keeper. Ignored gate
tasks park and re-remind; **they never default-approve**. Vacation mode: a
human's a(t)=0 parks their inbox like any other scarce queue — gated flows
wait; predefined degraded defaults apply where explicitly configured.

## 6. Executor — thin KB-polling workers

No Temporal/Hatchet: at homelab scale (dozens of tasks/day) a durable-execution
platform would duplicate Kanboard as a second source of truth. Instead, one
small worker daemon per model-queue (compose services, ~200 lines, shared lib):

```
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)
```

Leases + heartbeats make dead workers safe: an expired lease returns the task to
queued. Two workers on one queue never double-run a task (claim is atomic).
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`.