nisse/docs/Architecture-Build-Spec.md

Nisse — Build Specification

A build-oriented spec for two self-hosted AI agents sharing a common library. This document states requirements per component so major pieces can be broken out and built independently. It is deliberately lighter on the supporting services (Matrix, Gitea, Vikunja, Outline, LiteLLM) and heaviest on the agent library and the two agents, which is where the real design work lives.

Document history. The Tiered Inference amendment has been folded into this document and no longer exists separately: its A-series components appear as A10–A16, its network and credential revisions in A5 and B3, and its revised build order as Part G. The inference-hardware question it raised has since been resolved in principle (Part H: a 4090-class host).

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0. Naming

The project is Nisse, after the Nordic farm spirit that keeps the place running overnight and expects to be treated well in return. Three artifacts:

Artifact	Name	Role
Shared library	nisse-core	The spine both agents sit on (Part A)
Dev Agent	nisse-dev	Plans and implements software projects (Part B)
Sysadmin Agent	nisse-ops	Operates infrastructure (Part C)

Throughout this document, Dev Agent and Sysadmin Agent name the roles in conceptual discussion; nisse-dev and nisse-ops name the artifacts — packages, container images, VM hostnames, Matrix identities, and service accounts. Use the same string across all of those: the VM, the container, the Caddy host, the Matrix account, and the Bitwarden machine account for a given agent all share one name, so that grepping the infrastructure for nisse-ops finds everything.

The three artifacts live as three packages in one repository, nisse (decision recorded in Part H). Directory and package names carry the identity. If mirrored to GitHub, it goes under a personal account rather than an org — github.com/nisse is squatted and GitHub does not reclaim inactive usernames. nisse-core is free on PyPI if packages are ever published.

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0.1 What We're Building

Two agents:

1. Dev Agent — nisse-dev (build first) — plans and implements software projects, produces Gitea PRs for human review, reacts to review comments and commits.
2. Sysadmin Agent — nisse-ops (follow-on) — operates and maintains infrastructure, driven by tickets, gated on high-consequence actions.

Both are thin applications over a shared nisse-core library. The library is extracted from the Dev Agent after it works — not designed up front for two consumers that don't exist yet. Build the Dev Agent as if the library were internal, then lift the seams that proved real.

The three artifacts live in one repository (decided — Part H), so the extraction step in the build order is a package split within one tree, not a repo split.

Guiding principles (apply to everything below)

• Human-gated autonomy. Agents work independently once told to start, but never cross key boundaries (start implementation, apply infrastructure change) without explicit sign-off.
• No per-command approval. Within an approved unit of work, execution is unattended. Control lives at the boundaries, not per action.
• Isolation matched to blast radius. Dev Agent is disposable and sealed; Sysadmin Agent is trusted with live-infrastructure reach. They never share a running instance.
• Stateless image, stateful volumes. Containers are replaceable; durable state lives on named volumes and must survive both redeploys and unexpected reboots.
• Structured channels for machines, prose channels for humans. Never route machine event intake through human-facing formatters (chat bridges).
• Own the stack. Everything self-hostable stays inside the network where practical.

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PART A — THE AGENT LIBRARY (nisse-core)

The shared spine. Every component here is consumed by both agents. This is the part to get right.

A1. Transport

Purpose: all human⇄agent conversation — commands in, questions/approvals/updates out.

Requirements:

• A Transport interface the orchestrator depends on; no platform SDK imported directly anywhere else. Methods (at least): send_message, send_approval_request, ask_question, on_message (inbound callback/stream).
• MatrixTransport is the first and primary implementation. Self-hosted homeserver (Synapse or Conduit) keeps the control plane in-network. Must support threaded conversations (for plan discussion) and per-agent identities.
• Interface must make adding TelegramTransport / SlackTransport later a matter of one new class + config, no orchestrator changes.
• Both agents run as distinct identities (@nisse-dev, @nisse-ops) in separate rooms on the same homeserver.

Explicitly not required: rich message formatting, custom clients. Existing Matrix clients (Element) are the UI.

Matrix is strictly a UI channel. Although the homeserver is self-hosted and therefore not "remote," room history must not become load-bearing context. The canonical log (A10) is the sole source of truth.

A2. State Store and Durability (critical path — build first)

Purpose: durable, crash-safe persistence of everything that must outlive a process restart. This is what makes the whole system safe to redeploy and reboot; it blocks everything else.

Must persist: active project/task, per-project state-machine position, approvals granted, pending questions, session continuity, (dev) git clone locations.

Disposable (never on the state volume): agent code, build artifacts/test containers, shipped-out logs.

Requirements:

• Crash-atomic writes. SQLite in WAL mode (default choice) or atomic file writes (write-temp → fsync → rename). A power cut mid-write must never produce torn state.
• Checkpoint after every state transition, with fsync. No "flush later" — in-memory state that hasn't hit disk is assumed lost.
• Durable intent log. Before a unit of work starts, record intent, so a restart knows what was in flight.
• Idempotent transitions reconciled against external truth. On any operation with an external side effect (open PR, push branch), restart must detect the effect already happened (query Gitea/git) rather than repeat it.
• schema_version field from the first commit, with a migration hook, so a newer image can read older state.

A3. Boot-Time Reconciliation

Purpose: turn an ambiguous crash into a safe, known state.

Every startup (clean or post-crash), before accepting operator commands:

1. Open state store (WAL recovery is automatic).
2. Run any pending schema migration.
3. Read intent log; look for a transition that began but never committed.
4. Reconcile against external truth (Gitea/git for the dev agent; relevant services for the sysadmin agent).
5. Interrupted mid-delegation (A13) — apply the deterministic resume rule. Auto-resume the delegation — re-render its transcript from the canonical log and continue the loop, posting a notice (not a question) to transport — when the cut is clean: the last durable event is a completed tool_result or assistant turn, the delegation branch sits at the expected commit, no cap is breached, and external truth matches the log. Gate to AWAIT_INPUT when the last event is a tool_call with no recorded result whose effect cannot be verified by inspection (tree diff for edits, Gitea query for pushes), or on any branch/log mismatch. The judgment is an observable check, never model self-assessment — the same reasoning as A12.
6. Any other interruption → move to AWAIT_INPUT and post to transport (e.g. "restarted mid-milestone-2, branch at commit abc123, continue or re-plan?"). Never blind-resume.
7. Otherwise resume the last committed state.

A4. Control Loop, State Machine, and Gating Primitives

Purpose: the generic engine that drives an agent through gated work. Each agent supplies its own concrete states; the primitives are shared.

Requirements:

• A state machine framework: named states, transitions, and human gates. Each gate blocks until an explicit operator action arrives via transport.
• Gating is structural, not advisory. The mechanism (used by both agents):
  • Expose a submit_plan(type, content) tool to the model. Calling it posts the plan for approval and ends the agent's turn. The agent cannot self-advance.
  • Expose an ask_human(question) tool — non-terminal: pause, post question, block until answered, resume.
  • Do not expose any "proceed" / "start" tool. Advancement past a gate happens only when the orchestrator receives operator approval and hands over the next task. The agent has no path to approve itself.
• Approval sources. A gate waits on an approval event from any registered ApprovalSource — the Matrix transport (A1), the Gitea comment poller (B2), or the Vikunja comment stream (C4). First valid approval wins and is consumed; the gate records which channel satisfied it, and the orchestrator echoes the outcome to the other channels so the records converge. Each source authenticates the approver against a per-channel operator allowlist (Matrix MXID, Gitea username, Vikunja username).
• Approvals bind to a plan revision, never to "the current gate." Every submitted plan revision carries a monotonically-increasing tag (rev-N). An approval naming a stale revision is rejected with a notice, never applied to a newer one — this is what makes multi-channel approval race-free.
• One consistent approval UI across all channels. Approval is always the typed word approve, attached to a specific revision: in Matrix, a reply to the plan message (the reply relation carries the binding; a bare approve in the room binds to the latest revision only when unambiguous, otherwise the agent asks); in Gitea and Vikunja, a comment approve rev-N, with the tag printed in the plan comment for copy-paste. Any other reply/comment is reject <feedback>. Reactions/emoji are never an approval path. Approvals are consumed at first sight; later edits to an approval comment are ignored and logged.
• Autonomy inside an approved unit of work: all tool/command use auto-approved (Claude Agent SDK bypassPermissions or an allow-all canUseTool). Escalation to the human happens only via ask_human.
• The loop must survive restart mid-work (relies on A2/A3).

A5. LLM Access (tiered)

Purpose: reach both a locally-hosted model and frontier models through one interface, with the frontier vendor swappable by config.

A locally-hosted model drives the control loop and performs mechanical work; frontier models are engaged for work that exceeds local capability. Two requirements drive the tiering:

1. No remotely-held state. Conversation and task state lives on the agent VM. No provider holds anything the system depends on to resume.
2. Vendor fluidity. The frontier tier should be swappable — Anthropic, OpenAI, Google, or a vendor that does not exist yet — as a configuration change, not a refactor.

These two are only in tension if portability is required within a session. Making frontier engagements ephemeral and self-contained (A13) allows vendor-specific session data to be used freely inside a delegation while keeping the durable record vendor-neutral.

Requirements:

• One client interface, complete(canonical_log, tools, model) -> canonical_events, present at the first real LLM call — not introduced as a later cutover. The same reasoning that produced the Transport abstraction.
• LiteLLM in front of everything, from the start. The agent VM gets exactly one LLM destination; LiteLLM fans out to the local inference host and to frontier vendors behind it. This also resolves the RFC1918 conflict in B3 — a LAN-resident inference host would otherwise need a second hole punched in the egress allowlist.
• Passthrough for the vendor driving a given session (/v1/messages for Anthropic), so vendor-specific features are not lost on the path where they matter — delegations (A13) always take this path. Normalized routing for everything else; the canonical log (A10) reconciles the difference.
• Fallback chains configured at LiteLLM. Rate limits and outages differ per vendor; a frontier failure should not kill a task.
• No vendor credentials on the agent VM. LiteLLM holds every key, sourced from Bitwarden machine accounts (A7); the agent holds one LiteLLM token. Adding a vendor does not touch the agent VM, and a compromised agent leaks no provider keys.
• Config from env/.env, nothing hardcoded: base URL, auth token, and the role mapping (A11). Changing any of it is an env change + restart, nothing more.
• Budget caps and spend alerts live on the LiteLLM virtual key, not in agent code.

A6. Browser Tool

Purpose: drive web UIs. Shared implementation; each agent points it at a different network with different trust.

Requirements:

• Playwright (headless Chromium), exposed to the model as tool actions: navigate, click, type, read DOM, screenshot. Screenshot + accessibility-tree loop so the model can operate UIs it wasn't trained on.
• Runs inside the agent's VM. Network reachability is set by the agent's deployment, not the tool.
• Must support authenticated sessions (for the sysadmin agent's admin UIs) while being usable without auth (dev agent's test apps).

A7. Secrets Client

Purpose: fetch credentials at runtime instead of baking them into images.

Requirements:

• Client for Bitwarden/Vaultwarden via machine account / Secrets Manager (or bw CLI + API key). Not the personal vault.
• Reads only from a dedicated, minimally-scoped machine account per agent.
• A single bootstrap token (the machine-account credential) is the one secret that lives on the box: tight file perms, never in image or repo.
• (Deferred, not first cut: dynamic/short-lived secrets via Vault/OpenBao. Leave the interface able to grow toward this; don't build it yet.)

A8. SSO Identity

Purpose: each agent authenticates as itself.

Requirements:

• Each agent is a dedicated LLDAP principal (svc-nisse-dev, svc-nisse-ops) in a tightly-scoped Authelia group. Never the operator's account.
• Support an unattended-auth path: because interactive MFA is impossible for a headless agent, allow a narrow IP-scoped, group-scoped, target-scoped single-factor exception. Keep it specific on all three axes.

A9. Logging

Purpose: observability without bloating state.

Requirements: structured logs to journald / external log stack, not kept in-container. Every gate crossing, tool call, and escalation is logged.

State-change log conventions. Every durable mutation of agent state emits exactly one structured log event, so problems can be diagnosed after the fact by replaying the operational trail:

• Emission is owned by the state store. The log line is written by the same code path that performs the durable write, immediately after the commit returns — a state-change line exists iff the change hit disk. Callers supply the cause; no other layer logs durable mutations, so the trail cannot drift from what was persisted.

• Format: one JSON object per line, with a fixed envelope on every event:

  • ts — UTC ISO-8601 timestamp
  • agent — nisse-dev | nisse-ops
  • event — dotted type from the taxonomy below
  • task_id — correlation ID for the enclosing unit of work, minted when the unit starts (use <project>, milestone start, ticket pickup) and threaded through every event it causes; null only outside any unit (e.g. boot).
  • cause — what triggered the mutation: operator command, approval (rev-N + channel), escalation trigger, reconciliation verdict, or crash recovery.

• Event taxonomy (extended as components are built; names are never reused or renamed):

  Event	Event-specific fields
  state.transition	from, to, project
  intent.begin / intent.commit / intent.abort	intent_id, kind
  gate.open / gate.approve / gate.reject	gate, rev, channel
  boot.reconcile	action, per-intent verdicts
  schema.migrate	from_version, to_version

• Logs are diagnostic, never load-bearing. Nothing reads logs back to derive state — the state store (A2) is the sole source of truth, the same rule that keeps Matrix room history (A1) non-authoritative.

A10. Canonical Transcript Schema

The durable record is a canonical event log in SQLite, not a provider-shaped messages array. Provider request bodies are rendered at call time from this log.

Event	Payload
user_message	text, source (Matrix event ID)
assistant_text	text, model that produced it
tool_call	canonical call ID, tool name, arguments
tool_result	canonical call ID, result, success/failure
plan_submitted	plan body, authoring model
gate_opened	gate type, reason
delegation_started	brief, target role, resolved model, caps
delegation_completed	handback note, artifacts, tokens, duration, exit status
compaction	summary, producing model, range of events replaced

Rules:

• Canonical tool-call IDs are minted locally. Provider IDs are mapped at the boundary and never used as a join key. Providers differ in both ID format and in where tool calls sit structurally (content blocks vs. a separate array on the assistant message).
• No provider-side handles for content. If a Files API is used, store the bytes or a content-addressed local path; upload or inline at render time. A provider file ID must never be the only reference to something.
• Vendor-opaque reasoning artifacts are stored, tagged with the producing model, and never replayed outside the session that produced them. This covers Anthropic signed thinking blocks, OpenAI encrypted reasoning items, and Gemini thought signatures. Inside a delegation, replay per that vendor's rules. On delegation exit, the artifacts become archival only. A "session" here is the delegation, not the process lifetime: a delegation resumed after a crash (A3) is the same session and may replay its own artifacts.
• Token budgeting is a render-time concern. Context window and tokenizer are properties of the model being called. Compute budget when building the request, not when writing the log.
• Compaction summaries are first-class log events with the producing model recorded, so a tier change does not silently inherit opaquely-compacted history.
• Prompt cache breakpoints are placed by the renderer and never stored. A tier change means a cold cache — a cost event, not a correctness event.

A11. Role Configuration

Model identity is expressed as roles, resolved to concrete model strings in config:

roles:
  executor:    local/<model>          # drives the loop, mechanical work
  planner:     <frontier>             # authors plans
  delegate:    <frontier>             # scoped sub-task execution
  consultant:  <frontier>             # advisory, no repo access

"Best model this week" is a config edit. The resolved mapping is pinned at task start and recorded in the log, so a mid-task config change cannot split a task across two planners.

A12. Escalation Triggers

The local model does not decide when it needs help. Small models are poorly calibrated about their own competence and will escalate either constantly or never. Triggers are observable from outside the model:

1. Phase policy. Planning and architecture escalate unconditionally. Mechanical edits stay local.
2. Failure count. Local attempts; tests fail twice; escalate with the failures attached to the brief.

A model-invoked ask_expert (A14) exists in addition to these, never as a replacement, and carries a per-task budget so a confused executor cannot spend twenty consults circling.

A13. Delegation

Escalation is delegation: a frontier model receives a scoped sub-task, works autonomously with full tool access, and hands back. It is not a consult, and the local model does not interpret or re-derive the frontier model's reasoning.

Harness — one, not one per vendor. Do not adopt each vendor's agent SDK to obtain their best agentic behavior; that recreates the lock-in this design avoids, relocated. One loop, one cap regime, one handback contract.

Normalization lives in the A10 renderer, not on the wire. Vendors disagree structurally about tool calling (content blocks vs. tool_calls arrays, tool-result placement, streaming deltas) and about reasoning artifacts (signed thinking blocks, encrypted reasoning items, thought signatures) — and a normalizing middlebox is lossy exactly where a long-running delegation lives. So:

• Delegations run over LiteLLM's passthrough route (/v1/messages for Anthropic, per A5), with the A10 renderer producing the vendor-native request body. The renderer is a per-vendor adapter by definition — it already has to know each wire shape to render the canonical log.
• LiteLLM's normalized route is reserved for calls where fidelity is cheap: ask_expert (A14), fallback chains, and trialing a new vendor before its renderer adapter exists.
• Adding a vendor therefore means writing one renderer adapter — or provisionally accepting LiteLLM's normalization for that vendor and validating it against the A16 eval set before trusting it with delegations. A day-one build needs exactly one adapter: the vendor actually in use.

Scoping and authority. Delegation sits inside the plan gate, not around it. A frontier sub-agent that acts freely would bypass submit_plan (A4), which is the structural control the design rests on.

• Planning is itself a delegation that produces a plan and stops. Human approves.
• Execution delegations are scoped to a sub-task the approved plan already contains.

The frontier model is smart twice; neither time is it authorized to expand the work.

Because plans may be executed by a materially weaker model, the planner system prompt must require explicit, low-inference plans — the executor cannot reconstruct intent.

Isolation and caps.

• Branch at delegation start. Abort discards the branch. This gives delegations the same atomicity property that boot-time reconciliation (A3) gives interrupted state transitions.
• Hard caps on turns, tokens, and wall clock.
• Checkpoint delegation events as they arrive. Every tool_call / tool_result / assistant turn inside a delegation is durably written to the canonical log when it happens (per A2), never batched at handback. This is what makes a delegation resumable after a crash — the deterministic resume rule lives in A3.
• Defined failure return. On cap breach or explicit failure, the local loop gates to AWAIT_INPUT rather than resuming on top of a half-edited tree.

Handback. The final delegation action is a structured handback note written for a weaker reader, not a human-facing summary:

• what changed
• what is now true that was not before
• what to do next

That note plus the resulting file state is the entire interface back to the local loop. The delegation's internal transcript is stored alongside the composite event, vendor-tagged, and never replayed into anything.

Rejected alternative: ask-and-apply

An alternative was considered in which the local model asks a frontier model for advice and applies the answer itself. Rejected.

Its principal advantage was blast radius — the frontier model never touches the repo, and everything flows through a gated local loop. That was explicitly not a concern here.

Without it, the comparison is lopsided:

• Delegation lets the frontier model read the code, run the tests, and see the failures. Ask-and-apply forces it to reason blind from whatever the local model thought to include in the brief, and small models write poor briefs.
• The cost advantage is illusory: one delegation that succeeds beats three consult round-trips that do not.
• Any variant where the local model interprets rather than applies verbatim reintroduces the capability gap downstream of the expensive call.

The diff-application machinery, verbatim-fidelity rules, and local-model-as-verifier design are all dropped.

A14. ask_expert (retained, minimal)

One narrow case survives delegation: questions requiring no repo access. "Which of these two approaches is better," "why does this library behave this way," "is this API deprecated."

• Single stateless call, text answer.
• No diff application, no fidelity concerns, no session state.
• Reuses the delegation brief format.
• Structured output contracted at the prompt level, not the API level — vendors differ on JSON mode and schema enforcement. Parse, validate against a local schema, retry once.

Expected implementation size: small. Do not let it grow into a second escalation subsystem.

A15. Local Model Accommodations

• Constrained decoding for tool-call JSON.
• Reduced tool schema for the executor role: fewer tools, flatter arguments, no deeply nested optionals. Maintain two tool manifests keyed on role, rendered at request time like everything else.

A16. Delegation Evaluation Capture

Capture a consult/delegation eval set from day one. Each delegation_started / delegation_completed pair (A10) is already a clean input-output record; the additional cost is marking outcomes as good or bad.

Without this, "best model this week" is decided by benchmark chatter, and swapping the frontier tier becomes something to avoid because the effect is unmeasurable. With it, a new model is a config change and a re-run.

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PART B — THE DEV AGENT / nisse-dev (build first)

A thin app over nisse-core. Plans and implements software projects; produces PRs.

B1. Workflow (state machine)

IDLE
  → [use <project>]            clone repo, begin architecture planning
PLANNING_ARCH
  → agent drafts architecture + implementation plan → submit_plan
AWAIT_ARCH_SIGNOFF             ← human gate (approve / reject+feedback)
  →
MILESTONE_PLANNING
  → agent drafts plan for a specific milestone → submit_plan
AWAIT_MILESTONE_SIGNOFF        ← human gate
  → [start milestone <n>]
EXECUTING                       autonomous; no per-command approval
  → may enter AWAIT_INPUT (ask_human) any time it's unsure
PR_OPEN
  → open PR; react to review comments and commits (review loop)
  → on acceptance → next milestone, or project complete

B2. Requirements

• One active project at a time. Operator selects it.
• Operator commands (over transport): use <project>, approve, reject <feedback>, start milestone <n>, status, plus free text during planning (forwarded to the agent as answers).
• Plan gating per A4: architecture plan and each milestone plan require sign-off before the next phase. Enforced structurally. Plans and approvals are externalized to Gitea issue comments (Part H, decided); Matrix and Gitea are both approval channels, with revision binding and the uniform approve UI per A4.
• Autonomous execution within a started milestone: builds, runs, tests without per-command approval; asks questions any time via ask_human.
• Gitea integration:
  • Outbound: git + Gitea API to push branches and open PRs, via a dedicated Gitea service account with a scoped token. Record any runnable test service's URL/port in the PR description.
  • Inbound: react to PR comments, commits, pushes. Poll the Gitea API (15–30s) rather than take webhooks, to keep the VM outbound-only. Intake is structured JSON — never via a chat bridge.
  • On Gitea unavailability: the poller backs off (exponential, capped at a few minutes) and retries indefinitely, logging the outage. Polling is pure intake — an outage delays reactions but never fails a task or forces a state transition. Outbound Gitea operations that fail mid-flight are already covered by the intent log and boot-time reconciliation (A2/A3).
• Browser (A6) scoped to the internal test network only — used to exercise web apps it built and reproduce bugs. No SSO needed for this.

B3. Deployment & Isolation

• Dedicated VM (Proxmox), not a bare container — the agent builds and runs Docker containers, so it needs its own real Docker daemon. (Docker-in-a-container via socket-mount ≈ host root; via dind = nested-storage pain. The VM boundary is the correct trust boundary for unattended arbitrary command execution.)
• Sealed to start: egress allow-list (LiteLLM, Gitea, package registries), no ingress. Egress denial must include RFC1918 except the specific LiteLLM/Gitea IPs — sealed from the internet must also mean no reach to NAS/Proxmox/UDM.
  • The LAN-resident inference host is not a second exception. It sits behind LiteLLM (A5), so the allowlist keeps exactly one LLM destination regardless of how many models or vendors are in play.
• No Tailscale on this VM (it routes around firewall policy). Manage via Proxmox console / jump host.
• Own VLAN from day one (even a VLAN of one) and test services on predictable ports (e.g. 18000–18099), so opening access later is a firewall edit, not a rebuild. (Sealed for now; may relax later.)
• Test containers on a dedicated internal Docker network with no route out.
• Build-daemon storage on a separate mount from the state volume, so docker system prune between milestones can't touch agent state.

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PART C — THE SYSADMIN AGENT / nisse-ops (follow-on)

A thin app over the extracted nisse-core. Operates infrastructure; driven by tickets; gated on consequences. Built after the Dev Agent works.

C1. How it differs (not reducible to config)

Axis	Dev Agent	Sysadmin Agent
Rollback	revert snapshot / discard branch	none for zfs destroy or a firewall lockout
Trust model	allow-all in sandbox	tiered allow / deny / confirm policy
Work shape	long milestones → a PR	short reactive tasks + scheduled checks → live changes
Driver	Gitea projects	Vikunja tickets
Placement	sealed, own VLAN, no Tailscale	Tailscale, credentials, broad reach

Running both roles in one instance would collapse isolation — build them as separate deployments over the same library.

C2. Permission Policy Engine (the defining addition)

Tiered policy, evaluated on every action:

• Read-only telemetry (metrics, logs, status) — free.
• Reversible service actions (restart a container) — allowed.
• High-consequence actions (storage, networking, auth) — confirm-first over transport before executing.

This is the sysadmin analog of the dev agent's plan gate: the human boundary sits at irreversible / high-blast-radius actions.

Special cases fold into the tiers:

• Browser actions against admin UIs default to confirm-first. Changes to network gear (UniFi) are the most conservatively gated action in the system — a bad remote UI click can sever the path used to fix it.
• Action-triggering tickets are operator-scoped (C4). Auto-generated tickets (e.g. from a monitoring alert) route through confirm-first, never drive unattended.

C3. Interfaces

• Browser (A6): into admin UIs. Prefer real APIs where they exist (Proxmox, TrueNAS good; UniFi partial); use the browser only for genuinely UI-only gaps — it's the least stable interface.
• SSO (A8): logs into admin UIs as svc-nisse-ops. This account is a high-value credential; its blast radius equals its Authelia scope, so scope it minimally.
• Secrets (A7): UI/SSO logins and service API tokens from Bitwarden.
• Event intake: monitoring feeds the orchestrator as structured JSON (directly or via Alertmanager), never through a Matrix bridge. The agent posts curated, state-aware updates to #ops ("Gitea 502ing, restarted, back up").

C4. Ticket-Driven Control (Vikunja)

The sysadmin analog of "dev agent driven by Gitea."

• Vikunja ticket = task of record (durable, has status/queue/history). Matrix = live conversation about it (questions, confirm-first prompts, progress).
• Flow: ticket filed → agent picks up → discuss/confirm over Matrix → update status as it progresses → close with result.
• Mechanics: Vikunja REST API + webhooks (same two-direction, JSON-only pattern as Gitea); dedicated service account, scoped token from Bitwarden. Verify the installed version's webhooks fire on comment creation; if not, poll comments as B2 polls Gitea.
• Approvals: Vikunja is an ApprovalSource (A4) for plan gates on its own tickets, via approve rev-N comments. Live confirm-first prompts (C2) stay Matrix-primary — polling latency makes a ticket comment a poor interactive channel — with the outcome written back to the ticket as the record.
• Trust: a driving ticket is an instruction channel into an infrastructure- changing agent → action-triggering ticket creation is operator-scoped (C2).

C5. Deployment & Isolation

• Longer-lived, stateful VM (not disposable).
• Has Tailscale + scoped credentials to reach infrastructure across both sites.
• Updated in place via the container pipeline, restart gated on an idle flag.

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PART D — NOTES / KNOWLEDGE (Outline)

Shared knowledge store for both agents (sysadmin-primary). Not a message bus — see note below.

Requirements:

• Read — runbooks as context. Procedures written in Outline are fetched before acting, constraining the agent toward your procedures over improvised ones.
• Write — durable record. Agent writes change logs / incident notes / findings back to Outline: searchable, off-box, and an extra externalized record that survives volume loss.
• Access via Outline REST API, dedicated service account, token from Bitwarden.

Deliberately rejected: using a shared notes store (Outline or an Obsidian vault) as an agent-to-agent message bus. It's unstructured, race-prone (last-write-wins, no delivery semantics), and a shared read-write store both agents touch would undermine the dev/sysadmin isolation boundary. Agent coordination, if ever needed, goes through the human, Matrix, or a Vikunja ticket handed from one agent to another — or a real message queue if genuine multi-agent async ever arrives.

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PART E — SUPPORTING SERVICES (installed)

Status: all supporting services are installed and running. What remains for each is per-agent configuration (service accounts, scoped tokens, rooms), created as each agent is built — not standing up the service itself.

• Matrix homeserver (Synapse/Conduit): ✅ installed. Remaining per-agent: create the two bot identities (@nisse-dev, @nisse-ops), one room per agent + #ops.
• Gitea: ✅ installed. Remaining per-agent: nisse-dev service account + scoped token; enable native-JSON webhooks or confirm API polling access.
• LiteLLM proxy: ✅ installed, and on the path from the first LLM call (A5) — it is the agent VM's single LLM destination, providing per-agent virtual keys with budget caps, central logging, vendor swap, fallback routing, and the Anthropic passthrough. Remaining per-agent: mint a virtual key; configure routes for the local inference host and each frontier vendor, with keys sourced from Bitwarden.
• Local inference host: 🔜 decided in principle, hardware pending — a 4090-class server (Part H). Interim: a Jetson Nano running Ollama validates the plumbing, and the executor role maps to a Haiku-class frontier model via LiteLLM until the server arrives. Everything in A5, A12, and A15 assumes the eventual host sits on the LAN behind LiteLLM.
• Vikunja: ✅ installed. Remaining (sysadmin): service account + scoped token; webhook/poll config (Part C4).
• Outline: ✅ installed. Remaining per-agent: service account + API token (Part D).
• Bitwarden/Vaultwarden: ✅ installed. Remaining per-agent: dedicated minimally-scoped machine account + bootstrap token (A7).
• LLDAP + Authelia: ✅ installed. Remaining per-agent: create svc-nisse-dev / svc-nisse-ops principals, scoped groups, and the narrow unattended-auth exception (A8).

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PART F — DEPLOYMENT MODEL (both agents)

• Containers + persistent volumes. Stateless image; named volumes carry state across redeploys (and allow optional persistence between deployments).
• Image delivery: Gitea Actions → Gitea OCI registry → Watchtower for the pull.
• Gate the restart, not the pull: an idle/"safe-to-restart" flag; Watchtower must never restart mid-work. Unplanned interruptions (reboot/crash) are handled by the durability layers (A2/A3), not by restart timing.
• Survive reboots: VM + Docker service set to start on boot; restart: unless-stopped; ZFS snapshots of the state dataset (scheduled + on each approval).
• Terraform + cloud-init: deferred. When adopted, bpg/proxmox + cloud-init, VM definitions as code, clean destroy/apply; the dev agent becomes a candidate for immutable rebuilds.

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PART G — BUILD ORDER

1. State store + durability (A2/A3) — critical path; everything sits on it.
2. Transport (A1, Matrix) + control loop/gating (A4) with a stub agent that just echoes plans — get the approval loop feeling right before any real LLM.
3. LiteLLM + LLM client interface (A5). The interface must exist at the first real LLM call, not as a later cutover. Mint a virtual key with a budget cap; verify prompt caching survives the passthrough.
4. Canonical log schema and renderer (A10). Before any transcript accumulates in a provider-shaped form that would have to be migrated later.
5. Local executor loop (A11/A12/A15) — role resolution pinned at task start, phase policy, failure-count escalation, reduced tool manifest, constrained decoding.
6. Gitea layer — polling intake, PR creation, comment/commit handling.
7. Delegation harness + plan-gate integration (A13/A14). Begin capturing the eval set here (A16); the records are free once the composite events exist.
8. Browser tool (A6) for nisse-dev — test apps on the sealed network.
9. First genuinely unattended milestone (budget caps + ZFS rollback earn their keep here). Crash-atomic writes and reconcile-on-boot must be in place first.
10. Extract nisse-core; build the Sysadmin Agent (nisse-ops): policy engine (C2), SSO + scoped browser (A8/A6), secrets (A7), Vikunja control (C4), Outline runbooks (D), scheduled checks, alert intake.
    • With the monorepo layout (Part H, decided), extraction is a package split within one tree: move code behind a package boundary, no release machinery required.

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PART H — OPEN DECISIONS

• Inference hardware — resolved in principle, no longer blocking. Target: a 4090-class host (24GB VRAM) running a 24–32B open model at 4–5-bit quantization (candidates: Devstral Small 24B, Qwen3-32B / Qwen3-30B-A3B, GPT-OSS-20B). Models of this class are competent at tool calling and mechanical edits, which puts the system in "tiering genuinely load-bearing" territory rather than "local model as dispatcher." Consequences:
  • Context, not weights, is the VRAM pressure. A 32B at 4-bit leaves roughly 5GB for KV cache, and agent loops render long contexts. Model size vs. context length vs. KV-cache quantization is an empirical trade — tune it, and the thresholds that depend on local capability (A12 failure count, A14 consult budget, A15 manifest size), at build-order step 5 on the real hardware.
  • Interim posture: until the server exists, the executor role points at a cheap frontier model (Haiku-class) through LiteLLM — a config entry under A11, nothing structural, so the build order does not stall. The Jetson Nano validates plumbing (Ollama → LiteLLM → agent) only; nothing learned from models it can run transfers to threshold tuning.
  • If the 24GB class proves insufficient in practice, additional hardware is the fallback — the role indirection (A11) makes that swap invisible to the agents.

The rest resolve when reached, not now.

• Repository layout — decided: one monorepo, three packages. For a solo operator, three repos tripled the admin surface (tokens, CI, webhooks, permissions, backups) and imposed a version-pinning contract on nisse-core — coordinated bumps every time the shared interface moves — while buying nothing: the repo boundary was never a trust boundary in this design (the VM is), and independent release cadence is irrelevant solo. A monorepo is also less friction while nisse-core is being extracted from a working nisse-dev and its seams are still moving. One repo can still build both agent images via path-filtered CI. Splitting later stays easy if a real need appears; merging later would not have been.
• Sysadmin browser access to network gear: standing vs. per-task hand-off. The lock-yourself-out risk argues for per-task.
• Policy action taxonomy (C2): the concrete list of which operations are allow vs. confirm. Best decided with the agent half-built in front of you.
• Approved-plan externalization — decided: Gitea-backed from the start. Plans are posted as issue comments on the project repo; the approval comment (author, timestamp, revision tag) is the durable off-box record, authenticated by Gitea's own auth. The state store keeps only a pointer ("gate 3 satisfied by comment 8841"), so boot-time reconciliation (A3) can re-derive gate state from Gitea even after state-volume loss — external truth wins (A2). Approval mechanics and revision binding are specified in A4.

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Repository Layout

nisse-core/              # shared library (extracted after nisse-dev works)
  transport/             #   matrix | telegram | slack                (A1)
  state/                 #   persistence, reconcile, migration     (A2/A3)
  control/               #   loop, state machine, gating, ask_human   (A4)
  llm/                   #   client interface, role resolution        (A5)
  log/                   #   canonical event log + renderer          (A10)
  delegation/            #   delegation harness, ask_expert       (C1/C3)
  browser/               #   Playwright tool                          (A6)
  secrets/               #   Bitwarden machine-account client         (A7)
  sso/                   #   LLDAP identity + auth path               (A8)
  logging/               #   structured operational logs              (A9)

nisse-dev/               # app: sealed VM                        (Part B)
  workflow (arch/milestone gating), Gitea PR layer, allow-all sandbox
  browser → internal test network only

nisse-ops/               # app: separate host, Tailscale         (Part C)
  policy engine, tiered confirmation, scheduled checks
  Vikunja ticket intake, Outline runbooks/records
  browser → admin UIs (API-first, UI fallback), confirm-first gating

infra/                   # (future) Terraform + cloud-init       (Part F)
  nisse-dev.tf | nisse-ops.tf | templates/

These are three packages under one repository root (nisse), plus docs/ for this specification (Part H, decided). The module boundaries are what matter; the package boundary is where the step-10 extraction lands.

log/ carries the canonical event log and its renderer (A10); delegation/ carries the delegation harness and ask_expert (A13/A14).