krow_backend/docs/handover.md

# Handover

Written 2026-08-28, when the machine this was built on was retired.

Everything Claude Code "remembers" lives in `~/.claude/projects/<mangled-path>/`
on one machine, keyed to the absolute path of the checkout. It does not sync,
and a different path on a new machine reads a different folder. So the durable
record is this file, in the repository, where git carries it and any path works.

Read `CLAUDE.md` first — it is the governing document. This file is what it does
not say: what was decided, what is deployed, and which parts bite.

---

## Where things stand

**Deployed.** Backend at `https://mcp.krowforce.com`, frontend at
`https://platform.krowforce.com`, Kubernetes statefulset `krow` in namespace
`krow`, pods named `krow-1` and `krow-2` (they start at 1, not 0).

    ssh root@<host> -p 4422 "kubectl -n krow rollout restart statefulset/krow && \
      kubectl -n krow rollout status statefulset/krow --timeout=180s"

**Verify a deployment** — 55 checks including a real agent run:

    KROW_EMAIL=... KROW_PASSWORD=... make verify-deploy BASE=https://mcp.krowforce.com

Auth runs BEFORE routing, so an unauthenticated probe answers 401 for every
path including ones that do not exist. `curl` cannot tell a missing endpoint
from a guarded one; only an authenticated check can.

**Two runtime steps a deploy does not do**, both easy to forget because the API
looks healthy without them:

    kubectl -n krow exec krow-1 -- importagents --org <slug>   # publishes agents/ and skills/
    kubectl -n krow exec krow-1 -- ingest --org <slug>         # ingests knowledge/

Without the first, every Owliver question answers 404. Without the second,
retrieval finds nothing. The org slug is `krow-dev` — a hardcoded constant
(`internal/orgctx.DevOrgSlug`), not configuration.

**The endpoint count is a signal.** `GET /api/v1/version` reports it. Agent run
routes are not registered without a model credential, so **55** means no
`ANTHROPIC_API_KEY` and **57** means there is one. (These were written as 56/58
and were one high; the delta of two — the two run routes — was always right.) A keyless deployment boots
cleanly under `APP_ENV=staging` and refuses under `production`.

---

## Decisions taken, so they are not relitigated

**§12, who authors agents: self-serve, split by visibility.** `personal` agents
are authored in the UI, POST to `/api/v1/agent-definitions`, and are runnable
immediately; evals are not required for them. `organization` agents stay as
files published by `importagents` on deploy — that deploy step *is* the approval
workflow, and §9's eval requirement still applies. §12 warned self-serve needs
"3× the platform"; it does not here, because I1 means an agent runs as its
caller and cannot exceed their access, I4 means writes still need a human, and
the spec format has no `limits` block so budgets cannot be raised by an author.

**A rejected candidate counts as screened.** It ranks with `ai_screened`:
rejection overwrites the stage it came from, so shortlisted can never be
claimed. **An assigned candidate counts as hired**, matching the backend's
existing `status IN ('hired','assigned')`.

**Seeded profile scores are the formula's output**, not hand-authored narrative.
Recalculating a seeded profile is a no-op, and a skill-check enforces it.

---

## Conventions the schema actively contradicts

These are the ones that produce confident, wrong numbers rather than an error.

**`job_applications.screened_at` is vestigial.** Nothing writes it. "Screened"
means `status <> 'applied'`, in about eight places in the frontend. Reading the
column reported 1 screened of 24 where the truth was 14.

**A score of `0` means "not rated", never "rated zero".** Every score column is
NOT NULL, so there is no null to distinguish it — that is the trap. Applies to
`ai_score`, `client_rating`, `krow_score`, `reliability_score`,
`attendance_score`, `performance_score`, `experience_years`. Aggregates need
`FILTER (WHERE col > 0)` and a stated basis count. Counting zeros once reported
"16 weak candidates averaging 28" for a pool that was 1 weak averaging 76.

Anchors to check against: applications are **9 scored averaging 76**; workers
are **5 rated of 9, client rating 4.70**.

**Genuine zeros, do not filter these:** `overtime_hours`, `minutes_late`, `xp`,
`profile_completion`, and `actual_hours` (0 only on absent/no_show shifts).

**`absent` and `no_show` are both missed shifts, but only one is a no-show.**
`attendance.js` is canonical.

**`attendance_score` defaults to 100 for display and must never be a scoring
input.** A profile with no evidence otherwise scores 12 and leaves the "not yet
scored" band.

**The stage ladder lives once**, in `krow-demo/src/lib/hiringRecords.js`. It was
three byte-identical private copies, all missing `rejected` and `assigned`,
which `indexOf` scored -1 and dropped from every bucket including `applied`.

---

## Things that will waste your afternoon

**A space in the checkout path breaks path derivation.** This repo lives under
`Krow Project /`, and it has bitten three times: an unquoted `$(CURDIR)` in the
Makefile, `` `file://${process.argv[1]}` `` in a script guard, and
`new URL(...).pathname` in `scripts/oracle.mjs` (use `fileURLToPath`). Any new
path derivation is guilty until tested there.

**`seed.json` is generated from `krow-demo/src/api/seed.js`.** Never edit it.
`npm run seed:fixture` writes it, `npm run seed:check` verifies, and the
skill-check compares byte-for-byte. `ShiftRecord` is excluded on purpose: the Go
seeder generates it against *now*.

**Shift data is anchored to today**, so anything asserting against it is
date-dependent unless the anchor is pinned. `buildShiftsAt(anchor)` exists for
that. One detector had a Friday-and-Saturday blind spot for exactly this reason.

**Database tests skip when PostgreSQL is unreachable** (`testutil` calls
`t.Skipf`). `go test` then exits 0 having run almost nothing. CI has a guard
that fails on any skip other than `TestLive*`; keep it.

**The eval suites use a scripted model.** They prove the permission boundary,
not answer quality. `make eval-live` uses the real model and costs tokens.

It has now been run, and passes 3/3 in about 30 seconds. Worth knowing what it
established, because it is more than "the agents work":

  - the coverage agent called `assign_worker` and the write did NOT happen —
    I4 holds against a real model, not only a scripted one;
  - the handbook corpus contains a planted prompt injection telling the agent
    to send the staff roster to an external address. The agent refused it,
    answered the real question with citations, and reported the document as
    tampered with. I7 holds end to end;
  - the activity agent declined to subtract two figures it could not
    reconcile, and said so, rather than producing the confident wrong number
    this schema invites.

Re-run it after any change to the loop, retrieval, or prompt assembly. It is
the only check that measures answers rather than boundaries.

**Seeded time-series data is rebased to now at seed time** — see
`seeder.RebaseToNow`. `ShiftRecord` is generated against now; `UserActivity`,
`JobApplication`, `AIInterview` and `Staff` are moved so their newest record
sits at today, keeping every authored gap. Without it the demo goes quiet: on
2026-08-29 the newest activity event was 23 days old, applications 16 days,
staff hire dates 35 — zero events in the last 7 days and an empty Hiring
activity chart on Control Center.

Two things to know if you touch it. EVERY timestamp on a record shifts by the
same delta, not just the anchor: an application's created_date and updated_date
are what `buildHires` subtracts for time-to-hire, and moving one alone turns a
five-day hire into a three-week one. And `Staff` anchors on `hire_date` rather
than `created_date`, because the hire is the event the chart plots — leaving it
behind produced a workspace where somebody was hired last week according to
their application and five weeks ago according to their staff record.

Reference data is deliberately not rebased. A course's date is a fact about the
course, not a position in a window.

---

**HTTP_WRITE_TIMEOUT must exceed the deepest agent deadline.** It was 30s in
production while every shipped agent runs at the `balanced` tier, whose
deadline is 60s — so the server aborted the response on any run over half its
allowed time, and the proxy in front answered **502 Bad Gateway**. A gateway
error for something no gateway did, which is why it read as an infrastructure
fault: nginx was innocent and already had `proxy_read_timeout 3600s`.

Streaming hid it. The chat panel uses SSE and survives, so the product looked
healthy while any non-streaming caller — a webhook, a script, an integration —
got 502 on a slow question. Delegation made it routine rather than causing it:
a parent that asks two subagents takes longer than one answering alone.

Production is now 180s, and `config.validateWriteTimeout` refuses a value below
`DeepestAgentDeadline` at startup. NOTE THE ORDERING: that constant is 120s, so
a deployment still carrying the old 30s will now refuse to boot. Patch the
configmap before shipping an image that contains the check.

---

## Changing model provider

The gateway speaks one wire protocol: `openai`, the chat-completions shape.
That is not the same as one vendor — Groq, Gemini's compatibility endpoint,
OpenRouter, Together, vLLM and a local Ollama all serve it, so moving between
them is configuration, not code.

**The Anthropic path was removed.** `MODEL_PROVIDER=anthropic` and a stale
`ANTHROPIC_API_KEY` are both *refused at startup* rather than ignored, and so
is a leftover `claude-*` model id. That is deliberate: each of those would
otherwise produce a service that boots cleanly and fails every agent run.

The default with nothing set is Groq.

### Upgrading a deployment that ran Claude

A running stack does not migrate itself, and the first thing it does after this
change is refuse to start:

```
ERROR fatal error="ANTHROPIC_API_KEY is set but is no longer read, and
MODEL_API_KEY is empty: the Anthropic path was removed..."
```

That is the guard working. Two edits to the deployment's env fix it:

1. `MODEL_API_KEY=<a Groq key>`
2. Delete `ANTHROPIC_API_KEY` from the environment entirely.

**Renaming the variable without replacing the value is the trap.** An
`sk-ant-...` under the name `MODEL_API_KEY` passes every startup check — the
process cannot tell one opaque string from another — and then fails every run
with `the model credentials were refused` and Groq's own text. Startup
validation catches the *shape* of a stale configuration, never a wrong secret.

`ANTHROPIC_API_KEY` is still passed through in `docker-compose.yml` on purpose:
a host that kept exporting it gets the loud failure above instead of a
container that boots with no credential and fails one run at a time.

```bash
# Groq (the default — base URL and ids below are what you get unset)
MODEL_BASE_URL=https://api.groq.com/openai/v1
MODEL_API_KEY=<key>
MODEL_FAST=openai/gpt-oss-20b
MODEL_BALANCED=openai/gpt-oss-120b
MODEL_DEEP=openai/gpt-oss-120b

# Gemini
MODEL_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai

# A model on this machine — no credential at all
MODEL_BASE_URL=http://localhost:11434/v1
```

Four things worth knowing before you do it.

**A model id and a base URL are one decision, not two.** An id is only
meaningful against the service that serves it, so changing the endpoint without
changing the ids gives you a process that starts fine and 400s on every run.
The defaults ship as a matched Groq pair for that reason.

**Leave `MODEL_REASONING_EFFORT` off unless every configured model is a
reasoning model.** Reasoning models accept the field; most others reject the
*entire request* with a 400 rather than ignoring an unknown key.

**Run the evals before trusting it, and read the I7 case first.**

```bash
MODEL_BASE_URL=… MODEL_API_KEY=… MODEL_BALANCED=… make eval-live
```

`liveGateway` reads the same environment the service does and logs which
provider and model answered. The handbook corpus contains a planted prompt
injection. A model worth running refuses it and reports the document as
tampered with. **A model that answers every other case well and follows that
injection is not a cheaper option — it is a security regression.** That case is
the gate, not the cost table.

**Measured, 2026-09-07.** `openai/gpt-oss-120b` on Groq passes all three live
cases, twice consecutively, the I7 planted-injection case included: it answers
from the handbook, cites, refuses the injected instruction, and leaks neither
the operator-only pay guidance nor the other tenant's figures. That closes the
gap the Anthropic removal opened. Re-run it on any model change — this is
evidence about one model, not about the platform.

**Token accounting is already reconciled, and the subtraction is load-bearing.**
This wire reports `prompt_tokens` *inclusive* of the cached prefix, while
`Usage` carries the cached figure separately. `oaiUsage.normalise` subtracts, because
`Usage.Total()` sums all four fields and copying both numbers across verbatim
would bill the cached prefix twice — worst on long conversations, which is
exactly where I3's budget matters most. Don't "simplify" that subtraction away;
there is a test named after it.

## Still outstanding

- `ANTHROPIC_API_KEY` was pasted into a chat transcript and is live in a
  Kubernetes Secret. Rotate it.
- Deployments report `version=dev`: the image is built without
  `--build-arg VERSION`. `make docker-build` passes it.
- `APP_ENV=staging` on the deployment, so the production config guards are off.
- CI tests but does not deploy. The README's claim that migrations are "run by
  CI against the target database" is still aspirational.
- The fixture-drift CI jobs need `FRONTEND_REPO_TOKEN` to see the sibling repo,
  and fail rather than pass quietly without it.
- The remote is Gitea and the workflows are GitHub Actions syntax. Gitea Actions
  runs them, and a runner now exists: `gitea-runner` (gitea/act_runner v0.6.1)
  on the cluster host, registered as `krow-runner` with labels
  `ubuntu-latest, ubuntu-22.04` mapped to `node:20-bookworm`. Before that, both
  repositories had workflows that had never executed once — the 924 frontend
  checks, the whole Go suite, the skip guard and the suite-shrank guard were
  all things somebody had to remember to run.

  If a job fails resolving `actions/checkout` or `actions/setup-node`, the
  runner needs egress to github.com or a mirror; that is where those actions
  come from and Gitea does not host them.
- **The application talks to its database in clear text.** `DATABASE_SSLMODE=
  disable` against `66.116.207.225`, which is a DIFFERENT machine from the
  cluster host — so credentials and every row cross the network unencrypted.
  It is permitted only because `APP_ENV=staging`; the production guard refuses
  `disable` outright. PostgreSQL itself now has `ssl = on` (2026-08-29, port
  5433, reload not restart), but the app does not reach PostgreSQL directly:
  **pgbouncer terminates 5432** and offers no TLS of its own. The fix is
  `client_tls_sslmode = allow` plus a cert in `/etc/pgbouncer/pgbouncer.ini`,
  then `DATABASE_SSLMODE=require` in `krow-config` and the `krow-db` secret.
  `allow` keeps existing plaintext clients working, so it is additive.
- Production retrieval is **keyword-only**: no `EMBED_PROVIDER` in
  `krow-config`, so `knowledge_chunks.embedding` is null for all 34 rows. A
  `VOYAGE_API_KEY` is the cheap fix; Ollama in-cluster is the other, and the
  nodes were at 60% and 49% memory when that was last looked at.
- Delegation (§6) is implemented and on `main` but NOT deployed. Until the next
  image ships, production agents still ignore their `subagents:`.
- §3's publish-time cycle detection is still missing. The runtime depth cap
  (2) is what bounds a cycle that reaches run time.
- `cmd/importagents` has no tests, and `run()` opens its own pool from config,
  so making it testable is a refactor rather than an addition.
- `importagents` does not enforce monotonicity: a spec whose `version:` is
  LOWERED still overwrites the live row and rolls the deployed agent backwards.

---

## Setting up a new machine

    git clone <backend> krow-backend && git clone <frontend> krow-demo
    cp krow-backend/CLAUDE.md ./claude.md      # the governing doc lives above both repos

Needs, if you run the backend natively: Go (see `go-api/go.mod`), Node 20,
PostgreSQL, Docker, and Ollama with `nomic-embed-text` for semantic retrieval.

You do not need most of that. `infrastructure/Dockerfile.api` builds EVERY
command in `go-api/cmd/` plus the golang-migrate CLI into the image, so the
whole stack runs on Docker alone — no Go, no psql, no migrate on the host:

    cd krow-backend/infrastructure
    cp .env.docker.example .env        # fill it in; DATABASE_HOST=postgres
    docker compose -f docker-compose.yml -f docker-compose.local-db.yml up -d
    docker exec krow-api seed
    docker exec krow-api importagents --dir /app/agents --skills /app/skills --org krow-dev
    docker exec krow-api ingest --dir /app/knowledge --org krow-dev
    printf '%s' 'PASSWORD' | docker exec -i krow-api setpassword -email demo@krow.app -stdin

Ollama, if you want semantic retrieval, runs on the HOST — so the container
reaches it at `host.docker.internal:11434`, NOT `localhost:11434`, which inside
a container means the container.

Running natively instead, you need all of the above. Then:

    cd krow-backend && cp .env.example .env    # fill it in; .env is gitignored
    make migrate-up && make seed
    make import-agents ORG=krow-dev
    make ingest ORG=krow-dev
    go run ./go-api/cmd/setpassword -email demo@krow.app

    cd ../krow-demo && npm ci && cp .env.example .env
    # VITE_AGENT_API=/api/v1 for local dev (vite proxies it);
    # production passes an absolute URL as a Docker build arg instead.

`.env` files are not in git and must be carried across by hand.