Files
classic-models-agent-flows/CLAUDE.md
T
Stéphane Tailland 95b6e3a1c9 docs: the events are emitted by the API, and what that implies
Recording where the work lands and the two facts about that codebase that
shape it: every write passes through one BaseModelViewSet, so one hook covers
every domain; and the models are managed = False, so a transactional outbox
introduces the estate's first Django-managed table beside the pre-existing
schema.

Emission belongs in the API rather than a database CDC or a gateway plugin,
because only the API knows which change is a business event and only it holds
the sub/azp that the event must carry for a downstream agent to act under the
same authority.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-29 13:49:18 +02:00

155 lines
6.7 KiB
Markdown

# Flows — the autonomous agents
**Runtime:** n8n · **Mode:** autonomous · Two agents in one repo.
| Agent | Client id | Trigger |
|---|---|---|
| Approvisionnement | `classic-models-agent-approvisionnement` | event-driven, plus a periodic ordering run |
| Recouvrement | `classic-models-agent-recouvrement` | weekly, Monday |
They share a repo because they are not code: n8n workflow exports, same tool,
same owner. Splitting them would buy nothing.
## Why low-code for these two
Their work is rules, thresholds and due dates — a sequence of conditions and
calls, which a visual editor expresses better than code. The LLM is optional on
their critical path, which makes them cheaper and more predictable than the
conversational agents.
They remain full A2A agents: they publish an Agent Card, accept tasks, answer.
## Approvisionnement
| | |
|---|---|
| MCP | catalog, sales |
| Scopes | `catalog:read`, `catalog:write`, `sales:read` |
Detect coming stock-outs, propose reorders — **two jobs with two different
triggers**, which an earlier revision of this file collapsed into one nightly
run.
*Detection is reactive.* It depends on something happening: an order placed, a
stock level crossing a threshold. Finding that out up to a day later is latency
chosen for nothing, and a nightly sweep re-reads the whole catalog to rediscover
what the domain already knew at the moment it changed.
*Proposing a purchase order is periodic.* Nobody reorders on every unit sold;
orders are grouped by supplier lead time and buying cycle. A clock is the right
trigger there, and that run is periodic on purpose rather than a batch standing
in for events.
Worth naming, because it changes what has to be built: **the autonomous mandate
is a cost of the trigger, not of the business need.** `offline_access`, a
refresh token stored encrypted and an exchange at trigger time are required only
because nobody is present at 3am. An agent woken by a domain event runs moments
after a human acted.
**Exposes** `check_availability` — called by Ventes before promising a delivery
date, and by Catalogue before discontinuing a reference. Returns a decision and a
date, never the stock table.
## Recouvrement
| | |
|---|---|
| MCP | customers, payments |
| Scopes | `customers:read`, `payments:read`, `payments:write` |
Weekly: unpaid invoices past 30 days, dunning, escalation.
**Exposes** `check_creditworthiness` — called by Ventes before accepting a large
order.
**Dunning is prepared automatically and sent only after human validation.**
Sending a letter to a customer is an outward-facing action; a rule bug would
otherwise write to the entire customer base in one run.
## The A2A server gap
The available n8n community nodes ([n8n-nodes-a2a](https://github.com/agentic-layer/n8n-nodes-a2a),
[n8n-nodes-agent2agent](https://github.com/pjawz/n8n-nodes-agent2agent)) make n8n
an A2A **client**: `Discover Agent`, `Send Task`.
But both agents here must be **servers** — Ventes calls them. That needs an
Agent Card on `/.well-known/agent.json` and a JSON-RPC endpoint with the task
lifecycle. n8n webhooks can do it by hand, at which point it stops being no-code.
Preferred approach: **Kong in front of n8n**, exactly as it fronts every other
resource. Kong serves the Agent Card statically and routes JSON-RPC to the
webhook, and token verification lands where it already lives. No new component.
Alternative: a small FastA2A adapter, written once for both — see
`classic-models-agent-catalogue`, which uses FastA2A already.
## The autonomous mandate
No user session exists when these run, so authority must be pre-established: the
user obtains `offline_access` when creating the mandate, the refresh token is
stored encrypted, and the exchange happens at trigger time. `sub` stays the user
who mandated, `azp` says which agent acted.
The MCP servers see no difference between an autonomous and a conversational
call — same `sub`, same `azp`, same audiences. That is deliberate: downstream
policy must not need to know the trigger mode.
## Backlog
**Events over Kafka, emitted by `classic-models-api`.** The target for the
reactive half. The estate emits no domain event today — the API has no signals
and no outbound webhook, and there is no bus in the cluster — which is what made
a nightly sweep look like the only option.
The emission belongs **in the API**, not in a database CDC and not in a gateway
plugin. Only the API knows which change is a business event rather than a row
update, and only it holds the identity that made it — `sub` and `azp` from the
caller's token, which the event has to carry if a downstream agent is to act
under the same authority as the person who triggered it.
Two facts about that codebase shape the work:
- Every write goes through one `BaseModelViewSet` in
`api/v1/classicmodels/views.py`. One hook there covers products, orders,
payments and the rest — Django signals scattered across models would be the
harder version of the same thing.
- **The models are `managed = False`.** The tables are the pre-existing
classicmodels schema and Django owns no migrations for them. A transactional
outbox means introducing the estate's first managed table beside unmanaged
ones — which works, and is worth deciding deliberately rather than
discovering.
An outbox is the part that matters: emitting from a viewset after `save()` drops
the event if the process dies between the two, and an agent that never hears
about a stock movement is worse than one that hears late.
Until this exists, detection stays unbuilt rather than being approximated by a
periodic scan: a batch that pretends to be reactive is worse than an absent
feature, because it looks finished.
The periodic ordering run does not wait on any of it.
## Two controls that become mandatory here
They were deferrable with a human in the loop. They are not, once an agent
loops on its own.
- **Read volume caps per `(azp, sub)`**, enforced on Kong. An agent reads a
thousand records in seconds where a human clicks ten; a scope authorises the
read but says nothing about volume.
- **Authorisation on tool-call parameters**, not just the tool name. `write`
authorises writing, not "this €40k transfer".
An autonomous agent hitting a rule that requires approval must **fail cleanly
and escalate**, never decide. And any approval screen must be rendered from the
structured tool-call parameters — never from a sentence the model produced, or a
prompt injection writes what the human validates.
## Before writing workflows
Read `classic-models-agent-platform/CLAUDE.md` for the identity model, the
endpoints and the known blockers.
Deliverables in English. Export workflows as JSON and commit them; do not rely
on the n8n instance as the source of truth.