Stéphane Tailland
95b6e3a1c9
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>
155 lines
6.7 KiB
Markdown
155 lines
6.7 KiB
Markdown
# Flows — the autonomous agents
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**Runtime:** n8n · **Mode:** autonomous · Two agents in one repo.
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| Agent | Client id | Trigger |
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| Approvisionnement | `classic-models-agent-approvisionnement` | event-driven, plus a periodic ordering run |
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| Recouvrement | `classic-models-agent-recouvrement` | weekly, Monday |
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They share a repo because they are not code: n8n workflow exports, same tool,
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same owner. Splitting them would buy nothing.
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## Why low-code for these two
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Their work is rules, thresholds and due dates — a sequence of conditions and
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calls, which a visual editor expresses better than code. The LLM is optional on
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their critical path, which makes them cheaper and more predictable than the
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conversational agents.
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They remain full A2A agents: they publish an Agent Card, accept tasks, answer.
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## Approvisionnement
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| MCP | catalog, sales |
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| Scopes | `catalog:read`, `catalog:write`, `sales:read` |
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Detect coming stock-outs, propose reorders — **two jobs with two different
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triggers**, which an earlier revision of this file collapsed into one nightly
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run.
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*Detection is reactive.* It depends on something happening: an order placed, a
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stock level crossing a threshold. Finding that out up to a day later is latency
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chosen for nothing, and a nightly sweep re-reads the whole catalog to rediscover
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what the domain already knew at the moment it changed.
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*Proposing a purchase order is periodic.* Nobody reorders on every unit sold;
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orders are grouped by supplier lead time and buying cycle. A clock is the right
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trigger there, and that run is periodic on purpose rather than a batch standing
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in for events.
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Worth naming, because it changes what has to be built: **the autonomous mandate
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is a cost of the trigger, not of the business need.** `offline_access`, a
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refresh token stored encrypted and an exchange at trigger time are required only
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because nobody is present at 3am. An agent woken by a domain event runs moments
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after a human acted.
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**Exposes** `check_availability` — called by Ventes before promising a delivery
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date, and by Catalogue before discontinuing a reference. Returns a decision and a
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date, never the stock table.
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## Recouvrement
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| MCP | customers, payments |
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| Scopes | `customers:read`, `payments:read`, `payments:write` |
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Weekly: unpaid invoices past 30 days, dunning, escalation.
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**Exposes** `check_creditworthiness` — called by Ventes before accepting a large
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order.
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**Dunning is prepared automatically and sent only after human validation.**
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Sending a letter to a customer is an outward-facing action; a rule bug would
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otherwise write to the entire customer base in one run.
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## The A2A server gap
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The available n8n community nodes ([n8n-nodes-a2a](https://github.com/agentic-layer/n8n-nodes-a2a),
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[n8n-nodes-agent2agent](https://github.com/pjawz/n8n-nodes-agent2agent)) make n8n
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an A2A **client**: `Discover Agent`, `Send Task`.
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But both agents here must be **servers** — Ventes calls them. That needs an
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Agent Card on `/.well-known/agent.json` and a JSON-RPC endpoint with the task
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lifecycle. n8n webhooks can do it by hand, at which point it stops being no-code.
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Preferred approach: **Kong in front of n8n**, exactly as it fronts every other
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resource. Kong serves the Agent Card statically and routes JSON-RPC to the
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webhook, and token verification lands where it already lives. No new component.
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Alternative: a small FastA2A adapter, written once for both — see
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`classic-models-agent-catalogue`, which uses FastA2A already.
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## The autonomous mandate
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No user session exists when these run, so authority must be pre-established: the
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user obtains `offline_access` when creating the mandate, the refresh token is
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stored encrypted, and the exchange happens at trigger time. `sub` stays the user
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who mandated, `azp` says which agent acted.
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The MCP servers see no difference between an autonomous and a conversational
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call — same `sub`, same `azp`, same audiences. That is deliberate: downstream
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policy must not need to know the trigger mode.
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## Backlog
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**Events over Kafka, emitted by `classic-models-api`.** The target for the
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reactive half. The estate emits no domain event today — the API has no signals
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and no outbound webhook, and there is no bus in the cluster — which is what made
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a nightly sweep look like the only option.
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The emission belongs **in the API**, not in a database CDC and not in a gateway
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plugin. Only the API knows which change is a business event rather than a row
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update, and only it holds the identity that made it — `sub` and `azp` from the
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caller's token, which the event has to carry if a downstream agent is to act
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under the same authority as the person who triggered it.
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Two facts about that codebase shape the work:
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- Every write goes through one `BaseModelViewSet` in
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`api/v1/classicmodels/views.py`. One hook there covers products, orders,
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payments and the rest — Django signals scattered across models would be the
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harder version of the same thing.
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- **The models are `managed = False`.** The tables are the pre-existing
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classicmodels schema and Django owns no migrations for them. A transactional
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outbox means introducing the estate's first managed table beside unmanaged
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ones — which works, and is worth deciding deliberately rather than
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discovering.
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An outbox is the part that matters: emitting from a viewset after `save()` drops
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the event if the process dies between the two, and an agent that never hears
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about a stock movement is worse than one that hears late.
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Until this exists, detection stays unbuilt rather than being approximated by a
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periodic scan: a batch that pretends to be reactive is worse than an absent
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feature, because it looks finished.
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The periodic ordering run does not wait on any of it.
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## Two controls that become mandatory here
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They were deferrable with a human in the loop. They are not, once an agent
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loops on its own.
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- **Read volume caps per `(azp, sub)`**, enforced on Kong. An agent reads a
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thousand records in seconds where a human clicks ten; a scope authorises the
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read but says nothing about volume.
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- **Authorisation on tool-call parameters**, not just the tool name. `write`
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authorises writing, not "this €40k transfer".
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An autonomous agent hitting a rule that requires approval must **fail cleanly
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and escalate**, never decide. And any approval screen must be rendered from the
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structured tool-call parameters — never from a sentence the model produced, or a
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prompt injection writes what the human validates.
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## Before writing workflows
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Read `classic-models-agent-platform/CLAUDE.md` for the identity model, the
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endpoints and the known blockers.
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Deliverables in English. Export workflows as JSON and commit them; do not rely
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on the n8n instance as the source of truth.
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