Add implementation instructions

Design agreed in discussion, nothing built yet. Records the agent's role, its
scopes and MCP servers, its A2A surface, and the framework choice with its
reason — so the next session starts from the decisions rather than re-deriving
them.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

CLAUDE.md

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+# Catalogue — product management agent
+
+**Framework:** PydanticAI + FastA2A · **Mode:** conversational · **Client id:** `classic-models-agent-catalogue`
+
+Serves the product manager: create and update products and product lines, set
+prices, decide what to discontinue.
+
+## Scopes and MCP servers
+
+| | |
+|---|---|
+| MCP | catalog (`mcp-catalog-audience`), sales (`mcp-sales-audience`) |
+| Scopes | `catalog:read`, `catalog:write`, `sales:read` |
+
+`sales:read` is there to answer "does this product actually sell?" before a
+pricing or discontinuation decision. Read only — this agent never touches orders.
+
+## A2A
+
+**Calls** — `Approvisionnement.check_availability`, to know remaining stock and
+open orders before discontinuing a reference.
+
+**Exposes** — nothing yet. Add skills only when another agent needs them.
+
+## Why PydanticAI here
+
+Not arbitrary. This is the agent that **writes structured data under
+constraints** — prices, quantities, references, product line membership. Typed
+validation of the model's output earns its place here in a way it would not on a
+purely conversational agent.
+
+## FastA2A, not to_a2a()
+
+Use FastA2A directly. `Agent.to_a2a()` still works in PydanticAI 1.x but emits
+a deprecation warning and is removed in v2.
+
+FastA2A is framework-agnostic, so whatever is built here is also the natural
+basis for the A2A adapter in front of the n8n agents — see
+`classic-models-agent-flows`.
+
+## Before writing code
+
+Read `classic-models-agent-platform/CLAUDE.md` first. It holds the identity
+model, the MCP and LLM endpoints, the token-exchange recipe and the known
+blockers. This file only covers what is specific to this agent.
+
+## Non-negotiables
+
+- **Inference goes through the Kong AI gateway**, `https://llm-dev.sttlab.local/llm/v1/chat/completions`.
+  Calling a provider directly bypasses the rate limits and prompt guards.
+- **One MCP client per conversation turn**, built with freshly exchanged tokens.
+  Do not hold a long-lived MCP connection with a fixed token.
+- **A2A calls return answers, not data.** Expose narrow decision-shaped skills,
+  never a generic `query`.
+- Deliverables in English.