feat: make the write path a mechanism instead of a prompt rule

The agent asked for confirmation because the system prompt told it to, and read
the answer itself. Both halves are now enforced.

Each MCP toolset is wrapped in ApprovalRequiredToolset whose predicate reads the
readOnlyHint the server publishes — the gateway decides what counts as a write,
and the agent keeps no list of tool names in step with it.
output_type=[str, DeferredToolRequests] lets a turn end holding a proposed call
instead of making it; the executor parks the A2A task in input-required and
renders the arguments the model actually sent, not what it said it would do.
The reply is matched against a closed set of words, so "oui mais change le prix"
denies the call and the correction comes back for approval on its own merits —
verified end to end, along with a draft the user approved that was still refused
because msrp sat below buyprice.

Also wired: a ProductDraft validated after approval and before the call leaves;
UsageLimits on every run; FilteredToolset denying sales' write tools inside the
agent as well as at the gateway; PrefixedToolset namespacing the two servers;
dynamic instructions naming the caller; Agent.instrument_all() for a span per
model call and per tool call; and pydantic_ai.retries on inference only, so a
429 from the gateway is waited out rather than failing mid-reasoning.

Inference gets its own httpx client: validate_response raises on any 4xx, and
applying that to the shared client would turn a token-exchange refusal into an
exception carrying no body — which is where Keycloak says what was wrong. Its
transport is built explicitly, because a transport passed to the client makes
verify= inert and the lab CA would otherwise go undeclared.

Tests grew from 7 to 31, covering what approval decides and what a draft must
satisfy. evals/catalogue_evals.py adds a behavioural suite, kept out of pytest
because every case spends real tokens; its first case checks that a count comes
from the tool rather than from what the model remembers of a public dataset.

CLAUDE.md corrected: instrument=True is not an Agent parameter in 2.35.3,
pydantic_ai.retries needs tenacity, TemplateStr needs pydantic-handlebars, and
approval does not remove the in-memory history — resuming still needs the
messages, so replicas: 1 stands until they live in the A2A task store.

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

src/catalogue_agent/identity.py

@@ -16,6 +16,8 @@ works, and the resulting token carries `aud: [mcp-catalog]`.
 
 from __future__ import annotations
 
+import base64
+import json
 import time
 from dataclasses import dataclass
 
@@ -86,3 +88,19 @@ class TokenExchanger:
         )
         self._cache[key] = token
         return token.access_token
+
+
+def caller_of(access_token: str) -> str:
+    """The username inside a token, for the prompt and nothing else.
+
+    Read without verifying the signature on purpose: this is a label shown to
+    the model, never a decision. Every decision that depends on this identity is
+    taken by Keycloak or by the gateway, both of which do verify.
+    """
+    try:
+        payload = access_token.split(".")[1]
+        payload += "=" * (-len(payload) % 4)
+        claims = json.loads(base64.urlsafe_b64decode(payload))
+    except Exception:  # noqa: BLE001 — a malformed token is the gateway's problem
+        return "an unidentified caller"
+    return claims.get("preferred_username") or claims.get("sub") or "an unidentified caller"